Bulk raw material supply chain whole-process intelligent management system and method based on one-code communication

Abstract

The invention relates to a one-code-based bulk raw material supply chain whole-process intelligent management system and method, and relates to the technical field of data management, and the system comprises a supplier demand module which is used for generating a supplier demand comprehensive index; the transportation cost module is used for calculating the transportation quality comprehensive cost of the bulk raw materials based on the supplier demand comprehensive index; the scheduling optimization module is used for generating inventory scheduling optimization indexes of the bulk raw materials; the resource allocation module is used for calculating a resource allocation index; the performance indicator module is used for generating comprehensive performance indicators of the whole process of the bulk raw material supply chain; and the intelligent management module is used for managing a supply chain of the bulk raw materials according to the comprehensive performance indexes, and realizing real-time acquisition and interactive transmission of data in each step through a one-code system to form closed-loop management. The system can improve the management intelligence level and the operation efficiency of a bulk raw material supply chain.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a full-process intelligent management system, method, electronic device and non-transient computer-readable storage medium for a bulk raw material supply chain based on Yimatong. Background Art

[0002] In modern industrial production and supply chain management, bulk raw materials, as key production factors, require comprehensive management of their procurement, transportation, storage, and use. Currently, companies generally use information technology to manage their supply chains, such as establishing ERP and WMS systems or integrating data aggregation through electronic spreadsheets and manual processes.

[0003] However, the lack of unified data interface standards between different systems has led to serious information silos and an inability to achieve efficient supply chain collaboration. On the other hand, processes that rely on manual entry or manual confirmation have a high error rate and are difficult to achieve real-time monitoring and rapid response. Summary of the Invention

[0004] In response to the technical problems existing in the prior art, the present invention provides a full-process intelligent management system, method, electronic device and non-transitory computer-readable storage medium for a bulk raw material supply chain based on a one-code pass, which can improve the management intelligence level and operational efficiency of the bulk raw material supply chain.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a full-process intelligent management system for a bulk raw material supply chain based on Yimatong, the system comprising: Supplier demand module, used to predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; A transportation cost module, configured to calculate the comprehensive transportation cost of the bulk raw materials based on the supplier demand comprehensive index; a scheduling optimization module, configured to generate an inventory scheduling optimization index for the bulk raw materials by combining the supplier demand comprehensive index and the transportation quality comprehensive cost; A resource allocation module, configured to integrate the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; A performance indicator module, configured to generate a comprehensive performance indicator for the entire bulk raw material supply chain process based on the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The intelligent management module is used to manage the supply chain of the bulk raw materials according to the comprehensive performance indicators, and realize the real-time collection and interactive transmission of data of each step through the one-code system to form a closed-loop management.

[0006] Optionally, the supplier demand module is further configured to: Collect historical quality data and delivery records of suppliers, and calculate the supplier's quality reliability index and delivery timeliness rate; Analyze the historical demand data of the supplier and establish a correlation model between historical cycle weights and historical demand quantities; Evaluate the supplier's technology R&D capabilities and price competitiveness, and adjust them based on the expected market growth rate to obtain the first impact item; Quantify service responsiveness and financial health, introduce seasonal factors, and obtain the second impact term; The supplier demand comprehensive index is generated by multi-dimensional weighted fusion of the supplier's quality reliability index, delivery timeliness rate, the association model, the first influencing item and the second influencing item.

[0007] Optionally, the transportation cost module is further configured to: Establish a transportation route network and collect the distance, unit transportation cost and transportation volume between each supply chain node; Introducing a risk sensitivity coefficient, combining the distance between each supply chain node, unit transportation cost and transportation volume, and the frequency of quality defects of the provided resources, to establish a transportation cost optimization function; Dynamically associate the transportation time and time efficiency cost between the supply chain nodes to obtain dynamic association items; The supplier demand comprehensive index is integrated, and the transportation cost optimization function and the dynamic correlation item are optimized to obtain the comprehensive transportation quality cost of the bulk raw materials.

[0008] Optionally, the scheduling optimization module is further configured to: Establish an inventory cost model that integrates order costs and holding costs; Constructing a first mapping relationship for collaborative optimization of a processing efficiency matrix and a resource allocation matrix; Constructing a second mapping relationship for collaborative optimization of the cost matrix and the time consumption matrix; A dynamic inventory scheduling strategy is generated based on the order cost, the holding cost, the supplier demand comprehensive index, the transportation quality comprehensive cost, the first mapping relationship, and the second mapping relationship to determine the inventory scheduling optimization index of the bulk raw materials.

[0009] Optionally, the resource allocation module is further configured to: Establish quantitative models of resource utilization matrix and efficiency matrix; Construct a set of constraints for the time matrix and cost matrix; Constructing a local resource allocation efficiency term for evaluating resource conversion efficiency per unit time and per unit cost based on the quantitative model and the set of constraints; Constructing a quality defect penalty factor according to the quality defect frequency; Constructing a basic potential factor based on the supplier demand comprehensive index, the inventory scheduling optimization index, and the comprehensive transportation quality cost; The resource allocation index is calculated according to the local resource allocation efficiency item, the quality defect penalty factor and the basic potential factor.

[0010] Optionally, the performance indicator module is further configured to: Obtaining the basic potential factor; Adjusting resource abundance according to the resource allocation index to construct a quantitative evaluation item of resource abundance; Constructing an exponential decay compensation term of the quality defect frequency according to the risk sensitivity coefficient and the quality defect frequency; Based on the basic potential factor, the quantitative evaluation item of the resource abundance and the exponential decay compensation item of the quality defect frequency, a comprehensive performance indicator of the entire process of the bulk raw material supply chain is generated.

[0011] Optionally, the one-code system includes: a coding module for uniquely identifying raw material batches, an intelligent terminal device for multi-source data collection, a blockchain data storage and traceability module, an intelligent early warning and handling system for abnormal events, and a multi-role collaborative work platform on the mobile terminal.

[0012] The present invention also provides a method for intelligent management of the entire process of a bulk raw material supply chain based on Yimatong, the method comprising: Predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; Calculating the comprehensive transportation quality cost of the bulk raw materials based on the comprehensive supplier demand index; Combining the supplier demand comprehensive index and the transportation quality comprehensive cost, generating an inventory scheduling optimization index for the bulk raw materials; Integrating the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Generate a comprehensive performance index for the entire bulk raw material supply chain process through the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The supply chain of the bulk raw materials is managed according to the comprehensive performance indicators, and the real-time collection and interactive transmission of data in each step are achieved through the one-code system to form a closed-loop management.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; a processor for reading and executing the computer software programs, thereby realizing a full-process intelligent management method for the bulk raw material supply chain based on Yimatong as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements a full-process intelligent management method for the bulk raw material supply chain based on Yimatong as described above.

[0015] The beneficial effects of the present invention are: (1) The present invention realizes the automatic collection, dynamic update and real-time transmission of data throughout the entire process of the raw material supply chain through the one-code system, effectively breaking down the information barriers between the various functional modules within the enterprise and external suppliers, and improving the response speed and collaborative management capabilities of the supply chain; (2) The present invention introduces multi-dimensional variables such as quality defect frequency, market growth expectations, and seasonal factors to construct a risk-sensitive model, which can dynamically adjust transportation strategies, inventory allocation, and resource scheduling to achieve rapid response and flexible adjustment to sudden risk events; (3) This invention constructs a supplier-demand comprehensive index (CSP) to quantitatively integrate multiple key factors such as quality, delivery, price, and service, providing enterprises with a more accurate basis for supplier rating and supporting the establishment of strategic procurement and long-term cooperative relationships.

[0016] In summary, the present invention effectively improves the management intelligence level and operational efficiency of the bulk raw material supply chain, enhances its ability to cope with complex market environments and dynamic demand changes, and has good application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A scenario diagram of a method for intelligent management of the entire bulk raw material supply chain based on Yimatong provided by the present invention; Figure 2 This is a schematic diagram of the structure of a full-process intelligent management system for bulk raw material supply chain based on Yimatong provided by the present invention; Figure 3 A flowchart of a method for intelligent management of the entire process of a bulk raw material supply chain based on Yimatong provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 , Figure 1 This is a scenario diagram of a method for intelligent management of the entire process of a bulk raw material supply chain based on Yimatong provided by the present invention. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0020] It should be noted that Figure 1 The scenario diagram of the full-process intelligent management method of the bulk raw material supply chain based on Yimatong is only an example. The terminal, server and application scenario described in the embodiment of the present invention are for the purpose of more clearly illustrating the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0021] Among them, the terminal can be used to: Predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; Calculating the comprehensive transportation quality cost of the bulk raw materials based on the comprehensive supplier demand index; Combining the supplier demand comprehensive index and the transportation quality comprehensive cost, generating an inventory scheduling optimization index for the bulk raw materials; Integrating the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Generate a comprehensive performance index for the entire bulk raw material supply chain process through the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The supply chain of the bulk raw materials is managed according to the comprehensive performance indicators, and the real-time collection and interactive transmission of data in each step are achieved through the one-code system to form a closed-loop management.

[0022] See also Figure 2 , Figure 2 This is a structural diagram of a full-process intelligent management system for a bulk raw material supply chain based on Yimatong provided by the present invention.

[0023] like Figure 2 As shown, an embodiment of the present invention proposes an intelligent management system for the entire process of a bulk raw material supply chain based on Yimatong, including: The supplier demand module 201 is used to predict the supplier credit and demand of bulk raw materials and generate a comprehensive supplier demand index; The transportation cost module 202 is used to calculate the transportation quality comprehensive cost of bulk raw materials based on the supplier demand comprehensive index; The scheduling optimization module 203 is used to generate an inventory scheduling optimization index for bulk raw materials by combining the supplier demand comprehensive index and the transportation quality comprehensive cost; Resource allocation module 204, for integrating supplier demand comprehensive index, inventory scheduling optimization index and transportation quality comprehensive cost to calculate resource allocation index; Performance indicator module 205, used to generate comprehensive performance indicators for the entire bulk raw material supply chain process through supplier demand comprehensive index, inventory scheduling optimization index, transportation quality comprehensive cost and resource allocation index; The intelligent management module 206 is used to manage the supply chain of bulk raw materials based on comprehensive performance indicators, and realize the real-time collection and interactive transmission of data in each step through the one-code system to form a closed-loop management.

[0024] In some embodiments, the supplier demand module 201 is further configured to: Collect suppliers' historical quality data and delivery records, and calculate suppliers' quality reliability index and delivery timeliness rate; Analyze the historical demand data of suppliers and establish a correlation model between historical cycle weights and historical demand quantities; Evaluate the supplier's technology R&D capabilities and price competitiveness, and adjust them based on the expected market growth rate to obtain the first impact item; Quantify service responsiveness and financial health, introduce seasonal factors, and obtain the second impact term; A comprehensive supplier demand index is generated by multi-dimensional weighted integration of the supplier's quality reliability index, delivery timeliness rate, correlation model, first impact item and second impact item.

[0025] Among them, the comprehensive index of supplier demand is expressed as: ; in, is the comprehensive index of supplier demand, is the quality reliability index, is the delivery timeliness rate, is the historical cycle weight, is the historical demand, It is the technology research and development capability, is price competitiveness, is the expected market growth rate, is the service responsiveness, It's financial health. is the seasonal factor, They are the first weight, the second weight and the third weight respectively.

[0026] In the specific implementation, It is a comprehensive supplier demand index used to comprehensively evaluate supplier capabilities and demand fit. Used to reflect the historical intensity of demand for quality and delivery. It is used to measure the combined impact of technology, price and market potential, and is the first impact item. The synergistic impact of service response, financial health and seasonality is the second influencing factor. The sum is 1, which is the weight coefficient of the three parts, and the importance is distributed according to needs.

[0027] The following example shows how to assign values ​​to various parameters to facilitate the calculation of CSP values. The details are as follows:

[0028] The calculation process is as follows, quality and deliverables:

[0029] Technology Price Market Item:

[0030] Service Financial Season Items:

[0031] The total CSP value is: 20.12+0.246+0.231=20.597.

[0032] Based on the above calculation results, CSP≈20.60. The unit of the CSP value depends on the specific application scenario. This shows that this supplier has good overall performance in terms of quality, delivery, technology, market potential, and service, making it suitable as a key cooperation partner in the bulk raw material supply chain.

[0033] In some embodiments, the shipping cost module 202 is further configured to: Establish a transportation route network and collect the distance, unit transportation cost and transportation volume between each supply chain node; The risk sensitivity coefficient is introduced, and the transportation cost optimization function is established by combining the distance between each supply chain node, unit transportation cost and transportation volume, and the frequency of quality defects of the provided resources; Dynamically associate the transportation time and time efficiency cost between each supply chain node to obtain dynamic association items; By integrating the comprehensive index of supplier demand, the transportation cost optimization function and dynamic correlation items are optimized to obtain the comprehensive transportation quality cost of bulk raw materials.

[0034] Among them, the comprehensive cost of transportation quality is expressed as:

[0035] Among them, TQC is the comprehensive cost of transportation quality, is the distance from point i to point j, is the unit transportation cost from point i to point j, is the transportation volume from point i to point j, QF is the quality defect frequency, k is the risk sensitivity coefficient, is the transportation time from point i to point j, is the timeliness cost from point i to point j, and CSP is the comprehensive index of supplier demand.

[0036] Specifically, is the transportation cost optimization function, It is a dynamic association item. It represents the product of transportation time and timeliness cost, and is linked to the comprehensive capability of the supplier (CSP). The larger the CSP, the stronger the supplier's capability and the smaller the impact on timeliness cost. Indicates that this cost item is used to find the minimum value in the optimization model.

[0037] Assume that there are three transportation routes from two supply points i to three demand points j:

[0038] Other parameter settings are as follows: QF=0.08 (quality defect frequency 8%); k=2 (moderate risk sensitivity); CSP=20.6 (from the previous example).

[0039] The calculation process is as follows:

[0040] Add the quality risk adjustment factor:

[0041]

[0042]

[0043] Calculate the impact of the exponential term:

[0044]

[0045] Yuan.

[0046] In this example, the comprehensive cost of transportation quality is approximately RMB 30,518.1, taking into account the actual logistics costs, the quality risk amplification effect, and the positive impact of supplier capabilities on timeliness.

[0047] In some embodiments, the scheduling optimization module 203 is further configured to: Establish an inventory cost model that integrates order costs and holding costs; Constructing a first mapping relationship for collaborative optimization of a processing efficiency matrix and a resource allocation matrix; Constructing a second mapping relationship for collaborative optimization of the cost matrix and the time consumption matrix; According to the order cost, holding cost, supplier demand comprehensive index, transportation quality comprehensive cost, the first mapping relationship and the second mapping relationship, a dynamic inventory scheduling strategy is generated to determine the inventory scheduling optimization index of bulk raw materials.

[0048] Among them, the inventory scheduling optimization index can be expressed as:

[0049] Among them, ISO is the inventory scheduling optimization index, CSP is the supplier demand comprehensive index, OC is the order cost, HC is the inventory holding cost, TQC is the transportation quality comprehensive cost, is the processing efficiency matrix from point i to point j, is the resource allocation matrix from point i to point j, is the cost matrix from point i to point j, It is the time consumption matrix from point i to point j.

[0050] Specifically, OC is the order cost, which is the fixed cost of each order; HC is the inventory holding cost, which is the holding cost of unit inventory; TQC is the comprehensive transportation quality cost, which is the comprehensive transportation quality cost. is the processing efficiency from point i to point j multiplied by the allocation (indicating operational capacity), It is the unit cost multiplied by the time consumption (representing the scheduling cost). The higher the ISO value, the more efficient the inventory scheduling. To reflect and The first mapping relationship between reflect and The second mapping relationship between .

[0051] Assume that the values ​​of the parameters are as follows: CSP=20.6, OC=500 yuan, HC=8 yuan, TQC=30,518.1 yuan, the matrix data is as follows:

[0052] The calculation process is as follows:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In this example, ISO ≈ 8.24 indicates that current inventory scheduling efficiency is relatively optimal given the current shipping costs and historical demand. Higher ISO values ​​indicate a better balance between order placement, inventory control, and scheduling efficiency.

[0059] In some embodiments, the resource allocation module 204 is further configured to: Establish quantitative models of resource utilization matrix and efficiency matrix; Construct a set of constraints for the time matrix and cost matrix; Based on the quantitative model and constraint condition set, a local resource allocation efficiency term is constructed to evaluate the resource conversion efficiency under unit time and unit cost. Construct a quality defect penalty factor based on the frequency of quality defects; Construct basic potential factors based on the supplier demand comprehensive index, inventory scheduling optimization index and transportation quality comprehensive cost; The resource allocation index is calculated based on the local resource allocation efficiency item, the quality defect penalty factor and the basic potential factor.

[0060] The resource allocation index is expressed as:

[0061] in, is the resource allocation index, is the supplier demand comprehensive index, ISO is the inventory scheduling optimization index, TQC is the transportation quality comprehensive cost, is the resource utilization matrix, is the efficiency matrix, is the time matrix, is the cost matrix, and QF is the quality defect frequency.

[0062] Specifically, The value of is used to measure the efficiency and rationality of resource allocation. It is the basic potential factor. CSP is the comprehensive index of supplier demand, which indicates the reliability of the supply chain source. ISO is the inventory scheduling optimization index, which indicates the efficiency of internal circulation. TQC is the comprehensive cost of transportation quality, which indicates the cost and risk between links. This item is used to measure the potential resource mobilization basis of the entire supply chain. For example, high CSP, high ISO, and low TQC indicate good overall potential; low CSP, low ISO, and high TQC indicate poor overall potential. The purpose of multiplying TQC by 1000 is to make the unit dimension more adaptable and prevent TQC values ​​that are too small from causing accuracy problems.

[0063] is the local resource allocation efficiency term, It is the resource utilization rate, which measures whether a certain resource is fully utilized. The closer to 1, the better. It is efficiency, such as the amount completed per unit time. It is the time consumed, the shorter the better. This is the cost of consumption; the lower the better. Essentially, this factor measures resource conversion efficiency per unit time multiplied by unit cost. For example, if resources are fully utilized (high R), output efficiency is high (high E), time is short (low T), and cost is low (low C), this value will be high, indicating a rational and efficient resource allocation. This part considers local / segmented paths and complements the first level of "global potential" assessment mentioned above.

[0064] is the quality defect penalty factor, QF is the quality defect frequency (the lower the better). ( ) is the “health” of the overall quality, and squaring allows small quality issues to have a more sensitive impact on the results.

[0065] for example: When QF=0.05 (5%), (1-0.05)²=0.9025; When QF=0.10 (10%), (1-0.10)²=0.81; When QF=0.20 (20%), (1-0.20)²=0.64.

[0066] It can be seen that once quality problems increase, the overall RAI will be hit hard, which shows that resource allocation cannot only consider efficiency, but also quality risks.

[0067] Assume that the values ​​of the parameters are as follows: CSP=20.6, ISO=8.24, TQC=30,518.1, QF=0.04.

[0068]

[0069] The calculation process is as follows:

[0070]

[0071]

[0072]

[0073] In the present invention, A normalized resource efficiency value used to compare multiple configuration options. When modeling and analyzing multiple options in parallel, such as different routes or scheduling strategies, RAI can be used to determine which option has the highest resource allocation efficiency and achieve the best cost-effectiveness for resource utilization at a low quality factor (QF).

[0074] In summary, the RAI value of the present invention is not an efficiency indicator that simply pursues speed or cost, but takes into account overall potential, local execution and quality assurance, truly achieving efficient, stable and sustainable resource allocation evaluation.

[0075] In some embodiments, the performance indicator module 205 is further configured to: Obtain basic potential factors; Adjust resource abundance according to the resource allocation index and construct a quantitative evaluation item for resource abundance; According to the risk sensitivity coefficient and the quality defect frequency, an exponential decay compensation term of the quality defect frequency is constructed; Based on the basic potential factor, quantitative evaluation items of resource abundance and exponential decay compensation items of quality defect frequency, comprehensive performance indicators of the entire process of bulk raw material supply chain are generated.

[0076] Among them, the comprehensive performance indicator is expressed as:

[0077] in, is a comprehensive performance indicator. is the supplier demand comprehensive index, ISO is the inventory scheduling optimization index, TQC is the transportation quality comprehensive cost, is the resource allocation index, is resource abundance, is the quality defect frequency, and k is the risk sensitivity coefficient.

[0078] Specifically, It is a comprehensive performance indicator used to evaluate the integrated performance of the entire supply chain. It is a comprehensive index of supplier demand, used to evaluate the ability to match supply and demand. ISO is an inventory scheduling optimization index, used to evaluate the coordination between inventory and scheduling. TQC is the comprehensive cost of transportation quality, the lower the value, the better. It is the resource allocation index, which indicates the efficiency of resource allocation. It is the resource redundancy. The higher the value, the more flexible it is, but too high a value may lead to waste. is the frequency of quality defects and is a risk indicator. k is the risk sensitivity coefficient and is the tolerance for quality defects. RAI×(1+RF) is a quantitative evaluation item for resource abundance. is the exponential decay compensation term of the quality defect frequency.

[0079] PI is an integrated performance measurement metric that connects the entire process of supply and demand matching, inventory scheduling, transportation efficiency, and resource allocation, avoiding local optimality. Any change in any process (such as TQC changes or RAI improvements) will have a real-time impact on PI, facilitating dynamic monitoring. A higher PI indicates a more stable, coordinated, and cost-effective overall chain, making it a suitable core metric for digital supply chain decision-making, providing a clear basis for decision-making. All variables are calculated in real time using a data-driven model collected by Yimatong. This makes it suitable for complex scenarios involving bulk raw materials and integrates well with the Yimatong system.

[0080] Assume that the values ​​of the parameters are as follows: CSP=20.6, ISO=8.24, TQC=30,518.1, RAI=9.93× , RF=0.15,QF=0.04,k=2.

[0081] The calculation process is as follows:

[0082]

[0083]

[0084]

[0085] In summary, in the present invention By incorporating normalization coefficients and a multiplication model, PI can be used as a horizontal comparison metric across multiple scenarios, identifying the supply chain path or configuration strategy with the best overall performance. High PI values ​​indicate a good match between supply and demand, efficient inventory and scheduling, optimized transportation quality and costs, rational resource allocation, and low quality risk.

[0086] In some embodiments, the one-code system includes: a coding module for uniquely identifying raw material batches, an intelligent terminal device for multi-source data collection, a blockchain data storage and traceability module, an intelligent early warning and handling system for abnormal events, and a multi-role collaborative work platform on the mobile terminal.

[0087] As can be understood, the function of the intelligent management module 206 is to dynamically manage and optimize each link in the bulk raw material supply chain (supply, transportation, inventory, scheduling, resource allocation, etc.) based on comprehensive performance indicators (PIs). It monitors the performance of each node in real time and, based on the comprehensive evaluation results, promptly adjusts resource scheduling, transportation strategies, or supply chain layout, thereby improving overall efficiency and stability.

[0088] To support this intelligent management, the system incorporates a one-code system. The coding module assigns a unique identification code to each batch of bulk raw materials, ensuring traceability throughout the entire process. Smart terminal devices use sensors or barcode scanners to automatically collect data in real time during supply, transportation, and warehousing. The blockchain data storage and traceability module stores key data on-chain, ensuring that the information cannot be tampered with, ensuring the trustworthiness and traceability of supply chain data. The intelligent early warning and response system also stores key data on-chain, ensuring that the information cannot be tampered with, ensuring the trustworthiness and traceability of supply chain data. The intelligent early warning and response system automatically identifies anomalies (such as delays, quality issues, and supply disruption risks) and issues real-time warnings and emergency response recommendations. The mobile collaboration platform supports various roles, including suppliers, transporters, warehouses, and purchasers, enabling them to collaborate on tasks online anytime, anywhere, improving response speed.

[0089] Through the above modules, the YiMaTong system realizes real-time synchronization and closed-loop control of data flow, business flow, and management decision flow, making the entire bulk raw material supply chain more efficient, safe, and intelligent.

[0090] See also Figure 3 , provides a flowchart of a method for intelligent management of the entire process of a bulk raw material supply chain based on Yimatong of the present invention, comprising the following steps: Step 301: Forecast the supplier credit and demand of bulk raw materials and generate a comprehensive supplier demand index; Step 302: Calculate the comprehensive transportation quality cost of the bulk raw materials based on the supplier demand comprehensive index; Step 303: Generate an inventory scheduling optimization index for the bulk raw materials by combining the supplier demand comprehensive index and the transportation quality comprehensive cost; Step 304: Integrate the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Step 305: Generate a comprehensive performance index for the entire bulk raw material supply chain process based on the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; Step 306: Manage the supply chain of the bulk raw materials according to the comprehensive performance indicators, and realize the real-time collection and interactive transmission of data of each step through the one-code system to form a closed-loop management.

[0091] It should be noted that the specific embodiments and beneficial effects of the above steps 301 to 306 can be found in Figure 2 The detailed description of modules 201 to 206 will not be repeated here.

[0092] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: Predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; Calculating the comprehensive transportation quality cost of the bulk raw materials based on the comprehensive supplier demand index; Combining the supplier demand comprehensive index and the transportation quality comprehensive cost, generating an inventory scheduling optimization index for the bulk raw materials; Integrating the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Generate a comprehensive performance index for the entire bulk raw material supply chain process through the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The supply chain of the bulk raw materials is managed according to the comprehensive performance indicators, and the real-time collection and interactive transmission of data in each step are achieved through the one-code system to form a closed-loop management.

[0093] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: Predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; Calculating the comprehensive transportation quality cost of the bulk raw materials based on the comprehensive supplier demand index; Combining the supplier demand comprehensive index and the transportation quality comprehensive cost, generating an inventory scheduling optimization index for the bulk raw materials; Integrating the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Generate a comprehensive performance index for the entire bulk raw material supply chain process through the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The supply chain of the bulk raw materials is managed according to the comprehensive performance indicators, and the real-time collection and interactive transmission of data in each step are achieved through the one-code system to form a closed-loop management.

[0094] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems, methods, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0097] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A full-process intelligent management system for bulk raw material supply chain based on Yimatong, characterized by: The system comprises: The supplier demand module is used to predict the supplier credit and demand of bulk raw materials and generate a comprehensive supplier demand index; A transportation cost module, configured to calculate the comprehensive transportation cost of the bulk raw materials based on the supplier demand comprehensive index; a scheduling optimization module, configured to generate an inventory scheduling optimization index for the bulk raw materials by combining the supplier demand comprehensive index and the transportation quality comprehensive cost; A resource allocation module, configured to integrate the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; A performance indicator module, configured to generate a comprehensive performance indicator for the entire bulk raw material supply chain process based on the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The intelligent management module is used to manage the supply chain of the bulk raw materials according to the comprehensive performance indicators, and realize the real-time collection and interactive transmission of data of each step through the one-code system to form a closed-loop management.

2. The intelligent management system for the entire process of bulk raw material supply chain based on Yimatong according to claim 1 is characterized in that: The supplier demand module is also used to: Collect historical quality data and delivery records of suppliers, and calculate the supplier's quality reliability index and delivery timeliness rate; Analyze the historical demand data of the supplier and establish a correlation model between historical cycle weights and historical demand quantities; Evaluate the supplier's technology R&D capabilities and price competitiveness, and adjust them based on the expected market growth rate to obtain the first impact item; Quantify service responsiveness and financial health, introduce seasonal factors, and obtain the second impact term; The supplier demand comprehensive index is generated by multi-dimensional weighted fusion of the supplier's quality reliability index, delivery timeliness rate, the association model, the first influencing item and the second influencing item.

3. The intelligent management system for the entire process of bulk raw material supply chain based on Yimatong according to claim 2 is characterized in that: The transportation cost module is also used to: Establish a transportation route network and collect the distance, unit transportation cost and transportation volume between each supply chain node; Introducing a risk sensitivity coefficient, combining the distance between each supply chain node, unit transportation cost and transportation volume, and the frequency of quality defects of the provided resources, to establish a transportation cost optimization function; Dynamically associate the transportation time and time efficiency cost between the supply chain nodes to obtain dynamic association items; The supplier demand comprehensive index is integrated, and the transportation cost optimization function and the dynamic correlation item are optimized to obtain the comprehensive transportation quality cost of the bulk raw materials.

4. The intelligent management system for the entire process of the bulk raw material supply chain based on Yimatong according to claim 3 is characterized in that: The scheduling optimization module is also used to: Establish an inventory cost model that integrates order costs and holding costs; Constructing the first mapping relationship of collaborative optimization between processing efficiency matrix and resource allocation matrix Constructing a second mapping relationship for collaborative optimization of the cost matrix and the time consumption matrix; A dynamic inventory scheduling strategy is generated based on the order cost, the holding cost, the supplier demand comprehensive index, the transportation quality comprehensive cost, the first mapping relationship, and the second mapping relationship to determine the inventory scheduling optimization index of the bulk raw materials.

5. The intelligent management system for the entire process of bulk raw material supply chain based on Yimatong according to claim 4 is characterized in that: The resource allocation module is further configured to: Establish quantitative models of resource utilization matrix and efficiency matrix; Construct a set of constraints for the time matrix and cost matrix; Constructing a local resource allocation efficiency term for evaluating resource conversion efficiency per unit time and per unit cost based on the quantitative model and the set of constraints; Constructing a quality defect penalty factor according to the quality defect frequency; Constructing a basic potential factor based on the supplier demand comprehensive index, the inventory scheduling optimization index, and the comprehensive transportation quality cost; The resource allocation index is calculated according to the local resource allocation efficiency item, the quality defect penalty factor and the basic potential factor.

6. The intelligent management system for the entire process of bulk raw material supply chain based on Yimatong according to claim 5 is characterized in that: The performance indicator module is also used to: Obtaining the basic potential factor; Adjusting resource abundance according to the resource allocation index to construct a quantitative evaluation item of resource abundance; Constructing an exponential decay compensation term of the quality defect frequency according to the risk sensitivity coefficient and the quality defect frequency; Based on the basic potential factor, the quantitative evaluation item of the resource abundance and the exponential decay compensation item of the quality defect frequency, a comprehensive performance indicator of the entire process of the bulk raw material supply chain is generated.

7. The intelligent management system for the entire process of bulk raw material supply chain based on Yimatong according to claim 6 is characterized in that: The one-code system includes: a coding module for uniquely identifying raw material batches, an intelligent terminal device for multi-source data collection, a blockchain data storage and traceability module, an intelligent early warning and handling system for abnormal events, and a multi-role collaborative work platform on the mobile terminal.

8. A method for intelligent management of the entire process of a bulk raw material supply chain based on Yimatong, the method being implemented based on the system according to claim 1, characterized in that: The method comprises: Predict supplier credit and demand for bulk raw materials and generate a comprehensive supplier demand index; Calculating the comprehensive transportation quality cost of the bulk raw materials based on the comprehensive supplier demand index; Combining the supplier demand comprehensive index and the transportation quality comprehensive cost, generating an inventory scheduling optimization index for the bulk raw materials; Integrating the supplier demand comprehensive index, the inventory scheduling optimization index, and the transportation quality comprehensive cost to calculate a resource allocation index; Generate a comprehensive performance index for the entire bulk raw material supply chain process through the supplier demand comprehensive index, the inventory scheduling optimization index, the transportation quality comprehensive cost, and the resource allocation index; The supply chain of the bulk raw materials is managed according to the comprehensive performance indicators, and the real-time collection and interactive transmission of data in each step are achieved through the one-code system to form a closed-loop management.

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