Supplier price management system and method based on big data analysis

Abstract

The invention belongs to the technical field of big data analysis, and discloses a supplier price management system and method based on big data analysis, and the method comprises the steps: collecting core data and auxiliary data, and constructing a multi-dimensional data cube; dynamically calculating a grid availability index, forming a dynamic space-time grid reference library, and synchronously generating a supply chain topological graph; receiving and quantifying a business target instruction, and selecting a path through a rule engine; generating purchase strategy suggestions with confidence rating and a dynamic priority supplier list; the authority is dynamically adjusted through an ABAC model, an intelligent contract engine is triggered, and an order or bargaining invitation is generated; parallel approval is carried out through a workflow engine, and a purchase order set and a permission change log are generated; receiving a real-time data stream of the Internet of Things and a historical case library, and triggering a disposal logic; and matching similar historical cases, pushing a historical optimal strategy, synchronously updating a weight rule, a data cube structure and a supplier credit sub-threshold, and forming full-link closed-loop iteration.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis. More specifically, the present invention relates to a supplier price management system and method based on big data analysis. Background Art

[0002] With the rapid development of big data analysis and supply chain management technologies, the intelligentization demand for supplier price management systems is becoming increasingly urgent. Enterprises urgently need to optimize procurement decisions by integrating multi-source data (such as real-time supplier production capacity, regional policies, and logistics dynamics), and at the same time cope with risks and uncertainties in the dynamic market environment. However, existing systems mostly rely on traditional data management methods, lack the ability to deeply analyze complex supply chain scenarios, and are difficult to achieve global and adaptive procurement strategy optimization.

[0003] Existing supplier price management systems have a series of interrelated technical defects, resulting in insufficient reliability and efficiency in the dynamic supply chain environment. First, the system's utilization of data is limited to isolated procurement records, and it fails to effectively integrate real-time supplier data (such as inventory and production capacity), external auxiliary data (such as regional policies and competitor prices), and historical case libraries, resulting in a lack of a global perspective in decision-making. Second, key processes (such as route selection and supplier screening) rely on manual price comparison and experience judgment, which are inefficient and prone to deviation. At the same time, risk response (such as delivery delays and quality defects) lacks an automated handling logic and relies on post-event manual intervention. In addition, route strategy updates are lagging, unable to respond in real time to supply chain fluctuations (such as regional power outages and logistics disruptions), and supplier credit assessment and permission management lack a dynamic mechanism and cannot adapt to the permission adjustment requirements in emergency scenarios. Moreover, existing systems lack the ability of closed-loop optimization. After event handling, no knowledge precipitation is formed, and historical cases are not combined with AI models, resulting in strategy optimization relying on manual experience and unable to push the optimal solution through semantic matching. These problems jointly restrict the decision-making resilience and intelligent level of the system and are difficult to meet the complex requirements of modern supply chains. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art and achieve the above object, the present invention provides the following technical solutions: A supplier price management system based on big data analysis, including: A multi-dimensional data cube construction unit: Collect real-time core data of suppliers through a standardized interface, obtain auxiliary data through a third-party API, and construct a multi-dimensional data cube; Divide the supplier cluster according to geographical grids, dynamically calculate the grid availability index, form a dynamic spatio-temporal grid reference library, and synchronously generate a supply chain topology map; Multi - path Decision - making Collaboration Unit: Based on the data cube and the dynamic spatio - temporal grid benchmark library, it receives and quantifies business target instructions, selects paths through a rule engine; adopts a dynamic weight voting model, and combines the path selection results to output procurement strategy suggestions with confidence ratings and a dynamic priority supplier list; Smart Contract Execution and Permission Network Unit: Based on the dynamic priority supplier list and the supply chain topology map, combined with the ERP contract status and supplier credit scores, it dynamically adjusts permissions through the ABAC model and triggers the smart contract engine to generate orders or bargaining invitations; through parallel approval by the workflow engine, it generates a set of purchase orders and a permission change log; Closed - loop Autonomous Optimization and Resilience Enhancement Unit: Receives real - time IoT data streams and historical case libraries, triggers disposal logic through a multi - modal anomaly response engine; based on the BERT model, matches similar historical cases to push the historical optimal strategy, and synchronously updates weight rules, the data cube structure, and the supplier credit score threshold to form a full - link closed - loop iteration.

[0005] Furthermore, the construction method of the multi - dimensional data cube includes: Collects the real - time core data of suppliers through a standardized interface, including quotes, inventory levels, actual production capacity, and contract status; Obtains auxiliary data in the region through a third - party API, including regional power rationing policies, competitor prices, design production capacity, and logistics interruption events; Cleans and unifies the collected core data and auxiliary data; Defines the dimensions and metrics of the data cube. The dimensions include time dimension, supplier dimension, administrative region dimension, product dimension, event dimension, and contract dimension; the metrics include average quote price, inventory availability rate, production capacity utilization rate, regional risk coefficient, and contract default risk index; Calculates the metrics within different administrative regions based on each administrative region dimension, including: Extracts the unit price, discount rules, and currency unit of the quote data in the core data, and calculates the average quote price; Extracts the available inventory and the highest inventory threshold of the inventory level in the core data, and calculates the inventory availability rate through ratio calculation; Takes the due date and default terms of the contract status, and calculates the contract default risk index; According to the processed auxiliary data, sets a time window, and calculates the ratio of the difference between the maximum price and the minimum price of competitor prices in the past time window to the average value of competitor prices as the competitor price fluctuation range with the current moment as the benchmark; Extracts the number of times, duration, and impact range of the regional power rationing policy in the past time window in the auxiliary data, normalizes them respectively, and then sums them up with weights to obtain the power rationing risk value; Take the price fluctuation range of competing products, the power rationing risk value, and the number of logistics interruption events within a past time window as coefficient indicators for calculating the risk coefficient of the administrative region. Normalize each coefficient indicator, assign weights to each coefficient indicator through the entropy weight method, and then calculate the regional risk coefficient through weighted summation; According to the defined dimensions and metrics, construct a dimension table and a fact table through a star model, integrate foreign keys to form a cube structure, which serves as a data cube.

[0006] Furthermore, the construction method of the dynamic spatio-temporal grid benchmark library includes: Based on the dimensions of the data cube, conduct business associations for the supplier dimension, product dimension, and event dimension according to product types and event types, and divide different dynamic regions according to the business associations, which serve as different supplier clusters; Extract all the data covered by the dynamic region from the data cube, recalculate the regional risk coefficient corresponding to the dynamic region, which serves as the dynamic region risk coefficient; Take the ratio of the total actual output value to the total designed production capacity of all enterprises within the dynamic region as the production capacity utilization rate of the dynamic region; Extract the performance status in all contract statuses within the dynamic region, and calculate the contract performance rate within the dynamic region; Based on the production capacity utilization rate, inventory availability rate, and contract performance rate within the dynamic region, assign weights to the production capacity utilization rate, inventory availability rate, and contract performance rate respectively, and then combine with the dynamic region risk coefficient to calculate the grid availability index; Integrate the grid availability index and the dynamic region risk coefficient of the dynamic region as the dynamic spatio-temporal grid benchmark of the dynamic region; integrate the dynamic spatio-temporal grid benchmarks of all dynamic regions to generate a dynamic spatio-temporal grid benchmark library.

[0007] Furthermore, the construction method of the supply chain topology map includes: According to the dimensions of the data cube, take all suppliers, products, dynamic regions, and events as nodes, and add corresponding attributes to each node; bind the supplier node to the dynamic region node, and if the dynamic region risk coefficient changes, synchronously trigger the update of the grid availability index of the supplier; Extract all supply relationships, logistics paths, contract associations, and event impacts in the data cube as edges, and assign edge weights to each type of edge; Take the relationship between the supplier and the product as the supply relationship, calculate the production capacity utilization rate of the supplier as the supplier production capacity utilization rate, and take the product of it and the corresponding dynamic region risk coefficient as the basic weight of the corresponding edge; Take the transportation route between two dynamic regions as the logistics path, obtain the corresponding transportation cost, and take the product of the dynamic region risk coefficient and the transportation cost as the basic weight of the corresponding edge; Take the relationship between the supplier and the contract as the contract association, and take the contract default risk index as the basic weight of the corresponding edge; Take the relationship between the event and the affected region as the event impact. Calculate the industrial chain dependence degree of the dynamic region through the TFR formula, and perform weighted summation of the dynamic region risk coefficient and the industrial chain dependence degree according to a preset ratio to obtain the dynamic region vulnerability; take the product of the influence scope of the event and the dynamic region vulnerability as the basic weight of the corresponding edge; Normalize the basic weights of all edges so that the sum of all basic weights is 1; Multiply the normalized basic weight by the global adjustment coefficient of the dynamic region risk coefficient to obtain the edge weight; Construct a supply chain topology map according to the nodes and edges.

[0008] Furthermore, the definition methods for the rule engine to select paths include: Decompose the business objective into cost path indicators, market path indicators, and risk path indicators through the KPI decomposition of the SMART principle; The cost path indicator is to calculate the weighted sum of the transportation cost and the inventory cost to obtain the total cost of each path as the quantification of the cost path; Query all paths from the dynamic region where the starting point is located to the dynamic region where the ending point is located through the supply chain topology map, and take the sum of the edge weights of the logistics paths of the paths as the transportation cost of each path; Obtain the total product of the available inventory at the inventory points passed by the path and the unit holding cost through the data cube as the inventory cost of the path; The market path indicator is to calculate the weighted sum of the delivery on-time rate and the market demand matching degree to obtain the market value as the quantification of the market path; Calculate the proportion of the number of on-time delivery orders of the path based on historical order data as the delivery on-time rate; Calculate the ratio of the market demand in the dynamic region where it is located to the total actual production capacity of the dynamic region to obtain the market demand matching degree; The risk path indicator is to calculate the path risk value of each path as the quantification of the risk path; Calculate the reliability index of the path for each supplier node or dynamic region node passed by the path through the fault tree analysis method as the path dependence degree of the path on the corresponding node; Obtain the dynamic region risk coefficient of each dynamic region passed by the path through the dynamic spatio-temporal grid benchmark library; Calculate the sum of the products of the dynamic region risk coefficients and the path dependence degrees to obtain the basic risk value of the path, and multiply the basic risk value by a preset risk weight to obtain the path risk value; According to the quantified cost path metrics, market path metrics, and risk path metrics, define the path selection rules of the rule engine as follows: The cost path is defined as the path with the lowest total cost; The market path is defined as the path with the highest market value; The risk path is defined as the path with the lowest path risk value.

[0009] Furthermore, the method for obtaining the procurement strategy recommendation with confidence rating and the dynamic priority supplier list includes: According to the selection results of the cost path, market path, and risk path, normalize the total cost, market value, and path risk value of the path respectively to obtain the cost score, market score, and risk score as the inputs of the dynamic weight voting model, and output the procurement strategy recommendation with confidence rating and the dynamic priority supplier list; Define the dynamic weight allocation rule as dynamically adjusting the path weight by the edge weight adjustment coefficient, and the total path weight is 1; Define the dynamic adjustment mechanism as follows: if the dynamic region risk coefficient rises and is greater than the preset dynamic region risk coefficient threshold, then increase the risk weight by a preset ratio; if an emergency order instruction is received, then increase the market weight by a preset ratio and correspondingly decrease the cost weight; Combine the cost score, market score, and risk score with the corresponding weights, and obtain the comprehensive score of the path through weighted summation; Define the generation logic of the confidence rating as: divide the confidence rating into high confidence, medium confidence, and low confidence; Set the scoring interval threshold, as well as the cost interval threshold and risk interval threshold; Based on the comprehensive score, as well as the cost score and risk score, determine the confidence level of the path through the scoring interval threshold, as well as the cost interval threshold and risk interval threshold; For each supplier node in the path, extract the performance status of all contracts corresponding to the supplier node, and take the ratio of the number of completed performances to the total number of failed performances as the supplier contract performance rate; Based on the edge weights of the supply relationships in the supply chain topology map, extract the path dependence degree of the path on the supplier node, and perform weighted summation with the available inventory of the supplier node and the supplier contract performance rate to obtain the supplier priority score of each supplier; Generate the dynamic priority supplier list in descending order of the supplier priority score of each supplier; Set the risk area threshold. If the dynamic area risk coefficient of the dynamic area is less than the risk area threshold, it is determined as a low-risk area. If the dynamic area risk coefficient is greater than or equal to the risk area threshold, it is determined as a high-risk area; Define the dynamic adjustment logic of the priority sorting rule of the dynamic priority supplier list as follows: In the case of emergency orders, preferentially select the path supplier with the highest market value. If the path passes through a high-risk area, preferentially select suppliers in the low-risk area.

[0010] Furthermore, the implementation method of the smart contract for generating orders or bargaining invitations includes: Through the interface between the supply chain topology map and the ERP system, obtain the ERP contract status, credit score, and level of the supplier; Obtain the grid availability index of the dynamic area where the supplier is located from the dynamic spatio-temporal grid benchmark library; Define the ABAC model including attributes and policy rules. Define the attributes including user attributes, environmental attributes, and operation attributes; User attributes include supplier level and credit score; Environmental attributes include the current status and the grid availability index; Operation attributes include the request type; The current status includes the normal status and the emergency status; Define policy rules based on attributes, including defining access permissions for different levels of suppliers in different current states; According to the defined ABAC model, generate an access control list for the supplier through the ABAC module of the smart contract engine and record the permission change log; Define the trigger rules of the smart contract engine including the regular procurement process and the reverse link contract; Define the trigger condition of the regular procurement process as when the ERP contract status is valid and the supplier is in the dynamic priority supplier list; Define the execution steps as selecting the supplier with the highest priority in the dynamic priority supplier list, calling the ERP interface of the smart contract engine, and automatically generating an order based on the preset historical order template; Define the trigger condition of the reverse link contract as when the grid availability index of the dynamic area where the supplier is located is lower than the preset grid availability index threshold; Define the execution steps as sending a bargaining invitation to the secondary supplier according to the preset historical quotation template, including parameters and constraints; The parameters are the demand quantity, the dynamic area risk coefficient, and the order urgency. The constraint is that the bargaining price needs to be lower than a preset percentage of the first-level supplier's quotation.

[0011] Furthermore, the generation method of the purchase order set and the permission change log includes: Based on the bargaining invitation sent to the secondary suppliers, when the secondary suppliers accept the bargaining invitation or the orders in the normal state require manual confirmation, start the workflow engine for parallel approval; Set approval nodes, including the Purchasing Department, the Finance Department, and the Legal Department, and perform parallel approval operations according to the approval nodes through the workflow engine; Define the parallel approval rule that orders in the normal state only require approval by the Purchasing Department, and orders in the emergency state or requiring bargaining require parallel approval by the Purchasing Department, the Finance Department, and the Legal Department; When all approvals are passed, update the ERP contract status through the smart contract engine, call the historical order template, and generate the final purchase order set in combination with the approval results; When the emergency state is lifted, through the ABAC module of the smart contract engine, revoke the temporary permissions of the secondary suppliers, update the access control list of the suppliers, restore the normal state permission rules, synchronize and record the permission change time, the approval process, and the order execution results, and generate a permission change log.

[0012] Furthermore, the method of synchronously updating the weight rule and the data cube structure to form a full-link closed-loop iteration includes: Receive the real-time data stream of the Internet of Things, including the logistics track and the quality inspection report; and read the processing records and results of past events in the historical case library; Define the handling logics as delivery delay, quality defect, and risk event. For delivery delay, if the delay time is greater than the preset delay time threshold, calculate the liquidated damages and deduct the supplier's credit score; for quality defect, if the product defect rate is greater than the preset defect rate threshold, trigger the supplier switching mechanism; for risk event, identify the unplanned risk through AI anomaly detection technology; According to the handling logics triggered by the multimodal anomaly response engine, use the BERT model to analyze the historical case library, match the similar historical cases of the current event handling logic, and then push the historical optimal strategy; According to the impact of the current event, update the risk weight, cost weight, and risk weight in the dynamic weight voting model according to the principle of resilience improvement, and recalculate the path selection rule; According to the type of the current event, if there is no corresponding type in the data cube dimension, expand the data cube dimension; and update the data cube fields; synchronously update the supplier's credit score and the corresponding threshold parameters; store the current event and the handling results in the historical case library to form a closed-loop iteration.

[0013] A supplier price management method based on big data analysis, which is implemented based on the above-mentioned supplier price management system based on big data analysis, includes: S1: Collect the real-time core data of suppliers through a standardized interface, and obtain auxiliary data through a third-party API to construct a multi-dimensional data cube; divide the supplier clusters according to geographical grids, dynamically calculate the grid availability index, form a dynamic spatio-temporal grid benchmark library, and simultaneously generate a supply chain topology map; S2: Based on the data cube and the dynamic spatio-temporal grid benchmark library, receive and quantify business objective instructions, and select paths through a rule engine; adopt a dynamic weight voting model, and combine the path selection results to output procurement strategy suggestions with confidence ratings and a list of suppliers with dynamic priorities; S3: Based on the list of suppliers with dynamic priorities and the supply chain topology map, combine the ERP contract status and the supplier credit score, dynamically adjust permissions through the ABAC model, and trigger the smart contract engine to generate orders or bargaining invitations; parallelly approve through the workflow engine to generate a set of purchase orders and a permission change log; S4: Receive the real-time data stream of the Internet of Things and the historical case library, and trigger the handling logic through a multi-modal anomaly response engine; match similar historical cases based on the BERT model to push the historical optimal strategy, and simultaneously update the weight rules, the data cube structure, and the supplier credit score threshold to form a full-link closed-loop iteration.

[0014] The technical effects and advantages of the supplier price management system and method based on big data analysis of the present invention: The present invention integrates the core data of suppliers (such as quotations, inventories), external data (such as regional policies, competitor prices), and the historical case library, constructs a multi-dimensional cube and a dynamic spatio-temporal grid benchmark library, and evaluates the regional risks and path dependencies in real time to solve the problem of lag in static analysis of traditional systems; then, quantifies the path risks through fault tree analysis, dynamically adjusts the weights (such as increasing the market weight in the emergency state), generates procurement strategies with confidence ratings, and preferentially matches suppliers with low risks, and the decision-making dimension is more comprehensive, which is superior to traditional manual price comparison; then, based on the ABAC model and smart contracts, automatically trigger order generation, bargaining with secondary suppliers, and dynamic permission adjustment (such as parallel approval by three departments for emergency orders), reducing manual intervention; finally, respond to logistics and quality inspection anomalies in real time (such as automatically deducting credit scores, switching suppliers), push the optimal strategy by matching historical cases based on the BERT model, and dynamically expand the data dimension to form a "data - analysis - decision - optimization" closed loop, continuously improving the system's self-adaptive ability; the present invention realizes the automation, intelligence, and self-evolution of supplier price management, improves the procurement efficiency, cost control, and risk resistance level, and ensures the stability of the supply chain. Brief Description of the Drawings

[0015] Figure 1 It is a schematic diagram of the supplier price management system based on big data analysis of the present invention; Figure 2Schematic flow diagram of the dynamic weight voting model of the supplier price management system based on big data analysis of the present invention; Figure 3 Schematic diagram of the supplier price management method based on big data analysis of the present invention. Specific implementation manner

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0017] Embodiment 1

[0018] Please refer to Figure 1 and Figure 2 As shown, the supplier price management system based on big data analysis in this embodiment includes: Multi-dimensional data cube construction unit: Collect real-time core data of suppliers through a standardized interface, and obtain auxiliary data through a third-party API to construct a multi-dimensional data cube; Divide the supplier cluster according to geographical grids, dynamically calculate the grid availability index, form a dynamic spatio-temporal grid reference library, and synchronously generate a supply chain topology map; Multi-path decision-making collaboration unit: Based on the data cube and the dynamic spatio-temporal grid reference library, receive and quantify business target instructions, and select paths through a rule engine; Adopt a dynamic weight voting model, and combine the path selection results to output procurement strategy suggestions with confidence ratings and a dynamic priority supplier list; Intelligent contract execution and permission network unit: Based on the dynamic priority supplier list and the supply chain topology map, combine the ERP contract status and the supplier credit score, dynamically adjust permissions through the ABAC model, and trigger the intelligent contract engine to generate an order or a bargaining invitation; Parallel approval through the workflow engine to generate a set of purchase orders and a permission change log; Closed-loop autonomous optimization and resilience enhancement unit: Receive real-time IoT data streams and historical case libraries, and trigger disposal logic through a multi-modal anomaly response engine; Push the historical optimal strategy based on the BERT model to match similar historical cases, and synchronously update the weight rules, the data cube structure, and the supplier credit score threshold to form a full-link closed-loop iteration.

[0019] Collect the real-time core data of suppliers through standardized interfaces (such as ERP systems, Internet of Things sensors, supplier contract systems), including quotations (including unit price, quantity, currency unit, discount rules, effective date. For example, the quotation of a certain electronic component provided by Supplier A may be "$2.50 USD per thousand", and some quotations also include volume discounts or special offers based on long-term contracts), inventory levels (including real-time inventory quantity, available inventory, inventory in transit, minimum / maximum inventory thresholds, representing the actual storage quantity of a certain product in the warehouse at a specific time point, usually expressed in quantity (such as number of pieces, tons)), actual production capacity, and contract status (including contract number, signatory parties, effective date, expiration date, payment terms, performance status, default terms, covering the validity, term, rights and obligations of both parties of the contract); Obtain the auxiliary data of the location through third-party APIs (such as government policy databases, competitor price monitoring APIs, logistics event monitoring systems), including regional power rationing policies (including policy name, effective date, duration, scope of influence (i.e., the proportion of affected areas, such as provincial vs. county-level), competitor prices (including competitor product models, prices, promotional activities, historical price trends, referring to the price levels set by competitors in the market for similar products or services. These data can help enterprises understand market pricing trends and formulate their own pricing strategies. In addition to direct price information, some data also includes promotional activities and discount information), designed production capacity, and logistics interruption events (including event type, affected area, duration, recovery status, recording various abnormal situations in the logistics process, such as road closures caused by natural disasters, port strikes, and transportation delays caused by traffic accidents. This type of data is crucial for assessing supply chain risks and can help enterprises prepare countermeasures in advance to reduce losses); and adopt a hierarchical caching mechanism to update the core data in real time and update the auxiliary data regularly (such as obtaining it once every 7 days and caching it for 7 days after obtaining it once); it should be noted that the auxiliary data is low-frequency data, relying on third-party APIs (such as government announcements, logistics platforms), and there is a certain probability of delay or data incompleteness, so it cannot be updated in real time and needs to be distinguished from the core data updated in real time. By regularly caching and updating, the real-time nature of the data can be ensured; For the collected core data and auxiliary data, perform data cleaning and unification, including cleaning outliers (such as mutated quotes), filling missing values (such as using industry averages through mean / difference methods for processing), removing duplicate data, aligning timestamps, and through the deployment of a lightweight adapter cluster (such as a Kafka-based message queue for real-time processing of core data and Redis cache for auxiliary data), unify the data format (such as core data is accessed through a real-time stream pipeline (Kafka), with fields including supplier ID, quote, inventory value, fulfillment rate, and auxiliary data is accessed through a batch loading channel (FTP / SFTP), with fields including regional code, policy level, average price of competing products), including converting different currencies (such as USD, CNY) to a unified unit (such as USD) through a real-time exchange rate API, and converting date fields (effective date, expiration date, event occurrence time) to a unified format (such as YYYY-MM-DD); Define the dimensions and measures of the data cube. The dimensions include the time dimension (the time attribute of the data, used to analyze trends, year (2025) → quarter (Q2) → month (April) → day (16th)), the supplier dimension (supplier attributes, used to evaluate supplier performance, supplier ID → supplier name → industry → contract status (effective / expired / default)), the administrative region dimension (geographical location, used to analyze regional risks and the impact of logistics disruptions, country (China) → province (Sichuan Province) → county (Chengdu City) → policy impact scope (provincial / county)), the product dimension (product attributes, used to analyze the correlation between quotes and inventory, product model → product category (such as chemical raw materials, electronic components) → product specification (such as PVC-A1 type)), the event dimension (external event attributes, used to calculate regional risks, event type (power outage, logistics disruption) → event severity (high / medium / low) → recovery status (not recovered / recovered)), and the contract dimension (key contract attributes, used to evaluate performance risks, contract number → signatory → payment terms (30% advance payment) → default clause (5% overdue fine per day)); The measures include average quote price, available inventory rate, capacity utilization rate, regional risk coefficient, and contract default risk index; Calculate the measures within different administrative regions based on each administrative region dimension, including: Extract the unit price, discount rule, and currency unit of the quote data from the core data, and calculate the average quote price; The calculation method is to extract the discount rate through the discount rule, calculate the unit price × (1 - discount rate), and then calculate the ratio of the obtained value to the currency unit conversion factor to obtain the average quote price; The currency unit conversion factor is the exchange rate conversion ratio between different currencies (for example, if the unified currency format is USD and the real-time conversion ratio between CNY and USD is 7.308, then 1 CNY divided by 7.308 equals 0.1368 USD); Extract the available inventory and the maximum inventory threshold of the inventory level in the core data, and calculate the inventory availability rate by ratio calculation; Take the expiration date and default clause of the contract status, and calculate the contract default risk index; The calculation method is to get the remaining days by subtracting the current date from the expiration date, and multiply the remaining days by the default fine rate in the default clause to get the contract default risk index; According to the processed auxiliary data, set a time window (such as 10 days, 30 days), and calculate the ratio of the difference between the maximum price and the minimum price of the competitor price in the past time window to the average value of the competitor price as the competitor price fluctuation range based on the current moment; Extract the number of times, the duration days, and the influence scope of the regional power rationing policy in the past time window from the auxiliary data, and perform normalization processing respectively (normalized to the range of 0 - 1 to eliminate the dimension difference), and then sum them with weights to get the power rationing risk value; Take the competitor price fluctuation range, the power rationing risk value, and the number of logistics interruption events in the past time window as the coefficient indicators for calculating the regional risk coefficient of the administrative region. Perform normalization processing on each coefficient indicator, and assign weights to each coefficient indicator through the entropy weight method, and then calculate the regional risk coefficient through weighted summation; Among them, the method of assigning weights to each coefficient indicator through the entropy weight method is as follows: Collect the historical data of each coefficient indicator (such as the regional risk event data in the past 3 years), calculate the information entropy value of each indicator through the information entropy formula of the entropy weight method. The lower the entropy value, the greater the difference of the indicator, and the higher the weight. Calculate the weight of each coefficient indicator according to the entropy value through the weight calculation formula of the entropy weight method; According to the defined dimensions and metrics, construct a dimension table for each dimension through the star model (such as the dimension table of the time dimension is year (2025) → quarter (Q2) → month (April) → day (16th)). Add the event type (such as power rationing, logistics interruption) to each dimension table, and associate the event dimension to different analysis scenarios (such as power rationing scenario, logistics interruption scenario) according to the event type, and add a primary key (such as TimeKey) and descriptive fields (such as country, supplier name) to each dimension table; Assign a unique ID to each dimension as the foreign key of each dimension table, integrate all foreign keys to construct a fact table, and associate the fact table with the dimension table through the foreign key. Then aggregate all metrics by time and region (that is, calculate the hierarchy in the way of time × region, such as the average price of the quarterly average quotation in the East China region, which is the average value of all quotation prices in a quarter (3 months) in the East China region) and add them to the fact table; Based on the dimension table and the fact table, combine dimensions and metrics into a cube structure, which serves as a data cube. For example: Cube[Time = 2021-04][Region = Sichuan][Supplier = Company A][Product = PVC-A1][Event = Power Outage][Contract = CT2025001], which contains metric values (such as average quoted price, risk coefficient); According to the dimensions of the data cube, conduct business associations for the supplier dimension, product dimension, and event dimension according to product types and event types, and divide different dynamic regions based on the business associations (for example, using the ArcGIS tool, divide the total region into different spatial units with GIS grids as the smallest units according to different regions of business associations), which serve as different supplier clusters (such as East China - Electronic Component Cluster, South China - Logistics Hub Area); It should be noted that the division of dynamic regions is updated in real time and can be aggregated into large regions (such as East China Industrial Cluster) or differentiated into small grids (such as the logistics hub in a certain city's high-tech zone) as needed; Extract all the data covered by the dynamic region from the data cube, and recalculate the regional risk coefficient corresponding to the dynamic region, which serves as the dynamic region risk coefficient; Take the ratio of the total actual output value to the total designed production capacity of all enterprises within the dynamic region as the production capacity utilization rate of the dynamic region; Extract the fulfillment status in all contract statuses within the dynamic region, and calculate the contract fulfillment rate within the dynamic region; A method for calculating the contract fulfillment rate is: through the fulfillment status of all contracts within the dynamic region, take the ratio of fulfillment completed to fulfillment failed as the contract fulfillment rate; Based on the production capacity utilization rate, inventory availability rate, and contract fulfillment rate within the dynamic region, assign weights to the production capacity utilization rate, inventory availability rate, and contract fulfillment rate respectively (such as setting weights according to industry standards or expert experience), multiply the production capacity utilization rate, inventory availability rate, and contract fulfillment rate by their corresponding weights respectively and then sum them up, and then divide by the total weight sum to obtain the weighted average value. Furthermore, multiply the weighted average value by the complementary value of the dynamic region risk coefficient to obtain the grid availability index; this grid availability index can reflect the resource status within the dynamic region; The relational expression is: ; Among them, represents the production capacity utilization rate, represents the weight of the production capacity utilization rate, represents the inventory availability rate, the weight of the inventory availability rate, represents the contract fulfillment rate, represents the weight of the contract fulfillment rate, represents the dynamic region risk coefficient; It should be noted that by integrating the capacity utilization rate, inventory availability rate, and contract fulfillment rate, the dynamic availability of the supply chain can be comprehensively reflected, avoiding the one-sidedness of a single indicator. Through weights, the indicator priorities can be adjusted for different business scenarios (for example, inventory weight is emphasized in the logistics hub area, and capacity weight is emphasized in the production area); Since the higher the dynamic regional risk coefficient (such as power rationing, logistics interruption), the lower the availability of the supply chain will inevitably be. Therefore, by multiplying the complementary value of the dynamic regional risk coefficient, the availability index of high-risk regions will be significantly reduced, intuitively reflecting the inhibitory effect of risks on the supply chain (for example, the availability in the power rationing area is directly discounted), and the dynamic regional risk coefficient can be updated in real time according to the update of the dynamic region (such as sudden logistics interruption), and the formula result automatically reflects the risk change, supporting rapid decision-making; Through weighted comprehensive evaluation + risk inhibition, this calculation method realizes the dynamic, comprehensive, and interpretable evaluation of the supply chain availability while ensuring the calculation efficiency, and is especially suitable for the optimization and risk warning of the supply chain in the grid management scenario.

[0020] Integrate the grid availability index of the dynamic region and the dynamic regional risk coefficient as the dynamic spatio-temporal grid benchmark of the dynamic region; integrate the dynamic spatio-temporal grid benchmarks of all dynamic regions to generate a dynamic spatio-temporal grid benchmark library; According to the dimensions of the data cube, all suppliers, products, dynamic regions, and events are used as nodes, and corresponding attributes are added to each node; and the supplier node is bound to the dynamic region node. If the dynamic regional risk coefficient changes, the grid availability index of the supplier is synchronously updated; Among them, the attributes of the supplier include ID, name, affiliated dynamic region, contract status, and capacity utilization rate. The attributes of the product include model, affiliated supplier, inventory status, and location area. The attributes of the dynamic region include geographical location, dynamic regional risk coefficient, logistics efficiency, and capacity load. The event attributes include type (such as power rationing), influence scope (dynamic region), and timestamp; Extract all supply relationships, logistics paths, contract associations, and event impacts in the data cube as edges, and assign edge weights to each type of edge; Take the relationship between the supplier and the product as the supply relationship, calculate the capacity utilization rate of each supplier respectively as the supplier capacity utilization rate, and then take the product of the supplier capacity utilization rate and the dynamic regional risk coefficient as the basic weight of the corresponding edge; Take the transportation route between two dynamic regions as the logistics path, obtain the corresponding transportation cost, and take the product of the dynamic regional risk coefficient and the transportation cost as the basic weight of the corresponding edge; Take the relationship between the supplier and the contract as the contract association, and take the contract default risk index between the supplier and the corresponding contract as the basic weight of the corresponding edge; Taking the relationship between events and the affected area as the event impact, based on the data cube, calculating the total external dependence through the TFR (Total Foreign Dependence) calculation formula as the industrial chain dependence, and performing a weighted sum of the dynamic regional risk coefficient and the industrial chain dependence to obtain the dynamic regional vulnerability; taking the product of the event's influence range and the dynamic regional vulnerability as the basic weight of the corresponding side; Among them, the calculation formula of TFR is that TFR is equal to the average of the import dependence (FIR) and the export dependence (FMR); Normalize the basic weights of all sides so that the sum of all basic weights is 1; Multiply the normalized basic weight by the global adjustment coefficient of the dynamic regional risk coefficient to obtain the edge weight; The calculation method of the global adjustment coefficient of the dynamic regional risk coefficient is: perform a weighted sum of the dynamic regional risk coefficient and the contract default risk index, and add 1 to the result to obtain the global adjustment coefficient; Based on the nodes and edges, construct a supply chain topology map, and display the node relationships and risk conduction paths (such as "Power rationing in Sichuan → Decrease in production capacity of Supplier A → Shortage of goods in Guangdong") through visualization tools (such as Tableau); the risk conduction path is to identify high-risk nodes and the affected area, and high-risk nodes can be defined as nodes corresponding to a dynamic regional risk coefficient higher than the preset threshold. When the risk of a certain area is too high, cascading suppliers are recommended (such as enabling backup Supplier B); Decompose the business objective into different KPI indicators through the KPI hierarchical decomposition method based on the SMART principle, including cost path indicators, market path indicators, and risk path indicators; It should be noted that the received business objective instructions need to follow the SMART principle to ensure that the objectives are achievable. The SMART principle includes specific, measurable, achievable, relevant, and time-bound; Specific: Clearly define the objective content. For example, "Reduce the total supply chain cost" needs to be refined to "Reduce the logistics cost in the East China region by 10%"; Measurable: Quantify the objective. For example, "Increase the on-time delivery rate from 85% to 95%"; Achievable: Combine resources and capabilities. For example, "Achieve cost reduction by optimizing the logistics path rather than building a new warehouse"; Relevant: Consistent with the corporate strategy. For example, "Minimize risks" needs to be associated with the goal of enhancing supply chain resilience; Time-bound: Set a time range. For example, "Complete cost optimization within the third quarter (Q3)"; The exemplary decomposition process of decomposing business objectives by the KPI hierarchical decomposition method is as follows: Business Objectives (Strategic Level): Reduce the total supply chain cost by 10% (annual target); Specific Objectives (Tactical Level): Cost Path: Optimize the logistics path to reduce transportation costs by 5% and reduce inventory holding costs by 3% through inventory sharing; Market Path: Increase the on-time delivery rate to 95% and increase the demand matching degree by 10%; Risk Path: Reduce the probability of regional cascading failure to below 0.1; KPI Metrics (Execution Level): Cost Path: Transportation Cost / Inventory Cost (specific value); Market Path: On-time Delivery Rate (percentage), Demand Matching Degree (index); Risk Path: Dynamic Regional Risk Coefficient, Path Dependence Degree; Quantify different KPI metrics respectively, including: Quantify the cost path metrics as: Define the core metrics of the cost path as transportation cost and inventory cost; Through the data cube and the supply chain topology map, query all paths between the dynamic region where the starting point is located and the dynamic region where the ending point is located (such as from dynamic region 1 to dynamic region 2). For each path, obtain the logistics path edge weight of each section on the path as the unit transportation cost of the section (such as path 1 passes through dynamic regions 1-2-3, the logistics path edge weight of 1-2 is used as the unit cost of section 1-2, and the logistics path edge weight of 2-3 is used as the unit cost of section 2-3). Take the sum of the unit transportation costs of all sections on the path as the transportation cost of the path; Through the data cube, obtain the available inventory of each inventory point within the dynamic region passed by the path, and simultaneously obtain the unit holding cost of the corresponding inventory point (such as the warehousing cost is ¥5 / unit / day). For each inventory point, take the product of the available inventory and the unit holding cost as the single inventory cost of the inventory point, and take the sum of the single inventory costs of all inventory points passed by all paths as the inventory cost of the path; Perform a weighted sum of the transportation cost and inventory cost of the path to obtain the total cost of each path, which is used as the quantified cost path metric; (The weights in the weighted sum process can be dynamically adjusted. For example, if the cost priority is higher, the transportation or inventory weight can be increased (such as transportation weight 0.7, inventory 0.3). If the inventory risk is greater, the inventory weight can be increased to prioritize controlling inventory costs. The weights can be dynamically adjusted according to real-time data (such as when the dynamic regional risk coefficient increases, the transportation weight can be reduced to avoid risk regions)); Quantify the market path metrics as: Define the core metrics of the market path as on-time delivery rate and market demand matching degree; Extract historical order data through the data cube, calculate the ratio of the number of on-time delivery orders to the total number of orders for a single path, and use it as the delivery on-time rate; obtain the market demand volume of the dynamic area (this is forecast data, which can be predicted through the market data of the area where it is located or directly obtained through a third-party API), combine the total actual production capacity within the dynamic area, calculate the ratio of the total actual production capacity to the market demand volume, and obtain the market demand matching degree; Perform a weighted sum of the delivery on-time rate and the market demand matching degree to obtain the market value of each path, which is used as the quantified market path indicator; Quantify the risk path indicator as: define the core indicators of the risk path indicator as the dynamic area risk coefficient and the path dependence degree; Through the fault tree analysis method (FTA) combined with the supply chain topology map, calculate the reliability indicator of each supplier node or dynamic area node passed by the path, which is used as the path dependence degree of the path on the corresponding node; The exemplary operation steps of the fault tree analysis method are as follows: Take the failure of the supplier node and the dynamic area node (such as supplier out-of-stock, inaccurate supplier delivery) as the top event, decompose it into underlying faults that may cause failure (such as equipment aging, human operation errors), construct logic gates (such as AND gates, OR gates) to connect the fault events, quantify the probability of each fault occurrence, and obtain the total probability of node failure through probability calculation, which is used as the reliability indicator; Obtain the dynamic risk coefficient of each dynamic area passed by each path through the dynamic spatio-temporal grid benchmark library; For each dynamic area node and supplier node passed by the path, calculate the product of the dynamic area risk coefficient and the path dependence degree of the path on the node, add up the product results of all dynamic areas passed by the path to obtain the basic risk value of the path, and multiply the basic risk value by a preset risk weight (such as 0.3 or 0.5), and this weight is dynamically adjusted by the business objective (such as a higher weight in the risk priority scenario)) to obtain the path risk value, which is used as the quantified risk path indicator; It should be noted that the preset risk weight will reflect the priority of the business objective. The lower the path risk value, the lower the path risk. Select the path with the lowest risk value to avoid the risk of cascading failures; Construct a multi-objective optimization model to perform multi-objective optimization on the quantified cost path indicator, market path indicator, and risk path indicator, and balance the conflicts among cost, market, and risk; The objective function of the multi-objective optimization model is: ; Define the constraint conditions as the logistics path capacity limit and supplier availability; Generate the Pareto optimal solution set through genetic algorithms, balance the conflicts among cost, market, and risk, and adjust the corresponding weights in real time according to business objective instructions; When the regional risk coefficient changes, recalculate the path risk value and trigger optimization; when a logistics interruption event occurs, trigger dynamic adjustment of path weights; For cost path selection, define the rule as selecting the path with the lowest total cost; For market path selection, define the rule as selecting the path with the highest market value; For risk path selection, define the rule as selecting the path with the lowest path risk value; Take the selection results of cost path, market path, and risk path selection as inputs, and output procurement strategy suggestions with confidence ratings and a dynamic priority supplier list through a dynamic weight voting model; Define the dynamic weight allocation rule as dynamically adjusting the path weights by the edge weight adjustment coefficient, and the total path weight is 1; Define the dynamic adjustment mechanism as follows: if the dynamic regional risk coefficient rises and is greater than the preset dynamic regional risk coefficient threshold (such as 0.2), then increase the risk weight by a preset ratio; if an emergency order instruction is received, then increase the market weight by a preset ratio (such as 0.3) and correspondingly reduce the cost weight; According to the selection results of cost path, market path, and risk path selection, normalize the total cost, market value, and path risk value of the path respectively, and use the normalized results as the cost score (value range 0 - 1, the lower the cost, the higher the score), market score (value range 0 - 1, the higher the market value, the higher the score), and risk score (value range 0 - 1, the lower the risk value, the higher the score) respectively; Combine the cost score, market score, and risk score with the weights of the corresponding path indicators, and obtain the comprehensive score of the path through weighted summation; Define the generation logic of the confidence rating: define the confidence rating division criteria as high confidence, medium confidence, and low confidence; Set the scoring interval threshold (such as [0.8, 0.5]), as well as the cost interval threshold (such as 0.7) and the risk interval threshold (such as 0.7); If the comprehensive score of the path is greater than or equal to the maximum value of the scoring interval threshold, and at the same time satisfies that the cost score is greater than or equal to the maximum value of the cost interval threshold and the risk score is greater than or equal to the maximum value of the risk interval threshold, then determine that the path has a high confidence level; If the comprehensive score of the path is greater than or equal to the minimum value of the scoring interval threshold and less than the maximum value of the scoring interval threshold, and at the same time satisfies that the cost score is greater than or equal to the minimum value of the cost interval threshold and the risk score is greater than or equal to the minimum value of the risk interval threshold, then determine that the path has a medium confidence level; If the comprehensive score of a path is less than the minimum value of the score interval threshold, or the cost score is less than the minimum value of the cost interval threshold, or the risk score is less than the minimum value of the risk interval threshold, then the path is determined to have low confidence; For each supplier node passed by the path, extract the performance status of all contracts of the supplier corresponding to the supplier node, and take the ratio of performance completed to performance failed as the supplier contract performance rate; Perform a weighted sum of the path dependence degree of the path on the supplier node, the available inventory of the supplier node, and the supplier contract performance rate to obtain the supplier priority score of each supplier; According to the supplier priority scores of each supplier, arrange them in descending order to form a dynamic priority supplier list; Set the risk area threshold. If the dynamic area risk coefficient of the dynamic area is less than the risk area threshold, it is determined to be a low-risk area. If the dynamic area risk coefficient of the dynamic area is greater than or equal to the risk area threshold, it is determined to be a high-risk area; Define the dynamic adjustment logic of the priority sorting rule of the dynamic priority supplier list as that emergency orders preferentially select suppliers of high-market-value paths (such as Supplier B with high market matching), and high-risk areas preferentially select suppliers from low-risk areas (such as Supplier A located in a low-risk area); Exemplarily, the output dynamic priority supplier list: Priority 1 is Supplier 1 (Path 1, dynamic area risk coefficient 0.2, sufficient inventory); Priority 2 is Supplier 3 (Path 2, low cost but dynamic area risk coefficient 0.4); Priority 3 is Supplier 5 (Path 3, high-risk area, backup supplier); Define the output format of the procurement strategy recommendation as: High-confidence strategy: Recommended path (Path 1 (lowest cost and risk controllable)), confidence rating (high confidence (confidence level 0.9)), execution recommendation (preferentially select Supplier 1, for example, cost savings of 15%, risk reduction of 20%); Alternative strategy: Path 2 (medium confidence, high market value but high cost); A visual auxiliary decision-making multi-path comparison graph can be constructed: Display the comparison of the total cost, market value, and risk value of the path through Tableau or GIS tools; High-risk paths are marked in red, and high-market-value paths are marked in green; Through Pareto frontier analysis, display the optimal solution set of cost-risk to assist in trade-off decisions (such as "Path 1 is the Pareto optimal solution with the lowest cost and lowest risk"); Obtain the ERP contract status of each supplier (including the validity period of the current contract, the used amount, and the payment terms), as well as the credit score of the supplier (for the historical performance of the supplier, such as on-time rate, quality pass rate) and the level (such as first level, second level) according to the dynamic priority supplier list and the supply chain topology map; Define the ABAC model rules including attributes and policy rules. Define the attributes including user attributes, environmental attributes, and operation attributes; User attributes include supplier level and credit score (such as ≥80 is high credit); environmental attributes include the current status and the grid availability index (such as <0.3 is low availability); operation attributes include the request type (such as order generation, bargaining invitation, data access); the current status includes the normal status and the emergency status; Define the policy rules based on the attributes, including defining the access rights of different level suppliers under different current statuses; An exemplary policy rule is defined as that in the normal status, first-level suppliers can access ERP contracts and initiate orders, while second-level suppliers can only view the public quotation template and cannot initiate orders; in the emergency status, second-level suppliers are temporarily granted contract permissions (such as a validity period of 24 hours), but high-risk data (such as inventory level, dynamic area risk coefficient) is hidden; According to the defined ABAC model, generate the access control list (ACL) of the supplier through the ABAC module of the smart contract engine. The ACL contains the access rights of the supplier (such as order generation, bargaining invitation) and the effective conditions, and record the permission change log; Define the trigger rules of the smart contract engine including the normal procurement process and the reverse link contract; Define the normal procurement process as automatically generating an order when the ERP contract status is valid and the supplier is in the dynamic priority supplier list; Define the execution steps as selecting the highest priority supplier according to the dynamic priority supplier list, calling the ERP interface through the smart contract engine, and automatically generating an order (such as product quantity, price, delivery time) based on the historical order template with thresholds; And synchronize the order information to the ERP system, update the contract status (such as used amount + order amount); and record the order hash value through the blockchain to ensure immutability; Define the reverse link contract as initiating bargaining with second-level suppliers when the grid availability index in the dynamic area where the supplier is located is lower than the preset grid availability index threshold; Define the execution steps as monitoring the grid availability index. If it is lower than the preset grid availability index threshold, trigger the reverse link contract, and send a bargaining invitation to the second-level supplier according to the preset historical quotation template, including parameters and constraints; The parameters are the demand quantity, the dynamic area risk coefficient, and the urgency of the order. The constraint is that the price needs to be lower than a preset ratio of the first-level supplier's quotation (e.g., 120% to avoid arbitrage); Based on the bargaining invitation sent to the second-level supplier, when the bargaining invitation is accepted by the second-level supplier or the order in the normal state requires manual confirmation, start the workflow engine (such as the Power Automate process); Set approval nodes, including the purchasing department, the finance department, and the legal department, and perform parallel approval operations according to the approval nodes through the workflow engine; Purchasing department: Review the order price and supplier priority; Finance department: Review budget compliance and payment terms; Legal department: Review contract terms and risk compliance; And define the parallel approval rule as that the order in the normal state only needs to be approved by the purchasing department, and the order in the emergency state or requiring bargaining needs to be approved in parallel by the purchasing department, the finance department, and the legal department; The approval logic is: It can take effect after the single-node approval of the purchasing department in the normal state; Emergency state or bargaining order: All three departments need to pass unanimously; If any department's approval times out (e.g., no response within 48 hours), trigger the automatic reminder mechanism; After all approvals are passed, update the ERP contract status (update to in execution) through the smart contract engine, call the historical order template, generate the final purchase order set in combination with the approval results, and record the log; Real-time monitor the events that cause the emergency state (such as the dynamic area risk coefficient exceeding the threshold). When the emergency state is lifted (such as the dynamic area risk coefficient returns to normal), through the ABAC module of the smart contract engine, revoke the temporary permissions of the second-level supplier, update the supplier access control list, synchronously record the permission changes, approval processes, and order execution results, and generate the permission change log; It should be noted that the deployment environment of the smart contract engine can be based on the hop network (such as the Star Tower Chain) or Ethereum, supporting parallel execution and fast confirmation. The regular orders in the trigger conditions can call the smart contract interface through the ERP system, and the reverse link can be triggered by loT sensors or network data (such as the availability index threshold is breached); The attributes of the ABAC permission dynamic adjustment, such as the supplier credit score, network availability index, etc., are stored in the blockchain or a trusted database; The policy can calculate the permission decision in real time based on the ABAC module; These can be integrated into the workflow engine integration. Through Power Automate or a custom workflow engine, it supports multi-department parallel branches and shortens the approval time; The Gas limit can also be set through Gas to avoid contract execution interruption. If the approval process times out or there is a permission conflict, the contract will automatically roll back and notify the administrator. High-risk data (such as inventory levels) is only visible to first-tier suppliers; Receive real-time Internet of Things data streams, including logistics trajectories (such as transportation time, location) and quality inspection reports (defect rate, batch data); and read the processing records and results of past events (such as delivery delays, quality defects) in the historical case library; Define the disposal logics for delivery delays, quality defects, and risk events. For delivery delays, if the delay time is greater than the preset delay time threshold (such as 3 days), calculate the liquidated damages and deduct the supplier's credit score (such as deduct 5 points); for quality defects, if the product defect rate is greater than the preset defect rate threshold, trigger the supplier switching mechanism (such as enabling alternative first-tier suppliers, enabling second-tier suppliers); for risk events, identify unplanned risks (such as equipment failures) through AI anomaly detection techniques (such as the LogBERT model); According to the disposal logics triggered by the multi-modal anomaly response engine, use the BERT model to analyze the historical case library and match similar historical cases of the current event's disposal logic (such as delivery delay + quality defect in a certain region), and then push the historical optimal strategy (such as the cost increases by 100% after switching to Supplier 1, but the risk decreases by 20%); According to the impact of the current event, update the risk weights, cost weights, and risk weights in the resilience improvement principle-based dynamic weight voting model, and recalculate the path selection rules (such as the lowest risk value has the highest priority improvement); According to the type of the current event, if there is no corresponding type in the data cube dimension, expand the data cube dimension (such as adding a cross-border policy risk dimension according to the policy backup mechanism); and update the data cube fields (such as adding a record of changes in the supplier's credit score); synchronously update the supplier's credit score and the corresponding threshold parameters; Store the current event and the disposal result in the historical case library to form a closed-loop iteration.

[0021] Embodiment 2

[0022] Please refer to Figure 3 As shown, for the parts not described in detail in this embodiment, refer to the description content of Embodiment 1. Provide a supplier price management method based on big data analysis, including: S1: Collect real-time core data of suppliers through a standardized interface, and obtain auxiliary data through a third-party API to construct a multi-dimensional data cube; divide the supplier cluster according to geographical grids, dynamically calculate the grid availability index, form a dynamic spatio-temporal grid benchmark library, and synchronously generate a supply chain topology map; S2: Based on the data cube and the dynamic spatio-temporal grid benchmark library, receive and quantify business target instructions, select a path through the rule engine; adopt a dynamic weight voting model, and combine the path selection results to output procurement strategy suggestions with confidence ratings and a dynamic priority supplier list; S3: Based on the dynamic priority supplier list and the supply chain topology map, combine the ERP contract status and the supplier credit score, dynamically adjust permissions through the ABAC model, and trigger the smart contract engine to generate an order or a bargaining invitation; conduct parallel approval through the workflow engine to generate a set of procurement orders and a permission change log; S4: Receive the IoT real-time data stream and the historical case library, trigger the disposal logic through the multimodal anomaly response engine; match similar historical cases based on the BERT model to push the historical optimal strategy, and synchronously update the weight rules, the data cube structure, and the supplier credit score threshold to form a full-link closed-loop iteration.

[0023] Embodiment III

[0024] This embodiment publicly provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the running mode of the above-provided supplier price management method based on big data analysis.

[0025] Since the electronic device introduced in this embodiment is the electronic device adopted for implementing the supplier price management method based on big data analysis in the embodiments of the present application, based on the supplier price management method based on big data analysis introduced in the embodiments of the present application, those skilled in the art can understand the specific implementation manners and various variation forms of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in the embodiments of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device adopted for the supplier price management method based on big data analysis in the embodiments of the present application, it falls within the scope of protection of the present application.

[0026] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0027] The above is only the preferred embodiment of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be noted that for ordinary technical users in the technical field, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. The supplier price management system based on big data analysis is characterized by: include: Multi-dimensional data cube construction unit: collects real-time core data of suppliers through standardized interfaces, obtains auxiliary data through third-party APIs, and constructs multi-dimensional data cubes; Divide supplier clusters by geographic grid, dynamically calculate grid availability index, form a dynamic spatiotemporal grid benchmark library, and synchronously generate supply chain topology maps; Multi-path decision-making coordination unit: Based on the data cube and dynamic space-time grid benchmark library, it receives and quantifies business target instructions and selects paths through the rule engine; Adopting a dynamic weighted voting model, combined with the path selection results, it outputs procurement strategy recommendations with confidence ratings and a dynamic priority supplier list; Smart contract execution and permission network unit: Based on the dynamic priority supplier list and supply chain topology map, combined with ERP contract status and supplier credit score, permissions are dynamically adjusted through the ABAC model, and the smart contract engine is triggered to generate orders or bargaining invitations; through the workflow engine, parallel approval is performed to generate purchase order sets and permission change logs; Closed-loop autonomous optimization and resilience enhancement unit: Receives real-time data streams and historical case libraries from the Internet of Things, triggers handling logic through a multimodal exception response engine; pushes historical optimal strategies based on matching similar historical cases with the BERT model, and simultaneously updates weight rules, data cube structures, and supplier credit score thresholds to form a full-link closed-loop iteration.

2. The supplier price management system based on big data analysis according to claim 1 is characterized in that: The multidimensional data cube is constructed in the following manner: Collect real-time core data from suppliers through standardized interfaces, including quotations, inventory levels, actual production capacity, and contract status; Obtain auxiliary data about the region through third-party APIs, including regional power restriction policies, competitive product prices, design capacity, and logistics disruption events; Clean and unify the collected core data and auxiliary data; Define the dimensions and metrics of the data cube. The dimensions include time dimension, supplier dimension, administrative region dimension, product dimension, event dimension and contract dimension. The metrics include average quotation price, inventory availability, capacity utilization, regional risk coefficient and contract default risk index. According to the processed auxiliary data, a time window is set, with the current moment as the benchmark, and the ratio of the difference between the maximum price and the minimum price of the competitor product in the past time window to the average price of the competitor product is used as the price fluctuation range of the competitor product; Extract the number, duration and impact range of regional power restriction policies in the past time window from the auxiliary data, perform normalization processing on them respectively, and obtain the power restriction risk value; The price fluctuation range of competing products, the power restriction risk value and the number of logistics interruption events in the past time window are used as coefficient indicators for calculating the administrative regional risk coefficient. Each coefficient indicator is normalized to obtain the regional risk coefficient. According to the defined dimensions and measures, dimension tables and fact tables are constructed through the star model, and foreign keys are integrated to form a cube structure as a data cube.

3. The supplier price management system based on big data analysis according to claim 2 is characterized in that: The construction method of the dynamic spatiotemporal grid benchmark library includes: Based on the dimensions of the data cube, the supplier dimension, product dimension, and event dimension are business-associated by product type and event type, and different dynamic areas are divided according to the business association as different supplier clusters; Extract all data covered by the dynamic area from the data cube, and re-obtain the regional risk coefficient corresponding to the dynamic area as the dynamic regional risk coefficient; The ratio of the total actual output value of all enterprises in the dynamic area to the total designed capacity is taken as the capacity utilization rate of the dynamic area; Extract the contract performance status of all contracts in the dynamic area to obtain the contract performance rate in the dynamic area; Based on the capacity utilization rate, inventory availability rate and contract fulfillment rate in the dynamic area, weights are assigned to the capacity utilization rate, inventory availability rate and contract fulfillment rate respectively, and then combined with the dynamic area risk coefficient to obtain the grid availability index; Integrate the grid availability index and dynamic area risk coefficient of the dynamic area as the dynamic space-time grid benchmark of the dynamic area; integrate the dynamic space-time grid benchmarks of all dynamic areas to generate a dynamic space-time grid benchmark library.

4. The supplier price management system based on big data analysis according to claim 3 is characterized in that: The supply chain topology map is constructed in the following ways: According to the dimensions of the data cube, all suppliers, products, dynamic regions and events are taken as nodes, and corresponding attributes are added to each node; the supplier node is bound to the dynamic region node, and if the dynamic region risk coefficient changes, the supplier's grid availability index is synchronously triggered to update; Extract all supply relationships, logistics paths, contract associations, and event impacts in the data cube as edges, and assign edge weights to each type of edge; The relationship between suppliers and products is regarded as the supply relationship, and the supplier's capacity utilization rate is calculated as the supplier's capacity utilization rate. The product of the supplier's capacity utilization rate and the corresponding dynamic regional risk coefficient is taken as the basic weight of the corresponding edge. The transportation route between two dynamic areas is used as the logistics path, the corresponding transportation cost is obtained, and the product of the dynamic area risk coefficient and the transportation cost is taken as the basic weight of the corresponding edge; The relationship between the supplier and the contract is taken as the contract association, and the contract default risk index is taken as the basic weight of the corresponding edge; The relationship between the event and the affected area is taken as the event impact. The industrial chain dependence of the dynamic area is calculated through the TFR formula. The dynamic area risk coefficient and the industrial chain dependence are weighted and summed according to the preset ratio to obtain the dynamic area vulnerability. The product of the impact range of the event and the dynamic area vulnerability is taken as the basic weight of the corresponding edge. Normalize the basic weights of all edges so that the sum of all basic weights is 1; Multiply the normalized basic weight by the global adjustment coefficient of the dynamic regional risk coefficient to obtain the edge weight; Based on the nodes and edges, a supply chain topology map is constructed.

5. The supplier price management system based on big data analysis according to claim 4 is characterized in that: The definition method of the rule engine selecting the path includes: Decompose business goals into cost path indicators, market path indicators and risk path indicators through KPI decomposition based on the SMART principle; The cost path indicator is to calculate the weighted sum of transportation cost and inventory cost to obtain the total cost of each path, which is used as the cost path quantification; The market path indicator is the weighted sum of the on-time delivery rate and the market demand matching degree, and the market value is obtained as the market path quantification; The risk path indicator is to calculate the path risk value of each path as the risk path quantification; The reliability index of each supplier node or dynamic area node passed by the path is calculated by the fault tree analysis method as the path dependency of the path to the corresponding node; The dynamic risk coefficient of each dynamic area of ​​the path is obtained through the dynamic space-time grid benchmark library; Calculate the sum of the product of the dynamic regional risk coefficient and the path dependency to obtain the basic risk value of the path, and multiply the basic risk value by the preset risk weight to obtain the path risk value; Based on the quantified cost path indicators, market path indicators and risk path indicators, the path selection rules of the rule engine are defined as follows: The cost path is defined as choosing the path with the lowest total cost; The market path is defined as choosing the path with the highest market value; The risk path is defined as the path with the lowest path risk value.

6. The supplier price management system based on big data analysis according to claim 5 is characterized in that: The procurement strategy recommendation with confidence rating and the dynamic priority supplier list are obtained in the following ways: According to the selection results of the cost path, market path and risk path, the total cost, market value and path risk value of the path are normalized respectively, and the cost score, market score and risk score are obtained as the input of the dynamic weighted voting model, and the procurement strategy recommendations with confidence rating and the dynamic priority supplier list are output; The dynamic weight allocation rule is defined as dynamically adjusting the path weight by the edge weight adjustment coefficient, and the total path weight is 1; The dynamic adjustment mechanism is defined as follows: if the dynamic area risk factor increases and is greater than the preset dynamic area risk factor threshold, the risk weight is increased according to the preset ratio; if an emergency order is received, the market weight is increased according to the preset ratio and the cost weight is reduced accordingly; Combine the cost score, market score and risk score with the corresponding weights and obtain the comprehensive score of the path through weighted summation; The generation logic of the confidence rating is defined as: the confidence rating is divided into high confidence, medium confidence and low confidence; Set scoring interval thresholds, as well as cost interval thresholds and risk interval thresholds; Based on the comprehensive score, cost score and risk score, the confidence level of the path is determined by the score interval threshold, cost interval threshold and risk interval threshold; For each supplier node in the path, extract the performance of all contracts corresponding to the supplier node, and take the ratio of the number of completed contracts to the total number of failed contracts as the supplier contract performance rate; Based on the edge weights of the supply relationship in the supply chain topology graph, the path dependency of the path on the supplier node is extracted, and the weighted sum is performed with the available inventory of the supplier node and the supplier contract fulfillment rate to obtain the supplier priority score of each supplier. Generate a dynamic priority supplier list by sorting each supplier's supplier priority score in descending order; Set a risk area threshold. If the dynamic area risk coefficient of the dynamic area is less than the risk area threshold, it is judged as a low-risk area. If the dynamic area risk coefficient is greater than or equal to the risk area threshold, it is judged as a high-risk area. The dynamic adjustment logic of the priority sorting rules that define the dynamic priority supplier list is: in an emergency order scenario, priority is given to the path supplier with the highest market value. If the path passes through a high-risk area, priority is given to suppliers in low-risk areas.

7. The supplier price management system based on big data analysis according to claim 6 is characterized in that: The smart contract implementation method for generating an order or a bargaining invitation includes: Through the supply chain topology map and ERP system interface, the supplier's ERP contract status, credit score and grade can be obtained; Obtain the grid availability index of the dynamic area where the supplier is located from the dynamic space-time grid benchmark library; The ABAC model is defined to include attributes and policy rules. The attributes defined include user attributes, environment attributes, and operation attributes. User attributes include supplier rating and credit score; environmental attributes include current status and grid availability index; operational attributes include request type; current status includes normal status and emergency status; Define policy rules based on attributes, including defining access rights for different levels of suppliers at different current states; According to the defined ABAC model, the ABAC module of the smart contract engine generates the supplier's access control list and records the permission change log; Define the triggering rules of the smart contract engine including the regular procurement process and the reverse link contract; Define the triggering condition of the regular procurement process as when the ERP contract status is valid and the supplier is in the dynamic priority supplier list; The execution steps are defined as selecting the highest priority supplier in the dynamic priority supplier list, calling the ERP interface of the smart contract engine, and automatically generating an order based on the preset historical order template; The triggering condition of the reverse link contract is defined as if the grid availability index of the dynamic area where the supplier is located is lower than a preset grid availability index threshold; The execution steps are defined as sending a bargaining invitation to the second-tier supplier based on the preset historical quotation template, including parameters and constraints; The parameters are demand, dynamic regional risk factor and order urgency, and the constraint is that the bargaining price must be lower than the preset proportion of the first-tier supplier's quotation.

8. The supplier price management system based on big data analysis according to claim 7 is characterized in that: The generation method of the purchase order set and the permission change log includes: Based on the price negotiation invitation sent to the second-tier supplier, when the second-tier supplier accepts the price negotiation invitation, or when the order in the regular status needs manual confirmation, the parallel approval workflow engine is started; Set approval nodes, including the purchasing department, finance department, and legal department, and use the workflow engine to perform parallel approval operations based on the approval nodes; Define parallel approval rules: Normal orders only need approval from the purchasing department, while emergency orders or orders that require negotiation need approval from the purchasing department, finance department, and legal department. When all approvals are passed, the ERP contract status is updated through the smart contract engine, the historical order template is called, and the final purchase order set is generated based on the approval results; When the emergency state is lifted, the temporary permissions of the secondary supplier are revoked through the ABAC module of the smart contract engine, and the supplier's access control list is updated, the normal status permission rules are restored, the permission change time, approval process and order execution results are recorded synchronously, and a permission change log is generated.

9. The supplier price management system based on big data analysis according to claim 8, characterized in that: The method of synchronously updating the weight rule and the data cube structure to form a full-link closed-loop iteration includes: Receive real-time IoT data streams, including logistics tracks and quality inspection reports; and read the processing records and results of past events in the historical case library; The disposal logic is defined as delivery delay, quality defect and risk event. For delivery delay, if the delay time is greater than the preset delay time threshold, liquidated damages will be calculated and the supplier's credit score will be deducted. For quality defect, if the product defect rate is greater than the preset defect rate threshold, the supplier switching mechanism will be triggered. For risk events, unplanned risks are identified through AI anomaly detection technology. According to the handling logic triggered by the multimodal exception response engine, the BERT model is used to analyze the historical case library, match similar historical cases of the current event handling logic, and then push the historical optimal strategy; Based on the impact of current events, the risk weights, cost weights, and risk weights in the dynamic weight voting model are updated through the resilience enhancement principle, and the path selection rules are recalculated; According to the type of the current event, if there is no corresponding type in the data cube dimension, the data cube dimension is expanded; and the data cube field is updated; the supplier credit score and the corresponding threshold parameters are updated synchronously; the current event and the disposal results are stored in the historical case library to form a closed-loop iteration.

10. A supplier price management method based on big data analysis, which is implemented based on the supplier price management system based on big data analysis according to any one of claims 1 to 9, characterized in that: include: S1: Collect real-time core data of suppliers through standardized interfaces, obtain auxiliary data through third-party APIs, and build a multi-dimensional data cube; Divide supplier clusters by geographic grid, dynamically calculate grid availability index, form a dynamic spatiotemporal grid benchmark library, and synchronously generate supply chain topology maps; S2: Based on the data cube and dynamic space-time grid benchmark library, it receives and quantifies business target instructions and selects paths through the rule engine; Adopting a dynamic weighted voting model, combined with the path selection results, it outputs procurement strategy recommendations with confidence ratings and a dynamic priority supplier list; S3: Based on the dynamic priority supplier list and supply chain topology map, combined with ERP contract status and supplier credit score, the authority is dynamically adjusted through the ABAC model, and the smart contract engine is triggered to generate orders or bargaining invitations; through the workflow engine, parallel approval is performed to generate a purchase order set and authority change log; S4: Receives real-time data streams and historical case libraries from the Internet of Things, and triggers processing logic through a multimodal exception response engine; pushes historical optimal strategies based on matching similar historical cases with the BERT model, and simultaneously updates weight rules, data cube structures, and supplier credit score thresholds to form a full-link closed-loop iteration.

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