Commodity information management method and system based on big data analysis

Abstract

The invention discloses a commodity information management method and system based on big data analysis. The method relates to the technical field of commodity information management, and comprises the following steps: multi-source data acquisition and synchronous quality quantification, data synchronous adaptive regulation and control, supply chain information penetration and risk quantification, and risk intelligent tuning and collaborative response. According to the method, a multi-source commodity information acquisition channel is established, the accuracy and timeliness of supplier service data synchronization are quantified, and whether the information visualization link is entered after self-adaptive regulation and control is judged according to the accuracy and timeliness; and then, obtaining supply chain information penetration and risk parameters, and judging whether to enter a supply chain collaboration link after intelligent adjustment and optimization so as to balance data, risks, resources and efficiency, thereby improving the commodity information management reliability and improving the commodity information management efficiency. The problem of low commodity information management reliability caused by islanding of the business process and the cooperation mode in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of commodity information management technology, and in particular to a commodity information management method and system based on big data analysis. Background Technology

[0002] The system dynamically loads required fields based on the attributes of different product categories, such as agricultural and sideline products, clothing, and footwear (e.g., production date and shelf life of food, registration number and scope of application of medical devices, and place of origin and testing report of non-standard agricultural and sideline products). Subsequently, the approval process automatically flows according to preset rules (e.g., purchase amount, category risk level). During the purchase plan generation stage, a rule engine dynamically generates accurate purchase plans by integrating multi-dimensional data such as historical sales volume, inventory levels, supplier delivery capabilities, product loss rates, and seasonal fluctuations.

[0003] By using standardized interfaces and data consistency mapping, supplier qualification information (such as business licenses, filing certificates and performance records) can be bound to specific products or batches, ensuring real-time synchronization and accurate interaction of data in supplier sourcing, inquiry / bidding, contract signing and order collaboration.

[0004] During the delivery and acceptance phase, the system automatically links orders, product compliance status, and supplier qualification validity, supporting quality inspection, quantity verification, and batch management (such as food traceability). It also drives reconciliation and payment processes through electronic vouchers (orders, receiving slips, and acceptance slips), reducing manual intervention. Finally, it continuously collects supplier performance data (such as on-time delivery rate and quality pass rate) and combines this data feedback (such as performance capability and dynamic variables like market fluctuations) to optimize subsequent procurement decisions.

[0005] For example, the product information management method, device and mobile terminal disclosed in Chinese invention patent application CN107220876B includes: acquiring at least one product image; displaying at least one product image in a preset manner; acquiring product tags for each displayed product image; and determining the product category of the product corresponding to the product image based on the product tags.

[0006] For example, the product information management method, host platform, and product information management component disclosed in Chinese invention patent application CN111476637B include: responding to a product viewing request, calling the target developer's mini-program to which the target product belongs to display the landing page of the target product; after the user places an order or makes a payment for the target product based on the target developer's mini-program, obtaining and storing the product order information reported by the target developer's mini-program.

[0007] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In multi-category product procurement and supplier collaboration scenarios, product information (such as price, inventory, specifications, and packaging units) is constantly changing. Data in the buyer's PIM system is frequently out of sync with the supplier's actual information, leading to incorrect purchase orders, inaccurate inventory, and distorted cost accounting. For example, a supplier may have discontinued a certain product model, but it may still be available for order in the buyer's catalog; the supplier may have updated its prices, but the buyer may still negotiate based on the old prices. Traditional collaboration often relies on the buyer periodically requesting data or the supplier sending data in batches (e.g., weekly), rather than event-driven real-time synchronization. This creates significant "data latency." When the buyer discovers data problems (such as incorrect images), there is no efficient channel to directly report to the supplier and correct them at the source. Often, issues are resolved offline via email, phone, or other means. Corrections are not automatically updated back into the system, leading to recurring problems. Suppliers believe they have provided "correct" information, and the buyer may unintentionally tamper with the data during data entry or cleaning. Furthermore, there is a lack of consensus between the two parties regarding "which version is the golden record." For complex commodities with multiple tiers of suppliers (raw materials, components, finished products), it is difficult for the purchaser to see the complete product spectrum and supply chain panorama in a PIM (Product Information Management) system. When a change occurs at a certain level (such as a chip shortage), it is impossible to quickly assess its impact on the procurement of finished products at the next higher level. Similarly, it is impossible to effectively manage dynamic information such as supplier qualifications and compliance certificates (such as RoHS and FDA). Traditional PIM design focuses on the binary relationship of "purchaser-direct supplier" and the attributes of the product itself, lacking the ability to map and manage multi-layered supply networks. Suppliers are unwilling (and have no obligation) to expose their supplier networks and detailed information to the purchaser, considering it a trade secret. Compliance certificates and test reports are mostly unstructured documents such as PDFs and images, making it difficult for PIM systems to automatically extract, parse, and monitor key information (such as expiration dates). This results in low reliability of commodity information management due to the silos of business processes and collaboration models. Summary of the Invention

[0008] To address the technical problem of low reliability in product information management due to siloed business processes and collaboration models in existing technologies, this invention provides a product information management method and system based on big data analytics. The technical solution is as follows: On the one hand, a product information management method based on big data analysis is provided. This method includes: S101, establishing a multi-source product information collection channel to collect multi-dimensional information throughout the product's lifecycle, obtaining synchronization accuracy parameters, and quantifying the consistency and timeliness of core supplier business data between the purchaser and supplier based on these parameters, thus obtaining the supplier business data synchronization accuracy and timeliness; S102, determining whether to perform adaptive adjustment of synchronization accuracy and timeliness based on the supplier business data synchronization accuracy and timeliness, thereby achieving adaptive adjustment of supplier business data synchronization and achieving an optimal balance between data consistency and resource consumption; if If yes, then proceed to the information visualization and correlation mapping stage after adjustment; otherwise, proceed directly to the information visualization and correlation mapping stage. S103: Obtain information penetration and risk parameters from the information visualization and correlation mapping stage to quantify the manageability of key dynamic information of the multi-level supply chain by the purchasing party, and obtain supply chain information penetration and risk. S104: Based on the supply chain information penetration and risk, determine whether to perform intelligent optimization of information penetration and risk, optimize the allocation of information management resources, and achieve an adaptive balance between the accuracy of supply chain risk prevention and control and operational efficiency. If yes, proceed to the supply chain collaboration stage after adjustment; otherwise, proceed directly to the supply chain collaboration stage.

[0009] On the other hand, a commodity information management system based on big data analysis is provided. This system includes: a multi-source data acquisition and synchronization quality quantification module, a data synchronization adaptive control module, a supply chain information penetration and risk quantification module, and a risk intelligent optimization and collaborative response module. The multi-source data acquisition and synchronization quality quantification module is used to establish multi-source commodity information acquisition channels, collect multi-dimensional information throughout the commodity's lifecycle, obtain synchronization accuracy parameters, and quantify the consistency and timeliness of core supplier business data between the purchaser and the supplier based on these parameters, thus obtaining the supplier business data synchronization accuracy and timeliness. The data synchronization adaptive control module is used to determine whether to implement adaptive control of synchronization accuracy and timeliness based on the supplier business data synchronization accuracy and timeliness, thereby achieving the synchronization of supplier business data... The system employs an adaptive adjustment mechanism to achieve an optimal balance between data consistency and resource consumption. If the adjustment is successful, information visualization and correlation mapping are performed after the adjustment; otherwise, the process proceeds directly. A supply chain information penetration and risk quantification module is used to obtain information penetration and risk parameters from the information visualization and correlation mapping process. This parameter quantifies the manageability of key dynamic information across the multi-level supply chain by the purchasing party, resulting in supply chain information penetration and risk assessment. A risk intelligent optimization and collaborative response module determines whether to perform information penetration and risk intelligent optimization based on the supply chain information penetration and risk assessment. This optimizes the allocation of information management resources, achieving an adaptive balance between the accuracy of supply chain risk prevention and control and operational efficiency. If the adjustment is successful, a supply chain collaboration phase is performed after the adjustment; otherwise, the process proceeds directly.

[0010] Beneficial effects The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: 1. By establishing multi-source commodity information collection channels, multi-dimensional information throughout the entire commodity lifecycle is collected to obtain synchronous and accurate parameters. Based on these parameters, core supplier business data is quantified to ensure consistency and timeliness between the buyer and supplier, resulting in the accuracy and timeliness of supplier business data synchronization. Adaptive adjustments to the synchronization accuracy and timeliness are then implemented based on these parameters, achieving an optimal balance between data consistency and resource consumption. Furthermore, information penetration and risk parameters are obtained from the information visualization and correlation mapping stages to quantify the buyer's manageability of key dynamic information across the multi-level supply chain, resulting in supply chain information penetration and risk. Intelligent optimization of information penetration and risk is then implemented based on these parameters, optimizing information management resource allocation and achieving an adaptive balance between supply chain risk control accuracy and operational efficiency, thereby improving the reliability of commodity information management.

[0011] 2. By implementing dual-threshold, trend-aware adaptive polling control, the system can dynamically adjust the data polling strategies (including polling frequency, verification granularity, and retransmission mechanism) of different suppliers based on real-time monitoring of accuracy and timeliness indicators and their changing trends. While ensuring the reliability of critical data synchronization, it intelligently reduces resource consumption in low-risk scenarios, achieving precise dynamic resource allocation and synchronization efficiency optimization. By implementing latency adaptive compensation control, the system can automatically select the optimal compensation strategy (such as predictive data filling, incremental snapshot completion, or cross-layer data redundancy) based on real-time data synchronization latency analysis. Under the premise of eventual data consistency, it proactively offsets the information lag caused by transmission latency, achieving seamless and continuous support for critical business decisions, thereby improving the reliability of commodity information management.

[0012] 3. By implementing inverse granularity dynamic mapping control driven by activity level, the granularity level of data collection and synchronization can be dynamically configured in reverse according to the data activity level (such as update frequency and business importance). Fine-grained real-time synchronization strategies are matched for high-activity data, and coarse-grained batch update strategies are matched for low-activity data. While maintaining the accuracy of key data, the overall data synchronization load is significantly reduced. Through asymmetric control of contingency plan duration driven by dual-dimensional thresholds, the rehearsal duration and triggering conditions of emergency plans can be dynamically adjusted according to the thresholds of risk level and penetration difficulty. Long-term in-depth rehearsals are implemented for high-risk, low-penetration scenarios, and short-term lightweight rehearsals are implemented for low-risk, high-penetration scenarios. This achieves optimal matching between emergency plan resource allocation and real risk scenarios, thereby improving the reliability of commodity information management. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart of a product information management method based on big data analysis is provided for embodiments of this application; Figure 2 A flowchart illustrating the time-delay adaptive compensation control process of a commodity information management method based on big data analysis, provided in an embodiment of this application; Figure 3 A flowchart illustrating the activity level-driven inverse granularity dynamic mapping control of a commodity information management method based on big data analysis, provided for embodiments of this application; Figure 4 This is a schematic diagram of the structure of a commodity information management system based on big data analysis, provided as an embodiment of this application. Detailed Implementation

[0015] The technical solution provided in this application will now be described with reference to the accompanying drawings.

[0016] To facilitate understanding of the embodiments of this application, the following points will be explained first: First, in this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the preceding and following related objects, but it does not exclude the possibility of indicating an "and" relationship; the specific meaning can be understood in context. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c; a and b; a and c; b and c; or a and b and c. Here, a, b, and c can be single or multiple.

[0017] Second, the use of prefixes such as "first" and "second" in this application is solely for the purpose of distinguishing and describing different things belonging to the same category, and does not constrain the order, size, or quantity of things. For example, "first message" and "second message" are simply different messages, and there is no chronological, size, or priority relationship between them.

[0018] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0019] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1 The diagram shown is a flowchart of a product information management method based on big data analysis provided in an embodiment of this application. The method includes the following steps: S101, establish a multi-source product information collection channel to collect multi-dimensional information throughout the product lifecycle, obtain synchronized and accurate parameters, and quantify the consistency and timeliness of core supplier business data (price, inventory, status) between the purchaser and the supplier based on the synchronized and accurate parameters, thereby obtaining the synchronization accuracy and timeliness of supplier business data; the multi-source product information collection channel shall include at least e-commerce platform interface channel, offline store POS channel, supply chain management system channel, user review interaction channel, and third-party product database channel; the multi-dimensional information throughout the product lifecycle shall include at least basic product attribute information, product circulation attribute information, product transaction attribute information, product evaluation attribute information, and product inventory attribute information.

[0021] It should be understood that the specific steps to obtain the accuracy and timeliness of supplier business data synchronization are as follows: The accurate synchronization parameters include the average time for resolving information anomalies, the average latency for order status synchronization, and the frequency of synchronization task execution. The average time for resolving information anomalies refers to the average time taken from the discovery of an order status synchronization anomaly (automatic alarm / manual reporting) to complete anomaly repair, data consistency restoration, and the formation of a closed-loop record; this is the ratio of the difference between the anomaly closure completion time and the anomaly discovery time to the number of anomalies within the statistical period. The average latency for order status synchronization refers to the average time difference between a change in the order status on the supplier's side (e.g., pending shipment - shipped - signed for - returned) and the completion of the status update in the buyer's system, making it readable by the business system; this is the ratio of the difference between the buyer's system status update effective time and the supplier's status change time to the number of status synchronizations; the frequency of synchronization task execution refers to the number of times the order status synchronization task is executed per unit of time; this frequency is obtained through the configuration table of the scheduled task system.

[0022] The average duration impact value is obtained by correcting the ratio of the average duration benchmark value to the average duration of information anomaly closed-loop resolution using an average duration compensation factor. Similarly, the average latency impact value is obtained by correcting the ratio of the average latency benchmark value to the average latency of order status synchronization using an average latency compensation factor. Finally, the execution frequency impact value is obtained by correcting the ratio of the synchronization task execution frequency to the execution frequency benchmark value using an execution frequency compensation factor. These average duration impact values, average latency impact values, and execution frequency impact values ​​are coupled to obtain the supplier business data synchronization accuracy and timeliness. The specific constraint expressions for the supplier business data synchronization accuracy and timeliness are as follows: ; ; In the formula, W represents the accuracy and timeliness of supplier business data synchronization; a1 represents the average duration compensation factor obtained from the information management database; a2 represents the average latency compensation factor obtained from the information management database; a3 represents the execution frequency compensation factor obtained from the information management database; N0 represents the average duration benchmark value obtained from the information management database; H0 represents the average latency benchmark value obtained from the information management database; R0 represents the execution frequency benchmark value obtained from the information management database; N represents the average time for information anomaly closed-loop resolution; H represents the average latency for order status synchronization; and R represents the synchronization task execution frequency.

[0023] It's important to explain that a higher synchronization task execution frequency means that supplier-side status change information can be collected and transmitted to the buyer's system more quickly, preventing information from accumulating while waiting for the next synchronization task, and resulting in a shorter average latency for order status synchronization. A higher synchronization task execution frequency also means faster detection of order status synchronization anomalies. The earlier the anomaly is detected, the earlier maintenance personnel or automatic repair mechanisms can intervene, preventing the anomaly from escalating, and resulting in a shorter average time for information anomaly closed-loop resolution. Conversely, a longer average latency for order status synchronization means that supplier-side status change information cannot be transmitted to the buyer's system in a timely manner, requiring more time for the system to determine "status inconsistency" as an anomaly. This delay in anomaly detection leads to a postponement of the repair initiation time, and a longer average time for information anomaly closed-loop resolution. Meanwhile, the average time for resolving information anomalies in a closed loop is negatively correlated with the accuracy and timeliness of supplier business data synchronization. The longer the average time for resolving information anomalies in a closed loop, the more inconsistent data will continue to affect business processes and may trigger new synchronization errors, resulting in lower accuracy and timeliness of supplier business data synchronization. The average latency for order status synchronization is also negatively correlated with the accuracy and timeliness of supplier business data synchronization. The longer the average latency for order status synchronization, the more significant the lag in the supplier data obtained by the purchasing system, which cannot reflect the actual business status in a timely manner, resulting in lower accuracy and timeliness of supplier business data synchronization. The frequency of synchronization task execution is positively correlated with the accuracy and timeliness of supplier business data synchronization. The higher the frequency of synchronization task execution, the faster the order status change information from the supplier can be collected and transmitted to the purchasing system, avoiding information backlog due to waiting for the next synchronization task, significantly reducing the average latency for order status synchronization, and resulting in higher accuracy and timeliness of supplier business data synchronization.

[0024] S102, determine whether to perform adaptive adjustment of synchronization accuracy and timeliness based on the synchronization accuracy and timeliness of supplier business data, so as to achieve adaptive adjustment of supplier business data synchronization and thus achieve the optimal balance between data consistency and resource consumption; if yes, then perform information visualization and association mapping after adjustment; if no, then directly perform information visualization and association mapping.

[0025] Furthermore, the specific steps for determining whether to perform adaptive adjustment of synchronization accuracy and timeliness are as follows: If the accuracy and timeliness of the supplier's business data synchronization are greater than or equal to the synchronization accuracy benchmark, then intelligent optimization of data synchronization accuracy will not be performed; otherwise, dual threshold-trend-aware adaptive polling control and latency adaptive compensation control will be performed.

[0026] As further explained in detail, the specific steps for implementing dual-threshold-trend-aware adaptive polling control are as follows: Within each preset monitoring period, the response latency of each poll is collected and recorded in real time. If the response latency is greater than the preset response baseline value, the poll is determined to be an abnormal poll, and the number of abnormal polls within the current preset monitoring period is counted.

[0027] At the end of the current preset monitoring period, the result of the analysis of the number of abnormal polling times in the current preset monitoring period and the proportion of the current preset monitoring period is recorded as the periodic polling abnormality rate. If the periodic polling abnormality rate is less than or equal to the lower limit of the abnormality rate benchmark, it is judged as a normal state level.

[0028] If the periodic polling anomaly rate is within the anomaly rate benchmark range, it is determined to be a moderate anomaly state. The anomaly rate benchmark range refers to the open interval formed by the lower limit and upper limit of the anomaly rate benchmark.

[0029] If the periodic polling anomaly rate is greater than or equal to the upper limit of the anomaly rate benchmark, it is judged as a severe anomaly state.

[0030] Based on the status level and the accuracy and timeliness of supplier business data synchronization, a preset level-number mapping table is queried to output the basic polling count for the next monitoring cycle. The preset level-number mapping table is configured such that the higher the status level, the lower the basic polling count, thus achieving polling frequency attenuation under abnormal pressure. This implements "intelligent frequency attenuation under abnormal pressure," automatically reducing the polling frequency when the system itself or the network environment malfunctions, alleviating the load, providing breathing room for abnormal nodes (which may be supplier systems or network links), and avoiding a cascading failure effect; it also protects resources by prioritizing the allocation of limited system computing and bandwidth resources to other data synchronization tasks that are responding normally, ensuring the stability and resource utilization efficiency of the overall synchronization system.

[0031] The implementation of dual-threshold-trend-aware adaptive polling control also includes: The system acquires the anomaly rate sequence from historical monitoring periods and calculates the first-order trend value using the difference mean method. If the first-order trend value is greater than or equal to the positive trend threshold, it inputs the current first-order trend value into a preset level-frequency mapping table, outputting the target basic polling frequency for the next monitoring period. The target basic polling frequency is a negative increase correction, further reducing the number of polling cycles. Even if the current absolute anomaly rate may not be high, the deteriorating trend is clear. This "negative increase correction" further reduces the number of polling cycles on the basis of basic control. It reduces pressure before the problem fully erupts, smoothing the performance curve and demonstrating the system's predictive and buffering capabilities.

[0032] If the first-order trend value is within the trend threshold range, the current basic polling count is maintained. The trend threshold range represents the open interval formed by the negative trend threshold and the positive trend threshold. The current basic polling count is maintained. The system is in a dynamic equilibrium or transitional state, avoiding unnecessary strategy fluctuations and maintaining the stability of regulation.

[0033] If the first-order trend value is less than or equal to the negative trend threshold, the current first-order trend value is input into a preset level-frequency mapping table, and the target basic polling frequency for the next monitoring cycle is output. The target basic polling frequency is a positive correction to increase the polling frequency. This indicates that previous adjustments may have been effective, or the external environment is improving. "Positive correction" is performed, appropriately increasing the polling frequency, and gradually improving the timeliness and granularity of data collection while ensuring controllable risks, thus realigning system resource utilization with data quality requirements.

[0034] In this embodiment, by combining threshold judgments based on two key dimensions—"current abnormal state" and "historical trend"—refined, multi-stage dynamic management of the data polling strategy is achieved. State thresholds address immediate risks, while trend thresholds predict future trends, forming a comprehensive defense and optimization system. This system can "intelligently" retreat to protect the system during anomalies and "cautiously" advance to improve performance during recovery, achieving an evolution from "brutal interruption" to "graceful degradation and smooth recovery." The polling frequency, a crucial resource, is always dynamically adapted to the current and foreseeable system health and data demands, ultimately ensuring the overall reliability of data synchronization tasks and optimal economic resource utilization in complex operating environments.

[0035] It should be noted that, as Figure 2The diagram shows a flowchart of a latency adaptive compensation control method for commodity information management based on big data analysis provided in this application embodiment. The specific process is as follows: After entering from the "Start" node, it is first determined whether the latency of state event transmission and reception is less than or equal to the baseline value. If so, latency adaptive compensation control is not performed. Otherwise, the preset latency-correction synchronization latency mapping table is queried, and then it is determined whether "latency increases and synchronization accuracy and timeliness are lower than the baseline value" is true. If so, an instruction to increase the correction synchronization latency is output, the increase value is calculated and coupled with the current correction synchronization latency to obtain the target value. If not, the correction synchronization latency is not adjusted, and the process ends.

[0036] As further explained in detail, the specific process of time delay adaptive compensation control is as follows: If the transmission and reception delay of status events is less than or equal to the baseline values ​​for transmission and reception delay, then adaptive delay compensation is not performed. This indicates that the data transmission channel is healthy and the real-time performance of the service data is within an acceptable range. It is determined that no compensation mechanism needs to be activated, thus avoiding unnecessary computational overhead and potential data logic complexity, and maintaining the system in a lightweight and efficient mode.

[0037] If the state event transmission and reception delay exceeds the baseline value, a preset delay-correction synchronization delay mapping table is queried based on the current state event transmission and reception delay and the supplier's business data synchronization accuracy and timeliness. The query logic of the preset delay-correction synchronization delay mapping table is configured as follows: when the state event transmission and reception delay increases and the supplier's business data synchronization accuracy and timeliness are less than the synchronization accuracy baseline value, an instruction to increase the supplier's business data correction synchronization delay is output. This instruction means inputting the current state event transmission and reception delay and the supplier's business data synchronization accuracy and timeliness into the preset delay-correction synchronization delay mapping table, outputting the adjusted synchronization delay value, and coupling the current adjusted synchronization delay value with the supplier's business data correction synchronization delay to obtain the target supplier's business data correction synchronization delay. This indicates that a perceptible delay has occurred in the network or processing stage, which may have threatened the timeliness of downstream business decisions (such as inventory updates and price synchronization) that rely on this state event. This proactively triggers a compensation and control process to counteract the risk of delayed business information caused by time delays.

[0038] In this embodiment, "precise and enhanced compensation in critical scenarios" is achieved. Individual transmission delays may be caused by temporary fluctuations; however, when "high latency" and "low synchronization quality" occur simultaneously, it strongly suggests a systemic or persistent bottleneck in the supply chain information flow. At this time, the data consistency and timeliness upon which downstream businesses rely face a dual challenge. To address this high-risk scenario, the "supplier business data correction synchronization latency" is dynamically increased, widening the internal waiting window for subsequent related business data (such as inventory and order status) to be synchronized. Compensation strategies such as predictive data filling, priority incremental synchronization, or enabling backup data pathways gain valuable buffer time. This allows the supply chain information system to intelligently weigh the trade-offs when facing uncertain network latency, finding the optimal balance between "unnecessary waiting" and "data inconsistency risk," providing continuous and reliable data continuity support for core business decisions.

[0039] S103, Obtain information penetration and risk parameters in the information visualization and correlation mapping process, which are used to quantify the manageability of key dynamic information of the multi-level supply chain by the purchaser, and obtain the supply chain information penetration and risk.

[0040] It should be understood that information penetration and risk parameters include the synchronous accuracy and validity value, the average time for early warning of supply disruption risks, and the average duration of risk response. Specifically, the synchronous accuracy and validity value refers to the accuracy and timeliness of the newly acquired supplier business data if adaptive adjustment of synchronization accuracy and timeliness is implemented; otherwise, the current accuracy and timeliness of the supplier business data is used. The average time for early warning of supply disruption risks refers to the average time difference between the time the system issues a valid warning and the time the risk actually occurs within a preset statistical period. The average duration of risk response refers to the average time elapsed between the time the risk is discovered or the warning is issued and the time the risk response plan is formally activated within a preset statistical period.

[0041] The synchronous accurate and effective impact value is obtained by correcting the ratio of the synchronous accurate and effective value to the synchronous accurate and effective benchmark value using a synchronous accurate and effective compensation factor. Similarly, the early warning impact value is obtained by correcting the ratio of the average early warning time to the early warning benchmark value using an early warning compensation factor. Finally, the average duration impact value is obtained by correcting the ratio of the average duration benchmark value to the average risk response duration using an average duration compensation factor. These synchronous accurate and effective impact values, early warning impact values, and average duration impact values ​​are coupled to obtain the supply chain information penetration and risk level. The specific limiting expressions for supply chain information penetration and risk level are as follows: ; ; In the formula, E represents the penetration and risk of supply chain information; c1 represents the synchronous, accurate, and effective compensation factor obtained from the information management database; c2 represents the early warning compensation factor obtained from the information management database; c3 represents the average duration compensation factor obtained from the information management database; K0 represents the synchronous, accurate, and effective benchmark value obtained from the information management database; G0 represents the early warning benchmark value obtained from the information management database; P0 represents the average duration benchmark value obtained from the information management database; K represents the synchronous, accurate, and effective value; G represents the average early warning time for supply disruption risks; and P represents the average risk response time.

[0042] It's important to understand that a higher synchronization accuracy and validity value means more accurate and consistent multi-level supplier data (such as capacity, inventory, production schedules, and logistics status) obtained by the purchaser. This allows the risk warning model to identify potential disruption risks in advance based on accurate basic data, resulting in a longer average warning time for supply disruption risks. A higher synchronization accuracy and validity value also means that when receiving a risk warning, the purchaser can directly determine the scope of the risk's impact (such as which materials, suppliers, and orders are involved) based on accurate supply chain data, without needing to spend extra time reconciling and verifying data with suppliers. This allows for rapid activation of contingency plans (such as switching to alternative suppliers, allocating inventory, and adjusting production plans), resulting in a shorter average risk response time. Furthermore, a longer average warning time for supply disruption risks provides the purchaser with more buffer time to respond, eliminating the need for hasty decisions and allowing for more composed risk assessment and resource allocation (such as contacting alternative suppliers and negotiating delivery dates), further shortening the average risk response time. Meanwhile, the accuracy and validity of synchronous data are positively correlated with supply chain information penetration and risk. The higher the accuracy and validity of synchronous data, the more accurate and consistent the multi-level supplier data (such as material inventory of second-tier suppliers, production scheduling plans of core production lines, and logistics status in transit) obtained by the purchaser, and the absence of problems such as "data distortion" and "information misalignment," resulting in greater supply chain information penetration and risk. The average early warning time for supply disruption risks is also positively correlated with supply chain information penetration and risk. The longer the average early warning time for supply disruption risks, the earlier the purchaser captures abnormal signals in the supply chain (such as material supply interruptions from third-tier suppliers and shutdown plans for key equipment), resulting in greater supply chain information penetration and risk. The average risk response time is negatively correlated with supply chain information penetration and risk. The longer the average risk response time, the less key data from multi-level supply chains is available, and information can only be temporarily "supplemented" after the risk occurs, leading to delayed decision-making, resulting in lower supply chain information penetration and risk.

[0043] S104. Based on the supply chain information penetration and risk assessment, determine whether to perform information penetration and risk intelligent adjustment, optimize the allocation of information management resources, and achieve an adaptive balance between the accuracy of supply chain risk prevention and control and operational efficiency. If so, proceed with the supply chain collaboration process after adjustment, automatically push risk warnings and key dynamic information (such as the capacity delay of second-tier suppliers) to the affected first-tier suppliers, procurement departments, production planning departments, etc., and initiate a formal collaborative response process, such as jointly developing alternative solutions and adjusting delivery plans. If not, proceed directly with the supply chain collaboration process.

[0044] Furthermore, the specific process for determining whether to perform information penetration and intelligent risk adjustment is as follows: If the supply chain information penetration and risk level are greater than or equal to the penetration and risk benchmark values, then information penetration and risk intelligent optimization will not be performed; otherwise, then the inverse granularity dynamic mapping control driven by the activity level and the contingency plan duration asymmetric control driven by the two-dimensional threshold will be performed.

[0045] It should be noted that, as Figure 3 The diagram shows a flowchart of an activity-level driven inverse granularity dynamic mapping control method for commodity information management based on big data analysis provided in this application embodiment. The specific process is as follows: First, starting from "Start", pre-configuration is completed, data fields are divided into high / medium / low activity levels, and a mapping relationship of "the higher the activity, the coarser the information penetration granularity; the lower the activity, the finer the granularity" is established. Next, the user's information penetration query request for the target business entity is received, the real-time recommended penetration granularity of the entity is obtained, and it is then determined whether it is ≥ the baseline penetration granularity. If so, the current recommended penetration granularity is set to the real-time value to avoid frequent page refreshes or data overload; otherwise, it is set to the baseline value to extract and assemble information from the data source. Finally, the query result is generated based on the determined recommended penetration granularity, and the process ends.

[0046] As further explained in detail, the specific process for performing activity level-driven inverse granularity dynamic mapping control is as follows: The dynamic update frequency of data fields is divided into multiple activity levels, including high, medium, and low activity levels. Based on these activity levels, supply chain information penetration and risk, and the granularity of supply chain information penetration, a mapping relationship between activity level and penetration granularity is established. This mapping relationship maps each activity level to a preset information penetration granularity level. The configuration of this mapping relationship is as follows: the higher the activity level of a data field, the coarser the assigned information penetration granularity level; conversely, the lower the activity level of a data field, the finer the assigned information penetration granularity level. For high-frequency (high-activity) data (such as market prices changing every second), the system assigns a coarser penetration granularity (e.g., penetrating only to the real-time quotes of first-tier suppliers, rather than the raw material cost details of their second-tier suppliers). This effectively avoids the massive and frequent data synchronization and computational overhead caused by tracking overly in-depth and detailed dynamic data, preventing system resources from being overwhelmed by rapidly changing but strategically limited data details. For data with low-frequency changes (low activity) (such as quarterly updated supplier qualification certifications and annual contract terms), a finer granularity of penetration is assigned (e.g., down to factory certification information of tier-3 suppliers). Because this type of data is stable, the marginal cost of in-depth tracking is low, but it can obtain high-value, comprehensive supply chain information, thereby maximizing the decision support value of information penetration at low cost.

[0047] When a user requests a penetration query for a target business entity, the real-time recommendation penetration granularity of that entity is obtained, and then compared with the baseline penetration granularity. Specifically: If the real-time recommendation penetration granularity is greater than or equal to the baseline penetration granularity, the current recommendation penetration granularity will be set to the real-time recommendation penetration granularity to prevent frequent page refreshes or data overload caused by high-frequency updates.

[0048] If the real-time recommended penetration granularity is smaller than the baseline penetration granularity, the current recommended penetration granularity is set as the baseline penetration granularity to extract and assemble information from the data source and generate the final penetration query results. This enables the penetration granularity to be dynamically adjusted in a "risk-response manner." For example, even if a material's activity level is usually "medium," when the system detects a high-risk event (such as an earthquake) in its supply chain path (e.g., the location of a second-tier supplier), its penetration priority will be temporarily increased. This may lead to the system dynamically optimizing the mapping relationship and assigning it a finer penetration granularity in a short period of time to obtain deeper emergency information. This ensures that, at critical moments, the depth of information penetration can be accurately matched with the level of business risk.

[0049] In this embodiment, by establishing an inverse mapping relationship between "activity level" and "penetration granularity" and integrating real-time risk assessment, intelligent resource allocation and dynamic experience optimization for supply chain information penetration queries are achieved. Its core effect is that the system can automatically match differentiated information tracking depths to different data based on the dynamic update frequency (activity level) of data fields and real-time supply chain risks. For frequently updated, highly active data (such as real-time prices), the system allocates a coarser penetration granularity. This suppresses excessively deep and frequent data tracking requests from the source, effectively avoiding frequent front-end page refreshes, chart jumps, and system data processing overload caused by instantaneous data changes, ensuring smooth interaction and system stability. For low-frequency, stable, low-activity data or key entities in high-risk scenarios, the system recommends a finer penetration granularity, ensuring sufficient depth of insight while maintaining controllable costs. In the final query response, the system makes a final decision by comparing the penetration granularity of the dynamic recommendations with the guaranteed baseline granularity: when the recommendation granularity is coarse, the recommended value is used to prioritize performance and user experience; when the recommendation granularity is finer, the baseline value is used to ensure that the minimum information depth requirement is met. Overall, this technology transforms supply chain penetration queries from a "one-size-fits-all" depth mode to a flexible "on-demand intelligent adaptation" mode, maximizing the effective delivery of information value with limited resources.

[0050] It should be further explained that the specific steps of the dual-threshold driven asymmetric control of the contingency plan duration are as follows: The system monitors the incoming real-time event stream and performs feature analysis on events within a preset analysis time window. Based on the number of newly generated transition events and the number of events generated by internal state self-evolution within the preset analysis time window, the system obtains the driver update ratio for the current analysis time window. The driver update ratio represents the ratio of the number of newly generated transition events to the sum of the number of newly generated transition events and the number of events generated by internal state self-evolution.

[0051] Establish an update mode-processing strategy mapping table. The update mode-processing strategy mapping table associates each event update mode with a set of preset contingency plan processing time limit parameters. The time limit parameters include at least: the maximum calculation time for contingency plan matching and the maximum waiting time for contingency plan details to be retrieved.

[0052] Dual-dimensional threshold-driven asymmetric control of contingency plan duration also includes: If the driver update ratio is less than or equal to the lower limit of the update ratio reference, it is determined to be an autonomous evolution-dominated mode. The current driver update ratio, supply chain information penetration and risk input are entered into the update mode-processing strategy mapping table, and the maximum calculation time for contingency plan matching is output to extend the calculation and recall time window, improve decision quality and the accuracy of contingency plan matching.

[0053] If the proportion of driver updates is greater than or equal to the upper limit of update proportion reference, it is determined to be an active driver-dominated mode. The current driver update proportion, supply chain information penetration and risk input are entered into the update mode-processing strategy mapping table, and the contingency plan details are output to retrieve the maximum waiting time, so as to shorten the decision-making and retrieval time limit, thereby prioritizing response timeliness and high concurrency throughput, and ensuring that feasible contingency plans are output within the critical time window.

[0054] If the percentage of driver updates is within the update percentage reference range, it is determined to be a hybrid update mode, and the current plan matching and call-out duration are maintained. The update percentage reference range refers to the open interval formed by the lower limit of the update percentage reference and the upper limit of the update percentage reference.

[0055] In this embodiment, by monitoring event flow characteristics in real time and calculating the proportion of driver updates, the system dynamically identifies event evolution modes (autonomous evolution-dominated, proactively driven, or hybrid modes). Based on this mode identification and real-time supply chain information penetration and risk assessment, it makes two-dimensional decisions, achieving asymmetric intelligent control over the processing time of emergency plans. Its core effect is that the system can dynamically adjust the balance between the "quality" and "effectiveness" of plan matching based on the inherent dynamic characteristics of the event (whether triggered externally or evolved internally) and the external risk situation. In the autonomous evolution-dominated mode (with a low proportion of driver updates), the system automatically extends the calculation and retrieval time window for plan matching, allowing for more complex algorithm analysis and more comprehensive plan comparison, thus prioritizing decision depth and matching accuracy, suitable for optimization scenarios with controllable risks and high complexity. In the proactively driven mode (with a high proportion of driver updates), the system significantly shortens the waiting time for retrieving plan details, prioritizing the system's real-time response speed and high concurrency throughput, ensuring rapid output of feasible plans within the time window of the sudden event, meeting the timeliness requirements of emergency response. In the hybrid mode, the system maintains the current strategy to remain stable. This technology enables emergency management systems to intelligently allocate limited computing and time resources, adaptively switching between "well-thought-out and precise plans" and "rapid and feasible plans," thereby maintaining optimal overall response performance in a dynamic event flow and risk environment.

[0056] like Figure 4The diagram shown is a structural schematic of a commodity information management system based on big data analysis provided in this application embodiment. It includes: a multi-source data acquisition and synchronization quality quantification module, a data synchronization adaptive control module, a supply chain information penetration and risk quantification module, and a risk intelligent optimization and collaborative response module. The multi-source data acquisition and synchronization quality quantification module is used to establish a multi-source commodity information acquisition channel, collect multi-dimensional information throughout the commodity's lifecycle, obtain synchronization accuracy parameters, and quantify the consistency and timeliness of core supplier business data between the purchaser and the supplier based on these parameters, thus obtaining the supplier business data synchronization accuracy and timeliness. The data synchronization adaptive control module is used to determine whether to implement adaptive control of synchronization accuracy and timeliness based on the supplier business data synchronization accuracy and timeliness, thereby achieving control over the supplier business data. The system employs adaptive adjustments to synchronize service data, achieving an optimal balance between data consistency and resource consumption. If the adjustment is successful, information visualization and correlation mapping are performed after the adjustment; otherwise, the process proceeds directly. A supply chain information penetration and risk quantification module is used to obtain information penetration and risk parameters from the information visualization and correlation mapping process. This quantifies the manageability of key dynamic information across the multi-level supply chain for the purchasing party, resulting in supply chain information penetration and risk assessment. A risk intelligent optimization and collaborative response module determines whether to implement information penetration and risk intelligent optimization based on the supply chain information penetration and risk assessment. This optimizes the allocation of information management resources, achieving an adaptive balance between the accuracy of supply chain risk control and operational efficiency. If the adjustment is successful, a supply chain collaboration phase is performed after the adjustment; otherwise, the process proceeds directly.

[0057] In this embodiment, a closed-loop intelligent management and control system, encompassing data governance and risk collaboration, is constructed, enabling dynamic optimization and adaptive resource allocation in supply chain management. Specifically, a reliable data foundation is first established through a multi-source data acquisition and synchronization quality quantification module, ensuring that the consistency and timeliness of core business data can be accurately measured. Subsequently, the data synchronization adaptive control module intelligently adjusts synchronization strategies based on the aforementioned quality indicators, proactively optimizing system resource consumption while ensuring data quality. Based on high-quality synchronized data, the supply chain information penetration and risk quantification module visualizes and quantifies the manageability and potential risks of key information in the multi-level supply chain, transforming ambiguous supply chain states into precise risk indicators. Finally, the risk intelligent optimization and collaborative response module dynamically adjusts monitoring resource allocation based on quantified risks and automatically triggers cross-departmental collaborative response processes. These four modules, progressing layer by layer and providing closed-loop feedback, enable the entire system to continuously achieve a dynamic balance between data consistency, resource efficiency, risk control, and operational response, ultimately achieving a systematic improvement in supply chain resilience, transparency, and decision-making agility.

[0058] The various features and processes described above can be used independently of each other or can be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure. Furthermore, certain method or process blocks may be omitted in some embodiments. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, the described blocks or states may be performed in an order different from the order specifically disclosed, or multiple blocks or states may be combined in a single block or state. Example blocks or states may be performed serially, in parallel, or in some other manner. Blocks or states may be added to or removed from the disclosed example embodiments. The exemplary systems and components described herein may be configured differently from those described. For example, elements may be added to, removed from, or rearranged compared to the disclosed example embodiments.

[0059] The various operations of the example methods described herein can be performed at least in part by an algorithm. This algorithm can be contained in program code or instructions stored in memory (e.g., the aforementioned non-transitory computer-readable storage medium). Such an algorithm may include a machine learning algorithm. In some embodiments, the machine learning algorithm may not be explicitly programmed into the computer to perform the function, but can learn from training data to create a predictive model that performs the function.

[0060] The various operations of the example methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0061] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, one or more processors can also be operated to support the performance of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors), where these operations are accessible via a network (e.g., the Internet) and via one or more suitable interfaces (e.g., application programming interfaces (APIs)).

[0062] The performance of certain operations can be distributed across processors, residing not only within a single machine but also deployed across multiple machines. In some example embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., within a home environment, office environment, or server cluster). In other example embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0063] In this specification, multiple instances may implement components, operations, or structures described as single instances. Although individual operations of one or more methods are shown and described as separate operations, one or more of the separate operations may be performed simultaneously and do not need to be performed in the order shown. Structures and functions presented as separate components in the example configuration may be implemented as composite structures or components. Similarly, structures and functions presented as single components may be implemented as separate components. These and other variations, modifications, additions, and improvements fall within the scope of this document.

[0064] While an overview of the subject matter has been described with reference to specific example embodiments, various modifications and changes can be made to these embodiments without departing from the broader scope of embodiments of this disclosure. Such embodiments of the subject matter are referred to herein, individually or collectively, by the term "invention," and are used for convenience only and are not intended to limit the scope of this application to any single disclosure or concept, should more than one disclosure or concept be disclosed in fact.

[0065] The embodiments described herein have been described in sufficient detail to enable those skilled in the art to practice the disclosed teachings. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Therefore, the detailed description should not be construed as limiting, and the scope of the various embodiments is defined only by the appended claims and the full scope of their equivalents.

Claims

1. A product information management method based on big data analysis, characterized in that, Includes the following steps: S101, Establish a multi-source commodity information collection channel to collect multi-dimensional information throughout the entire product lifecycle, obtain synchronous and accurate parameters, and quantify the consistency and timeliness of core supplier business data between the purchaser and the supplier based on the synchronous and accurate parameters, thereby obtaining the synchronization accuracy and timeliness of supplier business data. S102, determine whether to perform adaptive adjustment of synchronization accuracy and timeliness based on the synchronization accuracy and timeliness of supplier business data, so as to achieve adaptive adjustment of supplier business data synchronization and thus achieve the optimal balance between data consistency and resource consumption; if yes, then perform information visualization and association mapping after adjustment; if no, then directly perform information visualization and association mapping. S103, Obtain information penetration and risk parameters in the information visualization and correlation mapping process, which are used to quantify the manageability of key dynamic information of the multi-level supply chain by the purchaser, and obtain supply chain information penetration and risk. S104. Based on the supply chain information penetration and risk assessment, determine whether to perform information penetration and risk intelligent adjustment, optimize the allocation of information management resources, and achieve an adaptive balance between the accuracy of supply chain risk prevention and control and operational efficiency. If yes, then proceed with the supply chain collaboration process after adjustment; otherwise, proceed with the supply chain collaboration process directly.

2. The product information management method based on big data analysis as described in claim 1, characterized in that, The specific steps to obtain the accuracy and timeliness of supplier business data synchronization are as follows: The synchronization accuracy parameters include the average time for resolving information anomaly closed loops, the average latency for order status synchronization, and the frequency of synchronization task execution. The average duration impact value is obtained by correcting the ratio of the average duration benchmark value and the average duration of information anomaly closed-loop solution through the average duration compensation factor. The average delay impact value is obtained by correcting the ratio of the average delay baseline value to the average delay of order status synchronization through the average delay compensation factor. The ratio of the execution frequency of synchronous tasks to the baseline value of the execution frequency is corrected by the execution frequency compensation factor to obtain the execution frequency impact value. By coupling the impact values ​​of average duration, average latency, and execution frequency, the accuracy and timeliness of supplier business data synchronization can be obtained. The specific steps for determining whether to perform adaptive adjustment of synchronization accuracy and timeliness are as follows: If the accuracy and timeliness of the supplier's business data synchronization are greater than or equal to the synchronization accuracy benchmark, then intelligent optimization of data synchronization accuracy will not be performed; otherwise, dual threshold-trend-aware adaptive polling control and latency adaptive compensation control will be performed.

3. The product information management method based on big data analysis as described in claim 2, characterized in that, The specific steps for performing dual-threshold-trend-aware adaptive polling control are as follows: Within each preset monitoring period, the response latency of each poll is collected and recorded in real time. If the response latency is greater than the preset response baseline value, the poll is determined to be an abnormal poll, and the number of abnormal polls within the current preset monitoring period is counted. At the end of the current preset monitoring period, the result of the analysis of the number of abnormal polling times in the current preset monitoring period and the proportion of the current preset monitoring period is recorded as the periodic polling abnormality rate. If the periodic polling abnormality rate is less than or equal to the lower limit of the abnormality rate benchmark, it is judged as a normal state level. If the periodic polling anomaly rate is within the anomaly rate benchmark range, it is determined to be a moderate anomaly state level. The anomaly rate benchmark range refers to the open interval formed by the lower limit of the anomaly rate benchmark and the upper limit of the anomaly rate benchmark. If the periodic polling anomaly rate is greater than or equal to the upper limit of the anomaly rate benchmark, it is judged as a severe anomaly state. Based on the status level and the accuracy and timeliness of the synchronization with supplier business data, a preset level-number mapping table is queried to output the basic number of polling cycles for the next monitoring cycle. The preset level-number mapping table is configured such that the higher the status level, the lower the basic number of polling cycles, so as to achieve polling frequency attenuation under abnormal pressure.

4. The product information management method based on big data analysis as described in claim 3, characterized in that, The implementation of dual-threshold-trend-aware adaptive polling control also includes: Obtain the anomaly rate sequence of historical monitoring periods, calculate the first-order trend value of the sequence, and if the first-order trend value is greater than or equal to the positive trend threshold, input the current first-order trend value into the preset level-number mapping table and output the target basic number of polling for the next monitoring period. The target basic number of polling is a negative increase correction to further reduce the number of polling. If the first-order trend value is within the trend threshold range, then the current basic polling count is maintained. The trend threshold range represents the open interval formed by the negative trend threshold and the positive trend threshold. If the first-order trend value is less than or equal to the negative trend threshold, the current first-order trend value is input into the preset level-number mapping table, and the target basic polling number for the next monitoring period is output. The target basic polling number is a positive correction to increase the number of polling.

5. The product information management method based on big data analysis as described in claim 2, characterized in that, The specific process of the time delay adaptive compensation control is as follows: If the transmission and reception delay of a status event is less than or equal to the baseline value of the transmission and reception delay, then the adaptive delay compensation adjustment will not be performed. If the state event transmission and reception delay is greater than the transmission and reception delay baseline value, then based on the current state event transmission and reception delay and the supplier business data synchronization accuracy and timeliness, a preset delay-correction synchronization delay mapping table is queried. The query logic of the preset delay-correction synchronization delay mapping table is configured as follows: when the state event transmission and reception delay increases and the supplier business data synchronization accuracy and timeliness are less than the synchronization accuracy baseline value, an instruction to increase the supplier business data correction synchronization delay is output. The instruction to increase the supplier business data correction synchronization delay means inputting the current state event transmission and reception delay and the supplier business data synchronization accuracy and timeliness into the preset delay-correction synchronization delay mapping table, outputting the correction synchronization delay increase value, and coupling the current correction synchronization delay increase value with the supplier business data correction synchronization delay to obtain the target supplier business data correction synchronization delay.

6. The product information management method based on big data analysis as described in claim 1, characterized in that, The information penetration and risk parameters include synchronous, accurate and valid values, average early warning time for supply disruption risks, and average risk response duration. The synchronous accurate effective value is obtained by correcting the ratio of synchronous accurate effective value to synchronous accurate effective benchmark value through synchronous accurate and effective compensation factor. The impact value of early warning is obtained by correcting the ratio of the average early warning time to the early warning benchmark value of supply disruption risk through an early warning compensation factor. The average duration impact value is obtained by correcting the ratio of the average duration benchmark value to the average risk response duration using an average duration compensation factor. By coupling the synchronous, accurate, and effective impact value, the early warning impact value, and the average duration impact value, we can obtain supply chain information penetration and risk assessment. The specific process for determining whether to perform information penetration and intelligent risk adjustment is as follows: If the supply chain information penetration and risk level are greater than or equal to the penetration and risk benchmark values, then information penetration and risk intelligent optimization will not be performed; otherwise, then the inverse granularity dynamic mapping control driven by the activity level and the contingency plan duration asymmetric control driven by the two-dimensional threshold will be performed.

7. The product information management method based on big data analysis as described in claim 6, characterized in that, The specific process of implementing the inverse granularity dynamic mapping control driven by the activity level is as follows: The dynamic update frequency of data fields is divided into multiple activity levels, including high activity level, medium activity level, and low activity level. Based on multiple activity levels, supply chain information penetration and risk, and supply chain information penetration granularity, an activity level-penetration granularity mapping relationship is established. The activity level-penetration granularity mapping relationship maps each activity level to a preset information penetration granularity level. The activity level-penetration granularity mapping relationship is configured such that: the higher the activity level of the data field, the coarser the assigned information penetration granularity level; conversely, the lower the activity level of the data field, the finer the assigned information penetration granularity level. When a user requests a penetration query for a target business entity, the real-time recommendation penetration granularity of that entity is obtained, and then compared with the baseline penetration granularity. Specifically: If the real-time recommendation penetration granularity is greater than or equal to the baseline penetration granularity, the current recommendation penetration granularity will be set to the real-time recommendation penetration granularity to prevent frequent page refreshes or data overload caused by high-frequency updates. If the real-time recommended penetration granularity is smaller than the baseline penetration granularity, the current recommended penetration granularity will be set as the baseline penetration granularity to extract and assemble information from the data source and generate the final penetration query results.

8. The product information management method based on big data analysis as described in claim 6, characterized in that, The specific steps of the dual-dimensional threshold-driven asymmetric control of the plan duration are as follows: The system monitors the accessed real-time event stream and performs feature analysis on events within a preset analysis time window. Based on the number of newly generated transition events actively driven and the number of events generated by internal state self-evolution within the preset analysis time window, the system obtains the driving update ratio of the current analysis time window. An update mode-processing strategy mapping table is established, which associates each event update mode with a set of preset contingency plan processing time limit parameters. The time limit parameters include at least: the maximum calculation time for contingency plan matching and the maximum waiting time for contingency plan details to be retrieved.

9. The product information management method based on big data analysis as described in claim 8, characterized in that, The dual-dimensional threshold-driven asymmetric control of the plan duration also includes: If the driver update ratio is less than or equal to the lower limit of the update ratio reference, it is determined to be an autonomous evolution-dominant mode. The current driver update ratio, supply chain information penetration and risk input are entered into the update mode-processing strategy mapping table, and the maximum calculation time for contingency plan matching is output to extend the calculation and recall time window, improve decision quality and the accuracy of contingency plan matching. If the proportion of driver updates is greater than or equal to the upper limit of update proportion reference, it is determined to be an active driver-dominated mode. The current proportion of driver updates, supply chain information penetration and risk input are entered into the update mode-processing strategy mapping table, and the contingency plan details are output to retrieve the maximum waiting time, so as to shorten the decision-making and retrieval time limit, thereby prioritizing response timeliness and high concurrency throughput, and ensuring that feasible contingency plans are output within the critical time window. If the update ratio is within the update ratio reference range, it is determined to be a hybrid update mode, and the current plan matching and call-out duration are maintained. The update ratio reference range refers to the open interval formed by the lower limit of the update ratio reference and the upper limit of the update ratio reference.

10. A system applying the commodity information management method based on big data analysis as described in any one of claims 1-9, characterized in that, include: Multi-source data acquisition and synchronization quality quantification module, data synchronization adaptive control module, supply chain information penetration and risk quantification module, and risk intelligent optimization and collaborative response module; The multi-source data acquisition and synchronization quality quantification module is used to build a multi-source commodity information acquisition channel, collect multi-dimensional information of the entire commodity life cycle, obtain synchronization accuracy parameters, and quantify the consistency and timeliness of core supplier business data between the purchaser and the supplier based on the synchronization accuracy parameters, thereby obtaining the synchronization accuracy and timeliness of supplier business data. The data synchronization adaptive control module is used to determine whether to perform adaptive control of synchronization accuracy and timeliness based on the accuracy and timeliness of supplier business data synchronization, so as to achieve adaptive adjustment of supplier business data synchronization and thus achieve the optimal balance between data consistency and resource consumption; if yes, then information visualization and association mapping are performed after control; if no, then information visualization and association mapping are performed directly. The supply chain information penetration and risk quantification module is used to obtain information penetration and risk parameters in the information visualization and correlation mapping process, and to quantify the manageability of key dynamic information of the multi-level supply chain by the purchaser, thereby obtaining supply chain information penetration and risk. The aforementioned risk intelligent optimization and collaborative response module is used to determine whether to perform information penetration and risk intelligent optimization based on supply chain information penetration and risk assessment, optimize the allocation of information management resources, and achieve an adaptive balance between the accuracy of supply chain risk prevention and control and operational efficiency. If so, the supply chain collaboration process is carried out after adjustment; otherwise, the supply chain collaboration process is carried out directly.

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