Information processing method and device for cosmetic supply chain, equipment and medium

Abstract

The invention relates to an information processing method and device for a cosmetic supply chain, equipment and a medium. The method comprises the following steps: acquiring original data of a cosmetic supply chain, verifying integrity through a preset consensus algorithm, and then executing standardization processing to generate standardized supply chain data; receiving an access request of a supply chain participant carrying a role identifier, calculating an authority matching score in combination with a preset authority rule, if the score reaches the standard, authorizing and integrating credit data in an authorization range, and fusing a real-time transaction log to obtain a dynamic trust evaluation value; transaction log abnormity is identified through an abnormity detection algorithm, a dynamic trust evaluation value is reduced according to needs, credit data is updated, and an updated integrated credit data set is formed; and verifying the Hash consistency of the traceability path node based on the dynamic trust evaluation value, quantifying the real-time credit score of the participant in combination with the updated integrated credit data set, and finally integrating to form a supply chain information display result. According to the method, full-process management and control of cosmetic supply chain data are realized, and the supervision requirement of safety management and control is met.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to information processing methods, devices, equipment and media for the cosmetics supply chain. Background Technology

[0002] The cosmetics supply chain involves multiple stages and various participants, including production, logistics, and sales. The data is fragmented and heterogeneous, leading to a lack of a unified and reliable mechanism for verifying supply chain data integrity, making it susceptible to data tampering and missing information. Furthermore, access control for supply chain data often relies on a single role mapping model, making it difficult to achieve precise access control for sensitive data (such as production process parameters and ingredient formulas). Trust assessments of participants largely depend on static historical data, failing to dynamically update based on real-time transaction behavior. This results in traceability chains only verifying node connectivity, making it difficult to guarantee the credibility of participants and the authenticity of traceability information. In addition, the supply chain information obtained by consumers is often fragmented, lacking transparent and reliable information verified from multiple dimensions, failing to meet the cosmetics industry's core needs for product traceability, safety control, and enhanced consumer trust. Summary of the Invention

[0003] Therefore, it is necessary to provide an information processing method, device, equipment, and medium for the cosmetics supply chain that can achieve precise access control of sensitive supply chain data, improve the timeliness and accuracy of credit assessment, and ensure the authenticity and reliability of supply chain traceability information, in response to the above-mentioned technical problems.

[0004] Firstly, this application provides information processing methods for the cosmetics supply chain, including:

[0005] Obtain raw supply chain data for cosmetics, verify the integrity of the raw supply chain data using a pre-defined consensus algorithm, and standardize the verified data to generate standardized supply chain data.

[0006] A permission matching score is calculated for standardized supply chain data and supply chain data access requests. If the permission matching score is higher than the preset permission threshold, credit data within the authorized scope is integrated to obtain a dynamic trust assessment value.

[0007] Real-time transaction log information is obtained, and after anomaly detection algorithms are used to identify abnormal behaviors in the real-time transaction log information, the dynamic trust assessment value is reduced to obtain an updated integrated credit dataset.

[0008] The consistency of hashes of traceability path nodes in the cosmetic supply chain is verified based on dynamic trust assessment values. The credit credibility of the corresponding node participants is determined by integrating the credit dataset, and the information display results of the cosmetic supply chain are obtained.

[0009] In one embodiment, a preset consensus algorithm is used to verify the integrity of the original supply chain data and the verified data is standardized to generate standardized supply chain data, including:

[0010] Obtain raw supply chain data from the cosmetics supply chain; raw supply chain data includes production process parameters, logistics tracking information, and sales terminal data.

[0011] Based on a pre-defined consensus algorithm of the blockchain network, the original supply chain data is verified for integrity, and the initial supply chain data that has passed the integrity verification is obtained.

[0012] The initial supply chain data is standardized according to preset standards, including data format, field type and coding rules, to generate standardized supply chain data that conforms to unified specifications.

[0013] In one embodiment, a permission matching score is calculated for standardized supply chain data and supply chain data access requests. If the permission matching score is higher than a preset permission threshold, credit data within the authorized scope is integrated to obtain a dynamic trust assessment value, including:

[0014] Receive supply chain data access requests from supply chain participants, each carrying a role identifier; the supply chain data access requests are directed to standardized supply chain data.

[0015] Based on the preset permission rules corresponding to the role identifier and standardized supply chain data, permission matching calculation is performed to obtain the permission matching score.

[0016] If the permission matching score is higher than the preset permission threshold, the access authorization is deemed valid.

[0017] Once access authorization is established, the supply chain credit data within the authorized scope corresponding to the role identifier is integrated to obtain an integrated credit dataset.

[0018] By acquiring real-time transaction log information from supply chain participants and integrating the credit dataset with the real-time transaction log information, a dynamic trust assessment value is obtained.

[0019] In one embodiment, the permission matching score is calculated using the following formula:

[0020]

[0021] in, This indicates the permission matching score. , , These represent the weighting coefficients for role compliance and data access adaptability, respectively. This indicates the role's permission level coefficient, preset according to the role types in the cosmetics supply chain. , The historical compliance coefficient for a role is calculated by weighting the accuracy of data submissions and the percentage of compliant operation records over the past 12 months. , This indicates the total number of fields to be accessed in the standardized supply chain data. Indicates the first Sensitivity weights for each data field , Indicates the role and the first Access compatibility of each field .

[0022] In one embodiment, after identifying abnormal behavior in real-time transaction log information using an anomaly detection algorithm, the dynamic trust assessment value is reduced to obtain an updated integrated credit dataset, including:

[0023] Adjust the scope of supply chain data access permissions for supply chain participants based on dynamic trust assessment values.

[0024] A preset anomaly detection algorithm is used to identify abnormal behavior in real-time transaction log information, and anomaly behavior identification results are obtained.

[0025] If the abnormal behavior identification result indicates the presence of abnormal behavior, the dynamic trust assessment value will be reduced according to preset rules.

[0026] The updated integrated credit dataset is obtained by updating the supply chain credit data within the authorization scope corresponding to the role identifier based on the reduced dynamic trust assessment value.

[0027] In one embodiment, the dynamic trust assessment value is calculated using the following formula:

[0028]

[0029] in, This represents the dynamic trust assessment value. , These represent the weights of historical credit data and real-time transaction data, respectively. , , Indicates the weight of each component in the credit data. Indicates the fulfillment rate. Indicates compliance rate. This indicates consumer feedback ratings. , This represents a transaction data adjustment factor, calibrated based on the transaction volume of the cosmetics supply chain. Indicates the aging decay coefficient. This indicates the frequency of transactions over the past 30 days. Indicates the average transaction fulfillment time. Indicates the total number of abnormal behavior types. Indicates the first The severity weight of abnormal behaviors, Indicates the first in the real-time transaction log The number of times such abnormal behavior occurs.

[0030] In one embodiment, the consistency of hashes of traceability path nodes in the cosmetics supply chain is verified based on dynamic trust assessment values. The credit credibility of corresponding node participants is determined by integrating a credit dataset. The resulting information display of the cosmetics supply chain includes:

[0031] Verify the consistency of hash links between nodes in the cosmetic supply chain traceability path based on dynamic trust assessment values.

[0032] If the hash links between all path nodes are consistent, then the traceability path continuity verification is considered successful.

[0033] The credit credibility of each supply chain participant at each path node is calculated based on the updated integrated credit dataset. The credit credibility includes the historical behavior records of each participant at each node. The historical behavior records include performance records, compliance records, and abnormal behavior records.

[0034] A preset credit assessment algorithm is used to quantify historical behavior records and obtain real-time credit scores for each participant.

[0035] If the real-time credit score is higher than the preset credit threshold, the credit credibility of the corresponding node participant is determined to meet the standard.

[0036] Once all path nodes pass consistency verification and the corresponding participants at each node are deemed trustworthy, a cosmetic supply chain traceability chain is constructed.

[0037] Trusted information, verified by node consistency and participant creditworthiness, is extracted from the supply chain traceability chain to generate an information sharing view.

[0038] By using a pre-set encrypted distribution mechanism, the information sharing view is transmitted to the consumer end, resulting in a transparent information display of the cosmetics supply chain.

[0039] Secondly, this application also provides an information processing device for the cosmetics supply chain, the device comprising:

[0040] The data acquisition module is used to acquire raw supply chain data of the cosmetics supply chain, verify the integrity of the raw supply chain data using a preset consensus algorithm, and standardize the verified data to generate standardized supply chain data.

[0041] The trust assessment module is used to calculate the permission matching score between standardized supply chain data and supply chain data access requests. If the permission matching score is higher than the preset permission threshold, the credit data within the authorized scope is integrated to obtain a dynamic trust assessment value.

[0042] The anomaly detection module is used to acquire real-time transaction log information. After identifying abnormal behavior in the real-time transaction log information through anomaly detection algorithms, the dynamic trust assessment value is reduced to obtain an updated integrated credit dataset.

[0043] The information display module is used to verify the consistency of hashes of traceability path nodes in the cosmetic supply chain based on dynamic trust assessment values, determine the credit credibility of corresponding node participants by combining integrated credit datasets, and obtain the information display results of the cosmetic supply chain.

[0044] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method.

[0046] The aforementioned information processing method, apparatus, computer equipment, and storage medium for the cosmetics supply chain acquire raw supply chain data. This raw data undergoes integrity verification using a pre-defined consensus algorithm. After filtering out valid data that passes verification, it is standardized according to pre-defined data formats, field types, and encoding rules to generate unified and standardized supply chain data. Next, it receives supply chain data access requests from supply chain participants. Based on the role identifiers included in the access requests and the pre-defined permission rules corresponding to the standardized supply chain data, it calculates a permission matching score. If the score is higher than a pre-defined permission threshold, access authorization is deemed valid. Furthermore, it integrates supply chain credit data within the authorized scope corresponding to the role identifier and merges it with real-time acquired transaction logs. The information is dynamically assessed using a trust evaluation value. Then, a pre-defined anomaly detection algorithm is used to identify abnormal behavior in the real-time transaction logs. If abnormal behavior is detected, the dynamic trust evaluation value is lowered according to pre-defined rules, and the supply chain credit data within the authorized scope is updated based on the lowered evaluation value, forming an updated integrated credit dataset. Finally, the consistency of hash links between nodes in the cosmetic supply chain traceability path is verified based on the dynamic trust evaluation value. Simultaneously, combined with the updated integrated credit dataset, historical behavior records of the participants corresponding to each node are extracted. Real-time credit scores are quantified using a pre-defined credit evaluation algorithm to determine the creditworthiness of the participants. Under the condition that the hash links of all nodes are consistent and the creditworthiness of the participants meets the standards, a multi-dimensional verified cosmetic supply chain information display result is formed. This method achieves end-to-end control of cosmetic supply chain data from collection to display. The integrity of the original data is ensured by a pre-defined consensus algorithm, and standardized processing solves the problem of data heterogeneity, laying the foundation for subsequent data applications. Dual-dimensional permission matching based on role identification and data sensitivity enables precise access control to sensitive supply chain data, reducing the risk of data leakage. Dynamic trust assessment, combined with real-time transaction logs and anomaly detection, enables dynamic updates to the credit status of participants, improving the timeliness and accuracy of credit assessment. Dual verification through hash consistency verification of traceability path nodes and judgment of participant credit credibility ensures the authenticity and reliability of supply chain traceability information. The final transparent information display not only meets the regulatory needs of the cosmetics industry for product traceability and safety control but also provides consumers with credible supply chain information, helping to enhance consumer trust. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 A flowchart of an information processing method for a cosmetics supply chain provided in an embodiment of the present invention;

[0049] Figure 2 This is a structural block diagram of an information processing device for a cosmetics supply chain provided in an embodiment of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] In one embodiment, such as Figure 1 As shown, this application provides an information processing method for the cosmetics supply chain, which may include the following steps:

[0052] Step S101: Obtain the original supply chain data of the cosmetic supply chain, verify the integrity of the original supply chain data using a preset consensus algorithm, and standardize the verified data to generate standardized supply chain data.

[0053] Specifically, the raw supply chain data originates from various participants in the cosmetics supply chain, including production process parameters and raw material procurement records provided by manufacturers, logistics tracking information and temperature and humidity monitoring data provided by logistics providers, and sales terminal data and inventory change records provided by retailers. When verifying data integrity using a pre-defined consensus algorithm, the algorithm checks the field completeness, logical consistency, and data signature validity of the data submitted by each participant, eliminating data with missing, tampered, or logically contradictory entries, and selecting initial data that passes integrity verification. Subsequent standardization processing, based on pre-defined industry or system data standards, unifies and standardizes the format (e.g., date format, numerical units), field types (e.g., character, numeric), and encoding rules (e.g., data encryption encoding, classification encoding) of the initial data, ultimately generating standardized supply chain data with uniform format, standardized fields, and direct usability for subsequent processing.

[0054] Step S102: Calculate the permission matching score for standardized supply chain data and supply chain data access requests. If the permission matching score is higher than the preset permission threshold, integrate the credit data within the authorized scope to obtain a dynamic trust assessment value.

[0055] Supply chain data access requests are initiated by supply chain participants (such as manufacturers, distributors, and regulatory agencies). The request includes the initiator's role identifier and the scope of standardized supply chain data to be accessed. The permission matching score is calculated based on preset permission rules corresponding to the role identifier, combined with the sensitivity weights of various fields in the standardized supply chain data. A preset algorithm quantifies the compatibility between the role and the data to be accessed, resulting in a permission matching score. A preset permission threshold is a pre-defined authorization threshold set by the system. If the calculated permission matching score is higher than this threshold, access authorization is deemed valid. After authorization, the corresponding credit data access authorization scope is determined based on the role identifier. Credit data within this scope, including participants' historical performance records, compliance test results, and past credit ratings, is integrated. Through data fusion and quantitative calculation, a dynamic trust assessment value reflecting the participant's current credit status is obtained.

[0056] Step S103: Obtain real-time transaction log information, and reduce the dynamic trust assessment value after identifying abnormal behavior in the real-time transaction log information through an anomaly detection algorithm to obtain an updated integrated credit dataset.

[0057] Real-time transaction logs are derived from the real-time business operations of supply chain participants, encompassing dynamic information such as transaction initiation time, transaction amount, fulfillment progress, and data submission records. When analyzing these logs using a pre-defined anomaly detection algorithm, abnormal characteristics (such as abnormal transaction frequency, deviation from fulfillment timeliness thresholds, and abnormal data submission frequency) are identified, and anomaly behavior identification results are output. If the identification result indicates abnormal behavior, the resulting dynamic trust assessment value is reduced accordingly based on pre-defined anomaly impact weighting rules. Subsequently, the reduced dynamic trust assessment value is updated back into the supply chain credit data within the authorized scope corresponding to the role identification, overwriting historical assessment information in the original credit data, forming an updated integrated credit dataset reflecting the latest credit status of the participants.

[0058] Step S104: Verify the consistency of hashes of traceability path nodes in the cosmetic supply chain based on dynamic trust assessment values, determine the credit credibility of corresponding node participants by combining integrated credit datasets, and obtain the information display results of the cosmetic supply chain.

[0059] When verifying the hash consistency of traceability path nodes based on dynamic trust assessment values, the dynamic trust assessment values ​​are associated with the attribute information of each traceability node. By verifying whether the hash links between adjacent traceability nodes are continuous and consistent, it is determined whether there are any breaks or tampering in the traceability path. If the hash links between all nodes are consistent, the traceability path continuity verification is considered successful. Simultaneously, historical behavior records of the participants corresponding to each path node are extracted from the updated integrated credit dataset, including performance records, compliance records, and abnormal behavior records. These records are quantified and scored using a preset credit assessment algorithm to obtain the real-time credit score of each participant. If the real-time credit score is higher than a preset credit threshold, the participant's credit credibility is considered acceptable. When all path nodes pass the hash consistency verification and the corresponding participants' credit credibility is acceptable, the verified supply chain information of each node (such as raw material traceability information, production process data, logistics flow records, etc.) is integrated to generate a cosmetic supply chain information display result that meets the display requirements.

[0060] The aforementioned information processing method for the cosmetics supply chain acquires raw cosmetics supply chain data, verifies its integrity using a preset consensus algorithm, and then performs standardized processing according to preset standards to generate unified standardized supply chain data. It receives access requests from supply chain participants with role identifiers, calculates permission matching scores based on preset permission rules, and authorizes access if the score meets the standard, integrating credit data within the authorized scope and fusing real-time transaction logs to obtain a dynamic trust assessment value. Anomaly detection algorithms identify anomalies in transaction logs, lowering the dynamic trust assessment value and updating the credit data as needed to form an updated integrated credit dataset. Based on the dynamic trust assessment value, it verifies the hash consistency of traceability path nodes, quantifies the real-time credit scores of participants, and determines their credibility using the updated integrated credit dataset, ultimately integrating and forming a multi-dimensional verified supply chain information display result. This method enables end-to-end data management in the cosmetics supply chain. It ensures data integrity and resolves data heterogeneity issues through consensus algorithms and standardized processing; dual-dimensional permission matching enables precise control of sensitive data and reduces the risk of leakage; dynamic trust assessment combined with anomaly detection improves the timeliness and accuracy of credit assessment; and dual verification of node consistency and participant credibility ensures the authenticity and reliability of traceability information. The final transparent output can meet the regulatory needs of industry traceability and security management, while providing consumers with credible information and helping to enhance consumer trust.

[0061] In one embodiment, using a preset consensus algorithm to verify the integrity of the original supply chain data and standardizing the verified data to generate standardized supply chain data may include the following steps:

[0062] Step S201: Obtain the original supply chain data in the cosmetic supply chain; the original supply chain data includes production process parameters, logistics trajectory information and sales terminal data.

[0063] Step S202: Based on the preset consensus algorithm of the blockchain network, the integrity of the original supply chain data is verified, and the initial supply chain data that has passed the integrity verification is obtained.

[0064] Step S203: Perform standardization processing on the initial supply chain data according to preset standards, including data format, field type and encoding rules, to generate standardized supply chain data that conforms to unified specifications.

[0065] Specifically, the process begins with the targeted collection of raw supply chain data across the entire cosmetics supply chain. This data covers all core participants and key stages of the supply chain: production process parameters are provided by cosmetics manufacturers, including core production information such as raw material ratios, production temperature, fermentation time, and quality testing indicators; logistics tracking information is uploaded in real-time by logistics service providers, encompassing warehouse entry and exit records, vehicle location data, temperature and humidity monitoring data, and loading / unloading time points; and sales terminal data is compiled and submitted by retailers, including market information such as terminal sales volume, inventory changes, regional sales distribution, and consumer purchase times. After data collection, a pre-defined consensus algorithm deployed on a blockchain network is used to verify the integrity of the raw supply chain data. Leveraging the immutability of the blockchain's distributed ledger, the algorithm verifies the completeness of each data entry's fields (confirming no missing required fields), the validity of the data signature (verifying the legitimacy of the data submitter), and logical consistency (checking for contradictions in the timelines and relationships between data from different stages). Data with missing fields, abnormal signatures, or logical conflicts is marked and removed, ultimately resulting in initial supply chain data that has passed both integrity and authenticity verification. Subsequently, based on the pre-set unified data standards, the initial supply chain data undergoes comprehensive standardization processing: In terms of data format, the date (e.g., using the format "YYYY-MM-DD HH:MM:SS") and numerical units (e.g., mass units are standardized to "g" and "kg", and time units are standardized to "hour") are unified; in terms of field types, the data type of each field is clearly defined (e.g., production temperature is set to numerical type, raw material name is set to character type, and test result is set to Boolean type) to avoid type confusion; in terms of coding rules, unified classification coding or encryption coding rules are adopted for information such as raw material category, product model, and participant identity to achieve standardized data identification. Finally, standardized supply chain data with unified format, clear fields, and consistent coding is generated, which can directly support the data processing needs of subsequent permission matching, trust assessment, and other links.

[0066] This embodiment uses a pre-defined consensus algorithm on the blockchain network to perform data integrity verification. On one hand, leveraging the distributed storage and immutability of blockchain, it technically eliminates the possibility of malicious tampering of original data, ensuring the traceability of the data submitter's identity and effectively improving the authenticity and credibility of the original supply chain data. On the other hand, through multi-dimensional integrity verification, it accurately eliminates invalid and abnormal data, reducing redundant calculations in subsequent data processing stages and improving data processing efficiency. The standardized processing stage, through the unified standardization of data formats, field types, and encoding rules, completely solves the heterogeneity problem caused by inconsistent data collection standards among various participants in the cosmetics supply chain. It eliminates format barriers and misunderstandings between data from different sources, achieving cross-participant and cross-stage data interoperability and mutual recognition. The generated standardized supply chain data provides a unified and standardized data foundation for the accurate matching of role identifiers and data fields in subsequent permission matching calculations, the effective integration of credit data in dynamic trust assessments, and the correlation comparison of data at each node in traceability path verification. This ensures the accuracy and logical smoothness of calculations in subsequent processing stages, improving the stability and reliability of the entire cosmetics supply chain information processing flow from the source, and laying core data support for end-to-end data management.

[0067] In one embodiment, a permission matching score is calculated for standardized supply chain data and supply chain data access requests. If the permission matching score is higher than a preset permission threshold, credit data within the authorized scope is integrated to obtain a dynamic trust assessment value, which may include the following steps:

[0068] Step S301: Receive supply chain data access requests from supply chain participants, which carry role identifiers; the supply chain data access requests are directed to standardized supply chain data.

[0069] Step S302: Perform permission matching calculation based on the preset permission rules corresponding to the role identifier and standardized supply chain data to obtain the permission matching score.

[0070] In step S303, if the permission matching score is higher than the preset permission threshold, the access authorization is determined to be valid.

[0071] Step S304: Once access authorization is established, integrate the supply chain credit data within the authorized scope corresponding to the role identifier to obtain an integrated credit dataset.

[0072] Step S305: Obtain real-time transaction log information from supply chain participants, and perform fusion calculation on the integrated credit dataset and real-time transaction log information to obtain a dynamic trust assessment value.

[0073] Specifically, the system first receives supply chain data access requests initiated by various participants in the cosmetics supply chain (including manufacturers, logistics service providers, sales terminals, regulatory agencies, and third-party testing institutions). These requests must carry a unique and unalterable role identifier for the initiator (the role identifier is associated with the participant's subject type, business authority scope, and historical operation records, etc.). At the same time, it must clearly indicate the specific scope of the standardized supply chain data to be accessed, including the data's stage (production, logistics, sales, etc.), data field types (such as production process parameters, logistics temperature and humidity data, sales inventory data, etc.), and data time span, ensuring the access request's specificity and clarity. The permission matching calculation process is then initiated, based on a pre-defined permission rule system. This system has pre-established a multi-dimensional mapping relationship between role identifiers and standardized supply chain data, covering core content such as role permission level classification, sensitivity weight assignment for different data fields (e.g., sensitive data such as production process parameters and core ingredient formulas are assigned high weights, while ordinary logistics trajectories and sales quantities are assigned medium to low weights), and role historical compliance coefficient association rules. During the calculation process, the permission level coefficient corresponding to the role identifier is first extracted, and the historical compliance coefficient is obtained by weighting the accuracy rate of data submission and the proportion of compliant operation records in the past 12 months. Then, combined with the sensitivity weight of each field in the data to be accessed and the access compatibility between the role and the field (compatible is 1, incompatible is 0), the degree of compatibility between the role and the data to be accessed is quantified through a pre-defined algorithm, and finally, the permission matching score is output. Next, authorization is determined. Preset permission thresholds are tiered by the system based on data security levels, industry regulatory requirements, and supply chain data confidentiality needs (the threshold for sensitive data is higher than that for ordinary data). The calculated permission matching score is compared with the corresponding tier's preset permission threshold. If the score is higher than the threshold, access authorization is established, an authorization credential is generated, and an authorization log is recorded. If the score is lower than or equal to the threshold, the access request is directly rejected, and the initiator is informed of the reason for non-authorization (e.g., insufficient permission level, inadequate data sensitivity matching, etc.). Once access authorization is established, the corresponding credit data access authorization scope is determined based on the role identifier and authorization credential. This scope strictly matches the boundaries defined by the permission rules, integrating supply chain credit data within the scope. Specifically, this includes core information such as the participant's order fulfillment rate over the past 6 months, the percentage of compliance inspections in production / logistics / sales processes, past credit ratings, historical abnormal behavior records, and data submission accuracy. After data deduplication and format standardization, an integrated credit dataset covering the participant's historical credit status is formed.Finally, real-time transaction log information of the supply chain participant within the current business cycle is obtained through a real-time data interface. The log content covers dynamic data such as transaction initiation time, transaction amount, order fulfillment progress, data submission frequency, key operation node records, and abnormal operation warning information. Using a preset data fusion algorithm, the integrated credit dataset (static historical credit data) and real-time transaction log information (dynamic business data) are first aligned in terms of data dimensions, unified in units, and outlier removal is performed. Then, a weighted calculation is performed based on the influence weights of the two types of data to eliminate the evaluation bias caused by differences in data dimensions. Finally, a dynamic trust assessment value that can reflect the current credit status of the participant in real time and accurately is obtained.

[0074] This embodiment achieves refined and differentiated control over cosmetic supply chain data access through multi-dimensional permission matching calculations based on role identifiers and preset permission rules. It clarifies the access boundaries for different types of participants and further improves the accuracy of permission matching by correlating data sensitivity weights with role historical compliance coefficients. This effectively avoids the risk of irrelevant or low-compliance roles accessing sensitive data, reduces the possibility of leakage, misuse, and tampering of core supply chain data, and ensures the security and standardization of data access. The integration of credit data after access authorization, filtering targeted credit data based on the authorized scope, ensures the integrity and relevance of the integrated credit dataset while avoiding the redundant introduction of irrelevant credit data. This provides high-quality basic data support for subsequent dynamic trust assessments and reduces the interference of data noise on the assessment results. By integrating static historical credit data with dynamic real-time transaction log information to calculate dynamic trust assessment values, it overcomes the limitations of traditional trust assessments that rely solely on historical data. This allows assessment results to be dynamically updated in real time following the business operations of participants, accurately capturing changes in the credit status of participants and significantly improving the timeliness and accuracy of credit assessments.

[0075] In one embodiment, the permission matching score can be calculated using the following formula:

[0076]

[0077] in, This indicates the permission matching score. , , These represent the weighting coefficients for role compliance and data access adaptability, respectively. This indicates the role's permission level coefficient, preset according to the role types in the cosmetics supply chain. , The historical compliance coefficient for a role is calculated by weighting the accuracy of data submissions and the percentage of compliant operation records over the past 12 months. , This indicates the total number of fields to be accessed in the standardized supply chain data. Indicates the first Sensitivity weights for each data field , Indicates the role and the first Access compatibility of each field .

[0078] This embodiment's permission matching score calculation method, through a dual-dimensional fusion design, breaks through the limitations of traditional single role-permission mapping, achieving precise quantification of permission matching. It introduces a role-based historical compliance coefficient, associating past operational behaviors of participants with permission matching, avoiding compliance risks caused by authorization solely based on role type, and improving the security of permission allocation. By differentiating the security levels of different fields through data sensitivity weights, combined with access adaptability, it achieves precise control over sensitive data, reducing the risk of core data leakage. The value ranges of each parameter are clearly defined and adapted to the cosmetics supply chain scenario, and the weight coefficients can be dynamically calibrated, giving the calculation model strong scenario adaptability.

[0079] In one embodiment, after identifying abnormal behavior in real-time transaction log information using an anomaly detection algorithm, the dynamic trust assessment value is reduced to obtain an updated integrated credit dataset, which may include the following steps:

[0080] Step S401: Adjust the supply chain data access permissions of supply chain participants based on the dynamic trust assessment value.

[0081] Step S402: Use a preset anomaly detection algorithm to identify abnormal behavior in real-time transaction log information and obtain anomaly behavior identification results.

[0082] Step S403: If the abnormal behavior identification result indicates that abnormal behavior exists, the dynamic trust assessment value is reduced according to the preset rules.

[0083] Step S404: Update the supply chain credit data within the authorization scope corresponding to the role identifier based on the reduced dynamic trust assessment value to obtain the updated integrated credit dataset.

[0084] First, based on a dynamic trust assessment value, the access permissions for supply chain data to participants are adjusted. Specifically, according to a preset mapping rule based on the dynamic trust assessment value, the range of standardized supply chain data fields and data sensitivity levels accessible to participants are adaptively scaled. The higher the trust assessment value, the wider the range of accessible data and the higher the sensitivity level; conversely, the access boundaries are narrowed, achieving dynamic matching between permissions and credit status. Next, an anomaly monitoring process is initiated. A preset anomaly detection algorithm performs a full analysis of the real-time transaction logs of supply chain participants. The real-time transaction logs cover core indicators such as transaction initiation frequency, performance deviation, data submission completeness, and operational behavior standardization. The algorithm compares log indicators with preset normal thresholds and identifies abnormal behavior characteristics (such as high-frequency abnormal submissions, severe performance timeouts, and data logic contradictions), outputting anomaly identification results including the type, frequency, and severity of the abnormal behavior. If the abnormal behavior identification result determines that abnormal behavior exists, the current dynamic trust assessment value is reduced accordingly based on the pre-set abnormal impact weighting rules, according to the severity (e.g., minor, moderate, severe) or frequency of the abnormal behavior. Minor abnormalities correspond to a small reduction, while severe abnormalities correspond to a large reduction, ensuring that the punishment intensity matches the degree of abnormality. Finally, the reduced dynamic trust assessment value is used as the core update basis to update the supply chain credit data within the authorized scope corresponding to the participant's role identification. Specifically, this covers core fields such as historical credit ratings, compliance records, and abnormal behavior files. The latest trust assessment results and abnormal behavior information are integrated, and after data deduplication and format standardization, an integrated credit dataset that can reflect the participant's current credit status in real time is formed.

[0085] This embodiment achieves precise alignment between access control and participant credit status by dynamically adjusting data access permissions based on trust assessment values. This avoids the risk of retaining high privileges despite deteriorating credit under a fixed-permission model, enhancing the flexibility and security of supply chain data access control. A pre-set anomaly detection algorithm continuously monitors real-time transaction logs, promptly capturing abnormal operational behaviors of participants, enabling early detection and response to anomaly risks, and reducing the impact of abnormal behavior on the authenticity of supply chain data and business continuity. The dynamic trust assessment value adjustment mechanism for abnormal behavior establishes a linked penalty logic of "behavior-credit-permission," strengthening constraints on compliant operations by participants and guiding them to standardize business behavior. Credit data updates based on reduced trust assessment values ​​ensure the real-time nature and accuracy of the integrated credit dataset, eliminating discrepancies between historical static data and the current actual credit status. This provides high-quality credit data support for subsequent permission matching, traceability verification, and other processes, improving the dynamic control capabilities and data reliability of cosmetic supply chain information processing.

[0086] In one embodiment, the dynamic trust assessment value can be calculated using the following formula:

[0087]

[0088] in, This represents the dynamic trust assessment value. , These represent the weights of historical credit data and real-time transaction data, respectively. , , Indicates the weight of each component in the credit data. Indicates the fulfillment rate. Indicates compliance rate. This indicates consumer feedback ratings. , This represents a transaction data adjustment factor, calibrated based on the transaction volume of the cosmetics supply chain. Indicates the aging decay coefficient. This indicates the frequency of transactions over the past 30 days. Indicates the average transaction fulfillment time. Indicates the total number of abnormal behavior types. Indicates the first The severity weight of abnormal behaviors, Indicates the first in the real-time transaction log The number of times such abnormal behavior occurs.

[0089] This embodiment uses a dual-dimensional fusion model of historical credit and real-time transaction data to calculate the dynamic trust assessment value. It precisely balances the influence of these two types of data through α and β weights, integrates multi-dimensional historical credit information such as fulfillment rate, compliance rate, and consumer feedback scores using a, b, and c sub-weights, and quantifies real-time transaction characteristics such as transaction frequency and average fulfillment timeliness over the past 30 days by combining d and f adjustment coefficients and k timeliness decay coefficient. Simultaneously, it... , By differentiating the types and frequencies of abnormal behavior to achieve differentiated consideration of the impact of abnormalities, this approach not only breaks through the limitations of traditional single static credit assessment, but also adapts to the characteristics of multiple links in the cosmetics supply chain through the range of values ​​for each parameter and scenario-based calibration rules. This effectively improves the accuracy and timeliness of trust assessment, provides objective quantitative basis for dynamic adjustment of permissions, prevention and control of abnormal risks, and determination of the credibility of traceability nodes, and strengthens the scientific nature and operability of credit management in the cosmetics supply chain.

[0090] In one embodiment, verifying the hash consistency of traceability path nodes in the cosmetics supply chain based on dynamic trust assessment values, determining the credit credibility of corresponding node participants by integrating an integrated credit dataset, and obtaining the information display results of the cosmetics supply chain can include the following steps:

[0091] Step S501: Verify the consistency of hash links between nodes in the cosmetic supply chain traceability path based on dynamic trust assessment values.

[0092] Step S502: If the hash links between all path nodes are consistent, then the traceability path continuity verification is deemed successful.

[0093] Step S503: Calculate the credit credibility of each supply chain participant corresponding to each path node based on the updated integrated credit dataset; the credit credibility includes the historical behavior records of each node participant; the historical behavior records include performance records, compliance records, and abnormal behavior records.

[0094] Step S504: Use a preset credit assessment algorithm to quantify historical behavior records and obtain the real-time credit score of each participant.

[0095] Step S505: If the real-time credit score is higher than the preset credit threshold, the credit credibility of the corresponding node participant is determined to meet the standard.

[0096] Step S506: When all path nodes have passed consistency verification and the corresponding participants of each node have been determined to meet the credibility standards, a cosmetic supply chain traceability chain is constructed.

[0097] Step S507: Extract trusted information from the supply chain traceability chain that has been verified by node consistency and the credit credibility of participating parties, and generate an information sharing view.

[0098] Step S508: The information sharing view is transmitted to the consumer terminal through a preset encrypted distribution mechanism to obtain the results of the transparent information display of the cosmetic supply chain.

[0099] Specifically, based on dynamic trust assessment values ​​and updated integrated credit datasets, the construction and information display of the cosmetics supply chain traceability chain are carried out in two steps. The first step is to verify the continuity of the traceability path. Based on the dynamic trust assessment values, the attribute information of each traceability node is associated, and the consistency of the hash links between adjacent nodes in the cosmetics supply chain traceability path is verified. By comparing the matching degree of node data signatures, hash values, and associated timestamps, it is determined whether there are any breaks, tampering, or forgery in the data transmission between nodes. If the hash links between all path nodes are completely consistent, the traceability path continuity verification is considered to have passed. The second step involves determining the creditworthiness of participating nodes. Based on the updated integrated credit dataset, historical behavioral records of supply chain participants corresponding to each path node are extracted. These records specifically cover performance records (such as order completion timeliness and delivery quality compliance), compliance records (such as production / logistics compliance testing results and industry standard compliance), and abnormal behavior records (such as past violations and abnormal data submissions). A preset credit assessment algorithm is used to quantitatively weight these historical behavioral records, transforming qualitative information into quantitative indicators to obtain a real-time credit score for each participant. The real-time credit score is then compared with a preset credit threshold. If the score is higher than the threshold, the creditworthiness of the corresponding node participant is deemed to meet the standard. When all path nodes pass continuity verification and the corresponding participants at each node meet the credit credibility standards, the supply chain data of each node, which has undergone dual verification, is integrated to construct a complete and reliable cosmetic supply chain traceability chain. Credible information verified by node consistency and participant credit credibility is extracted from this traceability chain, including raw material traceability information, production parameters, logistics flow records, and sales terminal information. Through a preset encrypted distribution mechanism (including data encryption, digital signature, and permission verification), the generated information sharing view is securely transmitted to the consumer end, ultimately resulting in a transparent information display that comprehensively and truthfully reflects the information across the entire cosmetic supply chain.

[0100] This embodiment uses the consistency verification of node hash links associated with dynamic trust assessment values ​​to technically ensure the integrity and authenticity of the traceability path, effectively avoiding problems such as tampering of traceability nodes and path breaks. Combined with an updated integrated credit dataset to determine the creditworthiness of participants, it achieves precise control over the credit status of the entities behind the traceability nodes, compensating for the shortcomings of only verifying path connectivity while ignoring the credibility of participants. The superimposed application of the dual verification mechanism ensures that the constructed cosmetic supply chain traceability chain possesses both data-level continuity and entity-level credibility, providing a reliable foundation for subsequent extraction of credible information. Based on the traceability chain after dual verification, credible information is extracted to generate a shared view, ensuring the accuracy and authority of the displayed content; the preset encrypted distribution mechanism ensures the security of information transmission, preventing data theft or tampering. The final transparent information display result not only meets the industry regulatory requirements for full-chain traceability and security control of the cosmetic supply chain, but also provides consumers with verifiable supply chain information, effectively improving the transparency and credibility of the cosmetic supply chain and contributing to the construction of a healthy supply chain ecosystem.

[0101] In one embodiment, such as Figure 2 As shown, this application also provides an information processing device for a cosmetics supply chain, the device may include:

[0102] The data acquisition module 601 is used to acquire the original supply chain data of the cosmetic supply chain, verify the integrity of the original supply chain data using a preset consensus algorithm, and standardize the verified data to generate standardized supply chain data.

[0103] The trust assessment module 602 is used to calculate the permission matching score between standardized supply chain data and supply chain data access requests. If the permission matching score is higher than the preset permission threshold, the credit data within the authorized scope is integrated to obtain a dynamic trust assessment value.

[0104] The anomaly detection module 603 is used to acquire real-time transaction log information. After identifying abnormal behavior in the real-time transaction log information through an anomaly detection algorithm, the dynamic trust assessment value is reduced to obtain an updated integrated credit dataset.

[0105] The information display module 604 is used to verify the consistency of hashes of traceability path nodes in the cosmetic supply chain based on dynamic trust assessment values, determine the credit credibility of corresponding node participants by combining integrated credit datasets, and obtain the information display results of the cosmetic supply chain.

[0106] The aforementioned information processing device for the cosmetics supply chain includes a data acquisition module, a trust assessment module, an anomaly detection module, and an information display module. These modules work together to achieve end-to-end processing of cosmetics supply chain information, ensuring a clear data flow and logical closed loop. The data acquisition module obtains raw supply chain data from each stage of the supply chain. It verifies the data's integrity, logical consistency, and signature validity using a preset consensus algorithm. After removing abnormal data, it standardizes the format, field types, and encoding rules of the verified data according to preset standards, generating standardized supply chain data to provide a unified data foundation for subsequent modules. The trust assessment module receives access requests from supply chain participants carrying role identifiers. Based on the preset permission rules corresponding to the role identifiers and the field sensitivity of the standardized supply chain data, it calculates a permission matching score. If the score is higher than a preset threshold, authorization is granted. It then integrates historical credit data of participants within the authorized scope and merges it with real-time transaction log information to obtain a dynamic trust assessment value. The anomaly detection module continuously acquires real-time transaction log information from participants. It identifies abnormal behavior characteristics in the logs using a preset anomaly detection algorithm. If an anomaly is detected, it lowers the dynamic trust assessment value according to preset rules and updates the supply chain credit data within the authorized scope based on the updated assessment value, forming an updated integrated credit dataset. The information display module uses dynamic trust assessment values ​​to verify the consistency of hash links between nodes in the cosmetic supply chain traceability path. It also combines updated integrated credit datasets to extract historical behavior records of participants at each node and quantify and calculate real-time credit scores to determine the creditworthiness of participants. When all nodes pass consistency verification and the creditworthiness of participants meets the standards, the module integrates the double-verified supply chain data to construct a traceability chain, extracts trusted information to generate an information sharing view, and transmits it to the consumer end through a preset encrypted distribution mechanism, outputting transparent information display results.

[0107] In this embodiment, the data acquisition module ensures the integrity and uniformity of the original data through consensus algorithms and standardized processing, solving the problem of heterogeneous supply chain data. The trust assessment module achieves refined management of sensitive supply chain data based on precise permission matching of role identification and data sensitivity, reducing the risk of data leakage. At the same time, the generated dynamic trust assessment value provides a core assessment basis for subsequent stages. The anomaly detection module ensures the timeliness and accuracy of credit data through real-time monitoring and credit updates. The information display module ensures the authenticity and reliability of the traceability chain through dual verification of node hash consistency and participant credibility judgment. The final transparent information output not only meets the traceability and security control needs of industry supervision, but also provides consumers with credible supply chain information, effectively improving the transparency and credibility of the cosmetics supply chain.

[0108] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0109] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the information processing method for a cosmetic supply chain as described above.

[0110] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0111] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and 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 disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0112] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. An information processing method for a cosmetic supply chain, characterized by, The method comprises: Obtaining cosmetic supply chain original supply chain data, checking the integrity of the original supply chain data using a preset consensus algorithm, and standardizing the data that passes the check to generate standardized supply chain data; Calculating the permission matching score of the standardized supply chain data and the supply chain data access request, and if the permission matching score is higher than the preset permission threshold, integrating the credit data within the authorized range to obtain a dynamic trust evaluation value; Obtaining real-time transaction log information, identifying abnormal behavior of the real-time transaction log information through an abnormality detection algorithm, and reducing the dynamic trust evaluation value to obtain an updated integrated credit data set; Verifying the hash consistency of the cosmetic supply chain traceability path node based on the dynamic trust evaluation value, determining the credit credibility of the corresponding node participant in combination with the integrated credit data set, and integrating to obtain the information display result of the cosmetic supply chain.

2. The method of claim 1, wherein, The method comprises: Obtaining original supply chain data in the cosmetic supply chain; the original supply chain data includes production process parameters, logistics track information, and sales terminal data; Based on the preset consensus algorithm of the blockchain network, the integrity of the original supply chain data is checked, and the initial supply chain data that passes the integrity verification is selected; According to the preset standard, the initial supply chain data is standardized, including data format, field type and coding rules, to generate standardized supply chain data that meets the unified standard.

3. The method of claim 1, wherein, The method comprises: Receiving a supply chain data access request issued by a supply chain participant, the request carrying a role identifier; the supply chain data access request is directed to the standardized supply chain data; Based on the role identifier and the preset permission rules corresponding to the standardized supply chain data, the permission matching score is calculated to obtain the permission matching score; If the permission matching score is higher than the preset permission threshold, the access authorization is determined to be valid; When the access authorization is valid, the supply chain credit data within the authorized range corresponding to the role identifier is integrated to obtain an integrated credit data set; Obtaining real-time transaction log information of the supply chain participant, and performing fusion calculation on the integrated credit data set and the real-time transaction log information to obtain a dynamic trust evaluation value.

4. The method of claim 3, wherein, The permission matching score is calculated by the following formula: wherein, represents the permission matching score, , , respectively represent the weight coefficients of role compliance and data access adaptability, represents the role permission level coefficient, which is preset according to the cosmetic supply chain role type, , represents the role historical compliance coefficient, which is calculated by weighting the data submission accuracy rate and the compliance operation record proportion in the past 12 months, , represents the total number of fields to be accessed in the standardized supply chain data, represents the sensitivity weight of the th data field, , represents the access adaptability of the role to the th field, .

5. The method of claim 1, wherein, The method comprises: Adjusting the supply chain data access permission range of the supply chain participant according to the dynamic trust evaluation value; Using a preset abnormality detection algorithm to identify abnormal behavior of the real-time transaction log information to obtain an abnormal behavior identification result; If the abnormal behavior identification result is abnormal behavior, the dynamic trust evaluation value is reduced according to the preset rules. Update the supply chain credit data in the authorization range corresponding to the role identifier based on the reduced dynamic trust evaluation value to obtain an updated integrated credit data set.

6. The method of claim 5, wherein, The dynamic trust evaluation value is calculated by the following formula: wherein, represents a dynamic trust evaluation value, , represents the weight of historical credit and real-time transaction data, respectively, , , represents the credit data sub-item weight, represents the rate of performance, represents the rate of compliance, represents the consumer feedback score, , represents the transaction data adjustment coefficient, calibrated according to the transaction scale of the cosmetic supply chain, represents the timeliness decay coefficient, represents the transaction frequency in the past 30 days, represents the average transaction performance timeliness, represents the total number of abnormal behavior types, represents the severity weight of the type of abnormal behavior, represents the number of occurrences of the type of abnormal behavior in the real-time transaction log.

7. The method of claim 1, wherein, The dynamic trust evaluation value is calculated by the following formula: The dynamic trust evaluation value is calculated by the following formula: The dynamic trust evaluation value is calculated by the following formula: If the hash links between the path nodes are consistent, it is determined that the traceability path continuity verification is passed. Based on the updated integrated credit data set, the credit reliability of each path node corresponding to the supply chain participant is calculated; the credit reliability includes the historical behavior records of each node participant; the historical behavior records include performance records, compliance records, and abnormal behavior records. The historical behavior records are quantitatively calculated using a preset credit evaluation algorithm to obtain real-time credit scores of each participant. If the real-time credit score is higher than a preset credit threshold, it is determined that the credit reliability of the corresponding node participant meets the standard. When all path nodes pass the consistency verification and the corresponding participants of each node are determined to meet the credit reliability standard, a cosmetic supply chain traceability chain is constructed. From the supply chain traceability chain, trusted information that has passed node consistency verification and participant credit reliability verification is extracted to generate an information sharing view.

8. An information processing device for a cosmetic supply chain, characterized by, The information sharing view is transmitted to the consumer end through a preset encryption distribution mechanism to obtain a cosmetic supply chain transparent information display result. The device comprises: A data acquisition module for acquiring cosmetic supply chain original supply chain data, checking the integrity of the original supply chain data using a preset consensus algorithm, and standardizing the checked data to generate standardized supply chain data; A trust evaluation module for calculating a permission matching score for the standardized supply chain data and supply chain data access requests, and integrating credit data within the authorized range to obtain a dynamic trust evaluation value if the permission matching score is higher than a preset permission threshold; An anomaly detection module for obtaining real-time transaction log information, identifying abnormal behaviors in the real-time transaction log information through an anomaly detection algorithm, and reducing the dynamic trust evaluation value to obtain an updated integrated credit data set; 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. An information display module for verifying the hash consistency of the cosmetic supply chain traceability path nodes based on the dynamic trust evaluation value, determining the credit reliability of the corresponding node participants in combination with the integrated credit data set, and integrating to obtain an information display result of the cosmetic supply chain.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.

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