Whole-industry link platform supply chain collaborative management method and system

Abstract

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain management, and in particular to a method and system for collaborative management of a supply chain on a full-industry linkage platform. Background Art

[0002] The current supply chain management faces the core pain points of data fragmentation in multiple links and low collaborative efficiency: supplier screening relies on manual evaluation, which can easily lead to matching deviations due to information asymmetry; file transmission security in the bidding and evaluation process is insufficient, and it is difficult to unify the scoring standards; demand forecasts are mostly based on single-dimensional data, and the accuracy is greatly affected by market fluctuations; logistics planning lacks dynamic adaptation capabilities, and changes in road conditions can easily cause transportation delays; full-link risk monitoring lags, and problems are often dealt with passively. The data of each link has not formed a unified management system, and there is a time lag in information synchronization, resulting in slow overall supply chain response speed and high operating costs, making it difficult to meet the needs of the entire industry chain for efficient collaboration and flexible scheduling. The present invention aims to solve the problems of existing supply chain data fragmentation, inefficient supplier matching, irregular bidding and evaluation, low demand forecast accuracy, rigid logistics planning, lagging risk monitoring, and poor collaboration and slow response in each link. Summary of the Invention

[0003] The present invention provides a method for collaborative management of supply chains on a full-industry linking platform, which includes: Collect multi-source data from the entire supply chain in real time, and use blockchain and lightweight encryption to build a distributed supply chain data warehouse; Perform feature mapping and weight calculation based on dual-core data to generate a multi-dimensional matching score table to recommend suppliers; Receive encrypted bidding documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bidding evaluation report; Call multi-dimensional correlation data to build a hierarchical prediction model and output three-dimensional prediction results; Combining the three-dimensional demand forecast results, the supply and demand gap is calculated based on the supply and demand elasticity coefficient coupling algorithm, and orders are automatically generated and the order status is synchronized to each terminal in real time; Build a digital twin model for the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using intelligent road condition algorithms; Monitor risk factors in real time. When risk signals are detected, automatically send warning information to generate response strategies, integrate full-link operational data and display them visually.

[0004] The above-mentioned method for collaborative supply chain management on a full-industry linkage platform collects multi-source data from the entire supply chain in real time and uses blockchain and lightweight encryption to build a distributed supply chain data warehouse, including: Collect full-link data from suppliers, manufacturers, logistics providers, distributors, and end customers in real time and perform standardized pre-processing; The processed data blocks are encrypted using a lightweight encryption algorithm, and the hash value and encrypted data are uploaded to the blockchain network to build a supply chain data warehouse.

[0005] The above-mentioned method for collaborative supply chain management of a full-industry linkage platform, wherein feature mapping and weight calculation are performed based on dual-core data to generate a multi-dimensional matching score table for recommending suppliers, includes: Based on the buyer's demand characteristics and the potential supplier's capability characteristics, a feature mapping model is established to quantify the characteristics of both the supply and demand sides and place them in the same metric space for comparison; Using the hierarchical analysis method, the weight of each characteristic dimension is dynamically calculated according to the current business scenario, the multi-dimensional matching degree of the supplier is generated, and a supplier recommendation list is formed.

[0006] The method for collaborative supply chain management of a full-industry linkage platform as described above includes receiving encrypted bidding documents, decrypting and storing them, calculating comprehensive scores based on quantitative indicators and expert qualitative indicators, and generating a structured bidding evaluation report, including: Receive digitally signed and encrypted bid documents, securely decrypt them using pre-configured key management services, and verify the validity of digital signatures by automatically parsing the structured data and storing it in a temporary database; The scores of quantitative indicators are automatically calculated. At the same time, the final comprehensive score of each bidder is calculated according to the preset weights through the online qualitative indicators of the evaluation experts, and a structured evaluation report is automatically generated.

[0007] The above-mentioned method for collaborative supply chain management on a full-industry linkage platform combines three-dimensional demand forecast results, calculates the supply-demand gap based on a supply-demand elasticity coefficient coupling algorithm, automatically generates orders, and synchronizes order status to each end in real time, including: Based on the construction of a global supply and demand view, the supply and demand gap in the future time period is dynamically calculated through the supply and demand elasticity coefficient coupling algorithm; The order generation logic is automatically triggered based on the supply and demand gap, a purchase order is generated to the supplier, a production work order is generated to the internal production system, and the order status is simultaneously notified to all parties involved.

[0008] The above-mentioned collaborative supply chain management method for a full-industry linkage platform includes building a supply chain digital twin model, accessing real-time logistics data, and dynamically planning the optimal transportation route using an intelligent road condition algorithm, including: Build a digital twin model of supply chain logistics based on geographic information systems, warehousing and transportation network node data; The intelligent road condition algorithm is used to analyze and process real-time data streams, dynamically simulate and calculate the current optimal transportation route, and dynamically reroute vehicles in transit.

[0009] The above-mentioned method for collaborative supply chain management of a full-industry linkage platform monitors risk factors in real time. When risk signals are detected, early warning information is automatically sent to generate response strategies. The full-link operational data is integrated and visualized, including: Set up a risk rule library to scan and identify the full-link real-time data in the data warehouse, and automatically trigger the warning mechanism when the risk signal reaches the warning threshold; Continuously integrate operational data from all links and use visual dashboards to present a panoramic view of the supply chain operation status in an intuitive manner.

[0010] A supply chain collaborative management system for a full industry linking platform, including: The chain encryption data warehouse module is used to collect multi-source data from the entire supply chain in real time, and uses blockchain and lightweight encryption to build a distributed supply chain data warehouse; The supplier recommendation module is used to perform feature mapping and weight calculation based on dual-core data, and generate a multi-dimensional matching score table to recommend suppliers; The bid evaluation report module is used to receive encrypted bid documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bid evaluation report; The demand forecasting module is used to call multi-dimensional related data to build a hierarchical forecasting model and output three-dimensional forecast results; The order generation module is used to combine the three-dimensional demand forecast results, calculate the supply and demand gap based on the supply and demand elasticity coefficient coupling algorithm, automatically generate orders and synchronize the order status to each terminal in real time; The logistics planning module is used to build a digital twin model of the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using the intelligent road condition algorithm; The risk monitoring module is used to monitor risk factors in real time. When risk signals are detected, it automatically sends early warning information to generate response strategies, integrates full-link operational data and displays them visually.

[0011] The beneficial effects achieved by the present invention are as follows: Through blockchain encryption, a distributed data warehouse is built to achieve secure storage and real-time sharing of data across the entire chain, eliminate information silos, and improve data credibility; relying on intelligent algorithms to complete accurate supplier matching, three-dimensional demand forecasting, and dynamic logistics planning, it replaces traditional manual decision-making, reduces human errors, and improves supply chain operational efficiency; it achieves automated collaboration in all links from supplier screening, bid evaluation to order generation and logistics scheduling, shortens response cycles, reduces operating costs, and provides stable technical support for the coordinated development of the entire industry chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0013] Figure 1 This is a flow chart of a supply chain collaborative management method for a full-industry linkage platform provided in Example 1 of the present application.

[0014] Figure 2 This is a schematic diagram of a supply chain collaborative management system for an entire industry linkage platform provided in Example 2 of the present application. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0016] Example 1 like Figure 1 As shown, the first embodiment of the present application provides a method for collaborative management of a supply chain of an entire industry linking platform, including: S1: Collect multi-source data from the entire supply chain in real time, and use blockchain and lightweight encryption to build a distributed supply chain data warehouse.

[0017] The real-time collection of multi-source data across the entire supply chain and the use of blockchain and lightweight encryption to build a distributed supply chain data warehouse include the following sub-steps: S11: Collect full-link data from suppliers, manufacturers, logistics providers, distributors and end customers in real time and perform standardized pre-processing.

[0018] By deploying distributed data collection nodes and adapting to multi-source data protocols from suppliers' ERP systems, manufacturers' MES data interfaces, logistics providers' GPS tracking systems, distributors' inventory management platforms, and end-customer feedback channels, the system establishes real-time data access channels for concurrent data collection across the entire supply chain. Data cleaning is completed by identifying and eliminating abnormal data using pre-set anomaly detection rules. Based on the supply chain data standard mapping library, heterogeneous data is uniformly converted into a structured format. In conjunction with the business scenario tagging system, the rule engine automatically matches the data to the supply chain link, business type, and associated objects, generating standardized data blocks containing multi-level tags.

[0019] S12: Encrypt the processed data blocks using a lightweight encryption algorithm, and upload the hash value and encrypted data to the blockchain network to build a supply chain data warehouse.

[0020] After receiving and processing standardized data blocks, a dynamic key derivation mechanism is introduced based on the national encryption SM4 algorithm. The core features in the data block label are first extracted, and a unique key bound to the data features is generated through a preset key derivation function. The key is used to perform SM4 block encryption. At the same time, a dynamic factor based on the timestamp is embedded in the encryption process, so that the encryption results of the same data block in different time periods are different.

[0021] After encryption is complete, a double hash value is calculated for the ciphertext (the primary hash is based on the entire ciphertext, and the secondary hash is based on the segmented ciphertext). The double hash, key derivation parameters, and dynamic factor metadata are packaged as a blockchain transaction and written to the ledger after consensus among the consortium chain nodes. Encrypted data blocks are distributed and stored in an off-chain shard cluster according to their feature tags. Double hash indexes in the blockchain ledger enable rapid location and integrity verification, preserving the efficiency of the SM4 algorithm while improving anti-cracking capabilities through dynamic keys and double verification.

[0022] S2: Perform feature mapping and weight calculation based on dual-core data to generate a multi-dimensional matching score table to recommend suppliers.

[0023] Among them, feature mapping and weight calculation are performed based on the dual-core data to generate a multi-dimensional matching score table to recommend suppliers, including the following sub-steps: S21: Based on the demand characteristics of the purchaser and the capability characteristics of the potential supplier, a feature mapping model is established to quantify the features of both the supply and demand sides and place them in the same metric space for comparison.

[0024] The system extracts buyer demand characteristics and potential supplier capability characteristics from the supply chain data warehouse. A feature parsing engine then structures these two types of data, identifying core demand indicators (such as lead time, quality standards, and cost thresholds) and supplier capability dimensions (such as production capacity, certifications, and historical performance records). Based on supply chain business scenarios, the system automatically matches supply and demand characteristics and establishes a multi-dimensional mapping matrix. A spatial mapping algorithm is used to project the feature vectors of both supply and demand sides into a pre-defined high-dimensional space. Coordinate calibration eliminates differences in feature dimensions and generates a standardized set of feature vectors.

[0025] The feature parsing engine uses the knowledge graph in the supply chain field as its underlying architecture. The graph predefines the core entities (such as delivery, quality, etc.), attributes and business rules of procurement requirements and supplier capabilities. At the same time, it embeds a dynamic rule learning sub-module, which is continuously trained through historical parsing cases and real-time business feedback data, and automatically adjusts the feature recognition threshold and parsing logic. Structured data is directly matched with graph fields to complete extraction, and unstructured data (such as demand description text, qualification certification documents) is mapped to graph nodes through semantic segmentation and entity linking technology, and core indicators and capability dimension label sets with confidence are dynamically generated.

[0026] S22: Using the hierarchical analysis method, the weight of each characteristic dimension is dynamically calculated according to the current business scenario, the multi-dimensional matching degree of the supplier is generated, and a supplier recommendation list is formed.

[0027] Based on the standardized feature vector set, a dynamic hierarchical analysis model is constructed based on the business scenario, dividing the feature dimensions into the target layer, the criterion layer, and the solution layer. The scenario feature extraction module analyzes the current business attributes to generate a scenario factor matrix, dynamically modifies the criterion layer judgment matrix, and adjusts the element values ​​by combining the statistical learning of the historical case feature influence. The eigenvalue method is used to calculate the weight of each dimension and pass the consistency test. The multi-dimensional matching degree calculation formula is then introduced:

[0028] and are the starting time and the ending time respectively; is the index of the feature dimension, is the total number of feature dimensions. Indicates the Dynamic weight function of feature dimensions, is the scene parameter and , while satisfying that the sum of all dimension weights is 1. Its value is dynamically adjusted as the business scenario changes, reflecting the importance of each feature dimension in different scenarios. Indicates that the supplier Time series vector on feature dimensions, It is a time variable, reflecting the dynamic changes of the feature dimension over time. Indicates the The historical impact coefficient of each feature dimension is updated in real time through the Bayesian estimation algorithm, reflecting the weight of the impact of the historical performance of the feature dimension on the current matching degree. represents the exponential decay term, where yes The first derivative with respect to time (i.e., the rate of change of the characteristic); It represents the covariance matrix between feature dimensions, reflecting the correlation between different feature dimensions. Its determinant value is used to quantify the impact of the degree of coupling between feature dimensions on the overall matching degree. Represents the Laplace operator, which strengthens the impact of changes in the spatial distribution of features on the matching degree. Represents the feature matching deviation matrix, and the elements in the matrix reflect the degree of deviation between the actual value and the ideal value of each feature dimension. Represents a real-time adjustment factor vector. The elements in the vector are dynamically generated based on real-time business data and are used to correct the impact of feature matching deviation on the final result. Represents the Hadamard product operator, which is used to implement element-level interactions such as feature matching deviation and real-time adjustment factors. represents the impulse response function vector, where is the lag time, reflecting the impact of external shocks on feature matching under different lag times. represents the real-time disturbance vector, Represents the current moment corresponding to the lag time, and the elements in the vector reflect the external disturbance occurring in real time.

[0029] Perform weighted operations on each supplier's feature vector and corresponding weight to generate multi-dimensional matching sub-scores and a comprehensive score. Sorted by the comprehensive score and associated with the feature matching details to generate a supplier recommendation list.

[0030] S3: Receive the encrypted bidding documents, decrypt and store them, calculate the comprehensive score based on the quantitative indicators and expert qualitative indicators, and generate a structured bidding evaluation report.

[0031] The following sub-steps are included: receiving encrypted bidding documents, decrypting and storing them, calculating comprehensive scores based on quantitative indicators and expert qualitative indicators, and generating a structured bidding evaluation report: S31: Receive the digitally signed and encrypted bidding document, decrypt it securely using the preset key management service, and verify the validity of the digital signature. The verification is performed by automatically parsing the structured data and storing it in a temporary database.

[0032] Build a dedicated receiving channel for encrypted bidding documents, and use the protocol adapter module to make it compatible with file transfers of different encryption formats. After receiving, it will automatically associate the bidder's identity and temporarily store it in an isolated buffer. Then call the preset key management service, adopt the "master key + session key" layered decryption mechanism, first unlock the session key through the hardware encryption module, and then use the session key to decrypt the bidding document. After decryption is completed, extract the digital signature information in the file and compare it with the bidder's public key pre-stored in the supply chain data warehouse. At the same time, verify the integrity of the file through the hash value verification algorithm. After the double verification is passed, start the structured parsing engine, and according to the standard field template of the bidding document, automatically extract core data such as price, qualifications, and performance commitments, map them to the corresponding fields of the temporary database, and establish a data index.

[0033] S32: Automatically calculate the scores of quantitative indicators, and at the same time calculate the final comprehensive score of each bidder according to the preset weights through the online qualitative indicators of the review experts, and automatically generate a structured evaluation report.

[0034] Retrieve the parsed bidding data, start the quantitative indicator calculation engine first, extract quantifiable data such as price and delivery cycle according to the preset standards, and use the formula

[0035] is the index of quantitative indicators, is the total number of quantitative indicators; Indicates the The weight coefficient of each quantitative indicator reflects the importance of different quantitative indicators in the comprehensive evaluation. Indicates the Real-time monitoring values ​​of quantitative indicators, Time variables, such as price, delivery cycle and other quantifiable data that change over time. Indicates the The benchmark threshold of each indicator is generated by fitting industry standards and historical optimal data, and serves as a reference benchmark for measuring indicator performance. Indicates the The calibration sensitivity coefficient of an indicator is dynamically adjusted according to the business scenario. A larger value indicates a higher sensitivity to indicator deviation. Indicates the Indicators in time The cumulative impact of abnormal deviations within the interval is , Indicates the The indicators in Abnormal deviation at a given moment. represents the exponential decay term, is the abnormal decay time constant, which is used to simulate the decay trend of abnormal impact over time. Indicates the The abnormal decay time constant of an indicator reflects the duration of the abnormal impact. Indicates the The first-order derivative of the abnormal deviation of an indicator with respect to time reflects the rate of change of the abnormal deviation and is used to capture the dynamic characteristics of the abnormal trend.

[0036] Automatically calculate the quantitative score of each bidder, and correct the impact of abnormal data through the dynamic threshold calibration module before calculation. Then open the expert online review port to collect experts' scores on qualitative indicators such as qualification compliance and solution feasibility. After the consistency verification algorithm eliminates abnormal scores, combined with multi-dimensional matching, the results are analyzed according to the

[0037] Indicates the multi-dimensional matching score; Scoring quantitative indicators; is the expert weight coefficient; Q is the mean of the expert qualitative scores after consistency verification; Represents the scenario adjustment factor, which is used to dynamically adjust the comprehensive score to suit different business scenarios. Its value is set according to specific business attributes. Indicates the quantitative index score F versus time The first derivative of , which reflects the rate of change of the quantitative indicator score over time; Represents the multi-dimensional matching S score versus time The first-order derivative of reflects the rate of change of multi-dimensional matching over time.

[0038] Calculate the supplier's comprehensive score, sort and integrate the score details by score, and automatically generate a structured evaluation report including the indicator calculation process and expert review opinions.

[0039] S4: Call multi-dimensional correlation data to build a hierarchical prediction model and output three-dimensional prediction results.

[0040] Call on multi-dimensional related data to build a hierarchical forecasting model to output refined demand forecast results for different regions and different products within a specific period in the future.

[0041] A hierarchical forecasting model is constructed using multi-dimensional correlated data from the supply chain data warehouse, including historical sales, inventory turnover, and regional market dynamics. The bottom layer uses a time series algorithm to capture basic demand trends for individual products. The middle layer uses a regional feature clustering algorithm to group and analyze regions with similar consumption patterns. The top layer incorporates external variables such as promotional activities, combining a market influencing factor model.

[0042] Through rolling iterations, the model parameters are optimized, ultimately outputting refined demand forecasts that integrate three dimensions: future specific cycles, regions, and products. Each dimension's forecast value is accompanied by a confidence interval.

[0043] S5: Combined with the three-dimensional demand forecast results, the supply and demand gap is calculated according to the supply and demand elasticity coefficient coupling algorithm, and orders are automatically generated and the order status is synchronized to each end in real time.

[0044] The process combines the three-dimensional demand forecast results, calculates the supply-demand gap based on the supply-demand elasticity coefficient coupling algorithm, automatically generates orders, and synchronizes the order status to each terminal in real time, including the following sub-steps: S51: Based on the construction of a global supply and demand view, the supply and demand gap in the future time period is dynamically calculated through the supply and demand elasticity coefficient coupling algorithm.

[0045] Based on three-dimensional demand forecasts, we simultaneously access internal production capacity data, real-time inventory data at all levels, and other information to construct a three-dimensional global supply and demand view. Using data cube technology, we dynamically correlate and visualize multi-dimensional data. On this basis, we use a supply-demand elasticity coefficient coupling algorithm to dynamically calculate the supply-demand gap, and simultaneously output the gap fluctuation range and influencing factors.

[0046] The formula for calculating the supply-demand gap is:

[0047] represents the real-time feedback correction factor; It represents the dynamic coefficient, which serves as a coupling bridge between demand elasticity and supply elasticity. When the market is dominated by demand fluctuations, its value approaches 1; when supply constraints are more significant, its value approaches 0, thus achieving scenario-based adaptation of supply and demand elasticity. It represents the three-dimensional demand forecast value at time t, which is the total future demand predicted by multi-dimensional factors. It represents the market volatility coefficient at time t, reflecting the intensity of market fluctuations on the demand side; Indicates the current calculation time point; Representation The policy adjustment coefficient at a certain moment reflects the impact of demand-side policy changes on demand. It is the first-order derivative of the demand forecast value with respect to time, reflecting the instantaneous rate of change of demand over time; Indicates the corresponding Three-dimensional demand forecast value at each moment; It is a sinusoidal function of the market volatility coefficient, simulating the cyclical characteristics of market fluctuations; represents the comprehensive supply capacity value at time t; represents the capacity elasticity coefficient at time t; represents the time response threshold; It is the time response threshold at time s, reflecting the response speed of the supply side to changes in demand.

[0048] The supply and demand elasticity coefficient coupling algorithm takes the dynamic correlation between supply and demand as its core and is designed in three progressive layers: the basic layer determines the core variables affecting supply and demand by analyzing historical data (the demand side focuses on market fluctuations, policy adjustments, etc., and the supply side focuses on production capacity elasticity and time response thresholds), and constructs calculation models for demand elasticity and supply elasticity respectively to ensure that single-dimensional elasticity can accurately reflect the sensitivity of each to the variables; the association layer designs a coupling mechanism and introduces a dynamic coefficient as a bridge. When demand fluctuations dominate the market, the dynamic coefficient approaches 1, and when supply constraints are more significant, the dynamic coefficient approaches 0, thereby realizing scenario-based adaptation of supply and demand elasticity; the optimization layer embeds a real-time feedback correction module, and reversely inputs the actual supply and demand deviation data into the algorithm. The weight distribution rules of dynamic parameters and dynamic coefficients enable the coupling results to reflect the elastic characteristics of each supply and demand, and capture the implicit correlation between the two (such as the inhibitory effect of demand surge on supply elasticity). The final output is a comprehensive supply and demand gap that takes into account both the immediate gap and trend changes.

[0049] S52: Automatically trigger the order generation logic based on the supply and demand gap, generate a purchase order to the supplier, generate a production work order to the internal production system, and simultaneously notify all participants of the order status.

[0050] Based on the supply and demand gap data, the order generation engine is started. First, the preset order trigger rules are matched based on the gap quantity combined with product type, regional distribution and business priority. When the gap meets the trigger conditions, the order generation process is automatically activated. The supplier recommendation list output by S2 is called, and the preferred suppliers are screened according to the comprehensive score. At the same time, the internal production system is connected to evaluate the existing production capacity and production cycle, and the order allocation ratio between external procurement and internal production is dynamically determined. The purchase order is pushed to the selected supplier, and the production work order with process arrangement and completion time limit is issued to the internal production system. All orders are assigned a unique blockchain identifier. Through the distributed message synchronization mechanism, the system pushes the order status to all participants in the supply chain in real time, and writes the order flow data into the blockchain for evidence storage to ensure that the order information is traceable and cannot be tampered with.

[0051] S6: Build a digital twin model of the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using the intelligent road condition algorithm.

[0052] The process of building a digital twin model for the supply chain, accessing real-time logistics data, and dynamically planning the optimal transportation route using the intelligent road condition algorithm includes the following sub-steps: S61: Build a digital twin model of the supply chain logistics link based on geographic information system, warehousing and transportation network node data.

[0053] Combining geographic information system road network data, spatial distribution data of storage nodes, and infrastructure parameters of the transportation network, the underlying data pool of the supply chain digital twin is constructed. Multi-source heterogeneous data is mapped to a unified spatial coordinate system through a spatiotemporal coordinate alignment algorithm. Using a layered modeling architecture, the physical layer performs three-dimensional digital reconstruction of entities such as warehouses, transportation hubs, and distribution points, assigning them physical properties. The logical layer constructs transportation path associations between nodes through a directed graph model, embedding constraint parameters such as road grade, traffic restrictions, and historical traffic efficiency. The interactive layer develops a real-time data access interface to achieve seamless integration with dynamic data sources such as vehicle GPS and warehouse sensors. Errors in the spatial relationships between nodes and paths in the model are automatically corrected, and model parameters are dynamically calibrated through deviation analysis between historical operating data and the physical world, enabling the digital twin model to accurately reflect the true state of the supply chain logistics network.

[0054] S62: Analyzes and processes real-time data streams through intelligent road condition algorithms, dynamically simulates and calculates the current optimal transportation route, and can dynamically reroute vehicles in transit.

[0055] Based on the supply chain digital twin model, real-time data interfaces are used to connect to the road condition monitoring system, meteorological platform, and GPS and status data of in-transit vehicles, forming a dynamic data stream. The intelligent road condition algorithm first performs spatiotemporal alignment and noise filtering on multi-source data, converting heterogeneous data such as road congestion index, weather impact coefficient, and road construction information into unified road section cost parameters. The weights of various influencing factors are adjusted in real time based on the timeliness requirements of the transportation task and the attributes of the cargo. The weight of real-time road conditions is increased in emergency transportation scenarios, and the weight of road carrying capacity factors is strengthened for bulk cargo transportation. Multiple candidate paths are simulated in the digital twin model using an improved A* path search algorithm. A comprehensive path score is calculated by combining historical traffic efficiency data with real-time cost parameters to select the optimal transportation route. When sudden changes in road conditions are detected, a dynamic rerouting mechanism is triggered, rapidly and iteratively calculating new paths within the twin model and simultaneously pushing them to the in-transit vehicle terminals and the dispatch center.

[0056] S7: Monitor risk factors in real time. When risk signals are detected, it automatically sends warning information to generate response strategies, integrates full-link operational data and displays them visually.

[0057] Among them, real-time monitoring of risk factors, when a risk signal is detected, automatically sending early warning information to generate a response strategy, generating a comprehensive supply chain operation decision report and visually displaying it, includes the following sub-steps: S71: Set up a risk rule library to scan and identify the full-link real-time data in the data warehouse, and automatically trigger the warning mechanism when the risk signal reaches the warning threshold.

[0058] Using the logistics digital twin model as the basic carrier, the real-time road conditions, vehicle trajectories, and path deviation data processed by the intelligent road condition algorithm are deeply reused and incorporated into the full-link risk monitoring system. A risk rule library covering all links of the supply chain is established, and the full-link data of the data warehouse is scanned through a real-time stream processing engine. The pattern recognition algorithm is used to compare risk characteristics. The logistics risk analysis directly calls the dynamic path data in the twin model, and combines the prediction model trained by historical delay cases to identify potential transportation interruption signals in advance. A dynamic warning threshold is set. When the risk signal of any link reaches the threshold, or a cross-link chain reaction trend is detected through the risk transmission algorithm, the warning mechanism is immediately triggered, and the graded warning information is automatically pushed to the relevant nodes. The preset response strategy library is called to generate solutions including logistics emergency routing and backup supplier switching.

[0059] S72: Continuously integrate operational data from all links and present a panoramic view of the supply chain operation status in an intuitive manner through a visual dashboard.

[0060] Continuously integrate real-time operational data from all supply chain links, including supplier fulfillment data, production progress data, logistics and transportation data, inventory change data, and risk warning information. Based on the supply chain digital twin model, a multi-dimensional visualization engine is constructed. The bottom layer is the basic indicator layer, displaying core data from each link with dynamic charts; the middle layer is the correlation analysis layer, using Sankey diagrams and network diagrams to illustrate cross-link data flows and dependencies; the top layer is the comprehensive decision-making layer, integrating key performance indicators to form a supply chain health score. Abnormal data is automatically triggered with visual highlighting, and combined with risk warning results, linked annotations are generated.

[0061] Example 2 like Figure 2 As shown, the second embodiment of the present application provides a supply chain collaborative management system for an entire industry linking platform, including: Chain Crypto Data Warehouse Module 21: Used to collect multi-source data from the entire supply chain in real time, using blockchain and lightweight encryption to build a distributed supply chain data warehouse; Supplier recommendation module 22: used to perform feature mapping and weight calculation based on dual-core data, generate a multi-dimensional matching score table to recommend suppliers; Bid evaluation report module 23: used to receive encrypted bid documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bid evaluation report; Demand forecasting module 24: used to call multi-dimensional related data to build a hierarchical forecasting model and output three-dimensional forecasting results; Order generation module 25: used to combine the three-dimensional demand forecast results, calculate the supply and demand gap based on the supply and demand elasticity coefficient coupling algorithm, automatically generate orders and synchronize order status to each terminal in real time; Logistics Planning Module 26: Used to build a digital twin model of the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using the intelligent road condition algorithm; Risk Monitoring Module 27: Used to monitor risk factors in real time. When risk signals are detected, it automatically sends warning information to generate response strategies, integrates full-link operation data and displays them visually.

[0062] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor; The memory is used to store one or more program instructions; A processor is used to run one or more program instructions to execute a supply chain collaborative management method for a full-industry link platform.

[0063] Corresponding to the above embodiments, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute a method for collaborative management of the supply chain of an entire industry linkage platform.

[0064] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are run on a computer, the computer executes the above-mentioned method for collaborative management of the supply chain of a full-industry linkage platform.

[0065] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0067] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0068] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0069] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0070] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0071] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0072] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A collaborative management method for supply chain of a full industry linking platform, characterized in that: include: Collect multi-source data from the entire supply chain in real time, and use blockchain and lightweight encryption to build a distributed supply chain data warehouse; Perform feature mapping and weight calculation based on dual-core data to generate a multi-dimensional matching score table to recommend suppliers; Receive encrypted bidding documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bidding evaluation report; Call multi-dimensional correlation data to build a hierarchical prediction model and output three-dimensional prediction results; Combining the three-dimensional demand forecast results, the supply and demand gap is calculated based on the supply and demand elasticity coefficient coupling algorithm, and orders are automatically generated and the order status is synchronized to each terminal in real time; Build a digital twin model for the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using intelligent road condition algorithms; Monitor risk factors in real time. When risk signals are detected, automatically send warning information to generate response strategies, integrate full-link operational data and display them visually.

2. The method for collaborative management of supply chain of a full industry linking platform according to claim 1, characterized in that: Real-time collection of multi-source data across the entire supply chain, using blockchain and lightweight encryption to build a distributed supply chain data warehouse, including: Collect full-link data from suppliers, manufacturers, logistics providers, distributors, and end customers in real time and perform standardized pre-processing; The processed data blocks are encrypted using a lightweight encryption algorithm, and the hash value and encrypted data are uploaded to the blockchain network to build a supply chain data warehouse.

3. The collaborative management method for supply chain of a full industry linking platform according to claim 1 is characterized in that: Based on the dual-core data, feature mapping and weight calculation are performed to generate a multi-dimensional matching score table to recommend suppliers, including: Based on the buyer's demand characteristics and the potential supplier's capability characteristics, a feature mapping model is established to quantify the characteristics of both the supply and demand sides and place them in the same metric space for comparison; Using the hierarchical analysis method, the weight of each characteristic dimension is dynamically calculated according to the current business scenario, the multi-dimensional matching degree of the supplier is generated, and a supplier recommendation list is formed.

4. The collaborative management method for supply chain of a full industry linking platform according to claim 1 is characterized in that: Receive encrypted bidding documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bid evaluation report, including: Receive digitally signed and encrypted bid documents, securely decrypt them using pre-configured key management services, and verify the validity of digital signatures by automatically parsing the structured data and storing it in a temporary database; The scores of quantitative indicators are automatically calculated. At the same time, the final comprehensive score of each bidder is calculated according to the preset weights through the online qualitative indicators of the evaluation experts, and a structured evaluation report is automatically generated.

5. The method for collaborative management of supply chain of a full industry linking platform according to claim 1, characterized in that: Combining the three-dimensional demand forecast results, the supply and demand gap is calculated based on the supply and demand elasticity coefficient coupling algorithm, and orders are automatically generated and the order status is synchronized to each end in real time, including: Based on the construction of a global supply and demand view, the supply and demand gap in the future time period is dynamically calculated through the supply and demand elasticity coefficient coupling algorithm; The order generation logic is automatically triggered based on the supply and demand gap, a purchase order is generated to the supplier, a production work order is generated to the internal production system, and the order status is simultaneously notified to all parties involved.

6. The method for collaborative management of supply chain of a full industry linking platform according to claim 1, characterized in that: Build a digital twin model for the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using intelligent road condition algorithms, including: Build a digital twin model of supply chain logistics based on geographic information systems, warehousing and transportation network node data; The intelligent road condition algorithm is used to analyze and process real-time data streams, dynamically simulate and calculate the current optimal transportation route, and dynamically reroute vehicles in transit.

7. The method for collaborative management of supply chain of a full industry linking platform according to claim 1, characterized in that: Real-time monitoring of risk factors. When risk signals are detected, early warning information is automatically sent to generate response strategies. The entire chain of operational data is integrated and visualized, including: Set up a risk rule library to scan and identify the full-link real-time data in the data warehouse, and automatically trigger the warning mechanism when the risk signal reaches the warning threshold; Continuously integrate operational data from all links and use visual dashboards to present a panoramic view of the supply chain operation status in an intuitive manner.

8. A supply chain collaborative management system for the entire industry linking platform, characterized by: include: The chain encryption data warehouse module is used to collect multi-source data from the entire supply chain in real time, and uses blockchain and lightweight encryption to build a distributed supply chain data warehouse; The supplier recommendation module is used to perform feature mapping and weight calculation based on dual-core data, and generate a multi-dimensional matching score table to recommend suppliers; The bid evaluation report module is used to receive encrypted bid documents, decrypt and store them, calculate comprehensive scores based on quantitative indicators and expert qualitative indicators, and generate a structured bid evaluation report; The demand forecasting module is used to call multi-dimensional related data to build a hierarchical forecasting model and output three-dimensional forecast results; The order generation module is used to combine the three-dimensional demand forecast results, calculate the supply and demand gap based on the supply and demand elasticity coefficient coupling algorithm, automatically generate orders and synchronize the order status to each terminal in real time; The logistics planning module is used to build a digital twin model of the supply chain, retrieve real-time logistics data, and dynamically plan the optimal transportation route using the intelligent road condition algorithm; The risk monitoring module is used to monitor risk factors in real time. When risk signals are detected, it automatically sends early warning information to generate response strategies, integrates full-link operational data and displays them visually.

9. A computer-readable storage medium, characterized in that It includes one or more program instructions, and the one or more program instructions are used by a processor to execute a method for collaborative management of supply chains on a full-industry linking platform as described in any one of claims 1-7.

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