A collaborative management system for enterprise procurement supply chain

Abstract

This invention discloses an enterprise procurement supply chain collaborative management system, comprising: a perception layer deployed at each node of the procurement supply chain, used to collect data on the flow and processing of renewable resource materials through passive RFID tags built into heterogeneous devices, and to perform data verification and synchronization through an inter-device self-organizing network; a trust layer used to bind the operator's biometric information with their identity, operating permissions, and initial credit rating, and to dynamically manage credit and cross-enterprise permissions based on their operating behavior and device feedback; and an analysis and decision-making layer used to analyze and issue early warnings on the perceived data through an edge computing module, and to simulate the value transformation path under different working conditions through a virtual mapping model. By deploying computing modules at the network edge, this invention can process data locally, independently and quickly identify and issue early warnings for anomalies such as equipment failures and quality deviations, achieving a leap from post-event response to pre-event prediction and in-event intervention.

Description

Technical Field

[0001] This invention belongs to the field of intelligent supply chain collaboration and circular economy technology, and in particular relates to an enterprise procurement supply chain collaboration management system. Background Technology

[0002] In current industrial and commercial practices, enterprise procurement supply chain management, particularly in the recycling and reuse of renewable resources, generally relies on a series of existing information technology tools and management models. Existing technologies typically employ a management system centered on Enterprise Resource Planning (ERP) systems, supplemented by Manufacturing Execution Systems (MES), Warehouse Management Systems (WMS), and barcode / QR code recognition technology. Data collection primarily relies on manual scanning or data entry, generating electronic documents at key nodes. To achieve a certain level of collaboration, enterprises exchange order and logistics information through Electronic Data Interchange (EDI) interfaces or by establishing supply chain data platforms. For identity verification, account passwords or physical access cards are commonly used. Regarding environmental value assessment, enterprises typically calculate deductible costs manually after the end of the financial cycle, based on third-party testing reports and procurement contracts, and separately declare carbon emission reductions or environmental credits according to environmental policies. These technological elements collectively constitute the current mainstream, segmented, and centralized supply chain management foundation.

[0003] However, the existing technological systems described above reveal systemic shortcomings when facing the demands of modern supply chains that require high transparency, real-time performance, strong collaboration, and immediate value transformation. First, their data foundation is fragile; reliance on human intervention leads to delayed, error-prone, and difficult-to-trace data collection, and data silos exist between systems, failing to support refined control and reliable traceability across the entire chain. Second, identity verification and access control methods are prone to misuse, and static, rigid permissions cannot be linked to dynamic behavior, resulting in unclear responsibility definitions and high trust costs during cross-organizational collaboration. Third, decision-making relies on centralized, lagging data analysis and human experience, failing to achieve predictive early warning of equipment status and global simulation optimization of operational strategies. Furthermore, enterprises often face a dilemma when attempting data collaboration: either refuse to share to protect trade secrets, creating collaboration barriers, or risk the leakage of original data for exchange. Finally, environmental performance and economic benefits are severely disconnected; the resource value accounting cycle is lengthy and opaque, failing to incentivize proactive and immediate green practices at all stages of the supply chain. These shortcomings hinder the in-depth exploration of the overall efficiency, transparency, and sustainability value of the supply chain. Summary of the Invention

[0004] To overcome the aforementioned shortcomings of existing technologies, this invention provides an enterprise procurement supply chain collaborative management system, which solves the problems of data lag, collaboration barriers, and value disconnect in existing supply chain systems that rely on manual labor and centralized architecture.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A collaborative management system for enterprise procurement supply chain includes a perception layer, a trust layer, an analysis and decision-making layer, a data security layer, and a value transformation layer that are sequentially connected in communication. The perception layer is deployed at each node of the procurement supply chain to collect data on the flow and processing of renewable resource materials through passive RFID tags built into heterogeneous devices, and to perform data verification and synchronization through an inter-device self-organizing network. The trust layer is used to bind the operator's biometric information to their identity, operating permissions and initial credit rating, and to dynamically manage credit and cross-enterprise permissions based on their operating behavior and equipment feedback. The analysis and decision-making layer is used to analyze and issue early warnings on the perceived data through the edge computing module, and to simulate the value transformation path under different working conditions through the virtual mapping model; The data security layer is used to achieve collaborative optimization of cross-enterprise credit assessment models through a federated learning model, provided that participating enterprises retain their data locally. The value conversion layer is used to connect with external carbon credit systems and, based on perceived data and personnel credit ratings, outputs cost deductions and carbon credit monetization amounts through preset accounting rules.

[0006] Preferably, in the sensing layer, the passive RFID tag stores equipment operating status parameters, material compatibility parameters, and communication protocols; the collected data is accompanied by time sequence and node markers, and is associated with the corresponding operator identification and equipment verification results to form a traceable data chain.

[0007] Preferably, the self-organizing network between devices in the perception layer has a self-healing function. When the communication of any node in the network is interrupted, its data can be relayed through adjacent nodes to ensure the continuity of critical data flow.

[0008] Preferably, the biometric information collected by the trusted layer is palm vein features and / or iris features; dynamic management specifically involves: collecting personnel operation behavior data and equipment verification results in real time, using them as input to dynamically update their credit rating, and adjusting their cross-enterprise operation permissions and transaction credit limits accordingly.

[0009] Preferably, in the analysis and decision-making layer, the edge computing module is deployed locally at the supply chain node. By running the edge computing algorithm, it independently identifies at least one of the following: equipment component wear, material purity deviation, and material compatibility anomaly, and triggers an alert.

[0010] Preferably, the virtual mapping model is constructed based on the equipment layout, parameters and flow rules of the physical nodes, keeps real-time status synchronized with the physical nodes, and is used to simulate and predict the results of new operating conditions after changes in suppliers, equipment or environmental policies.

[0011] Preferably, the data security layer adopts a horizontal federated learning model, in which each participating enterprise only sends the parameter gradient of the credit assessment model to the coordinator for aggregation and optimization, and all raw data is stored on the local server.

[0012] Preferably, the preset accounting rule for the value conversion layer is a two-factor rule. The first factor is a tiered deduction rule based on the waste resource utilization compliance rate, and the second factor is a fixed value superposition rule based on personnel credit rating. The tiered standard and superposition value can be dynamically configured according to policy updates.

[0013] Preferably, a two-way binding verification is implemented between the perception layer and the trust layer: the passive RFID tag in the perception layer is uniquely bound to the biometric information of the authorized operator in the trust layer; the device operation record is sent back to the trust layer as feedback data to correct the credit rating of the corresponding person.

[0014] Preferably, the heterogeneous equipment includes at least two of the following: waste treatment equipment, recycled material processing equipment, cold chain transportation equipment, and agricultural harvesting equipment; the data acquisition fields and internal verification logic configured on the passive RFID tags corresponding to different types of equipment are different.

[0015] The technical effects and advantages of the enterprise procurement supply chain collaborative management system of the present invention are as follows: 1. This invention, through passive RFID tags deployed in the sensing layer and self-organizing network technology, enables the system to automatically, in real-time, and continuously collect key data on renewable resource materials throughout the entire circulation process, assigning them time-series and node markers, fundamentally eliminating the lag and errors of manual data entry. The data is strongly correlated with operators and equipment status, forming an immutable and complete traceable data chain. This not only greatly improves data quality and business transparency but also provides a solid and reliable digital foundation for subsequent analysis, auditing, and accountability. The self-healing capability of the self-organizing network further ensures the continuity of key data and system robustness in complex industrial environments.

[0016] 1. This invention's trust layer uniquely binds and dynamically links an individual's identity and permissions to their operational behavior and device feedback by integrating strong biometric features such as palm vein and iris scans. This makes every operation trustworthy, traceable, and evaluable. The system dynamically updates the credit ratings of individuals and enterprises based on real-time behavioral data and intelligently adjusts their cross-enterprise operational permissions and transaction credit accordingly, realizing a shift from static permission allocation to dynamic credit governance. This mechanism significantly enhances identity credibility and operational security in cross-organizational collaboration, reducing the risk of internal fraud and external deception.

[0017] 1. This invention's analysis and decision-making layer, by deploying computing modules at the network edge, enables local data processing, independent and rapid identification and early warning of anomalies such as equipment failures and quality deviations, achieving a leap from post-event response to pre-event prediction and in-event intervention. Combined with a virtual mapping model synchronized 1:1 with the physical world, the system can simulate and predict various operating conditions such as supply chain changes and policy adjustments in the digital space, providing managers with quantitative and visualized decision support, thereby optimizing resource allocation and improving overall operational efficiency and resilience.

[0018] 2. This invention employs a horizontal federated learning model for its data security layer. Under the premise that the original sensitive data of all participating parties (such as biometrics, transaction details, and operational data) remains locally and does not need to be transmitted externally, the global credit assessment model can be collaboratively trained and optimized simply by exchanging encrypted model parameters. This design effectively resolves the contradiction between data silos and privacy leaks in supply chain collaboration, fully protecting core enterprise data assets and trade secrets while achieving the legitimate, secure sharing and joint enhancement of data value.

[0019] 3. The invention's value conversion layer automatically connects to external environmental rights systems such as carbon credits and incorporates intelligent accounting rules based on multi-dimensional data (such as resource utilization compliance rates and personnel credit). This enables real-time, automatic quantification of green behaviors like waste disposal and resource recycling into specific deduction ratios that can immediately offset procurement costs and tradable carbon assets. This directly transforms a company's environmental investment into real-time economic benefits, establishing a sustainable green business loop and greatly incentivizing all parties in the supply chain to proactively practice resource recycling and carbon reduction. Attached Figure Description

[0020] Figure 1 This is a flowchart of an enterprise procurement supply chain collaborative management system proposed in this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0023] refer to Figure 1 This invention provides a collaborative management system for enterprise procurement supply chain, the core of which lies in constructing a five-layer closed-loop architecture comprising a perception layer, a trust layer, an analysis and decision-making layer, a data security layer, and a value transformation layer. The perception layer utilizes passive RFID tags deployed on heterogeneous devices across industries and their self-organizing network to automatically collect and verify data on the flow and processing of renewable resource materials. The trust layer binds the operator's biometric information with identity, permissions, and credit rating, dynamically managing credit and cross-enterprise permissions based on real-time operational behavior and device feedback. The analysis and decision-making layer performs real-time analysis and early warning through a local edge computing module, and uses a virtual mapping model synchronized with physical nodes to simulate and optimize the value transformation path. The data security layer employs a horizontal federated learning model to achieve secure collaborative optimization of cross-enterprise credit assessment models while ensuring the local retention of original data for each enterprise. The value transformation layer, by associating with an external carbon credit system and based on perceived data and personnel credit ratings, automatically outputs cost deduction ratios and carbon credit monetization amounts according to preset two-factor accounting rules, ultimately achieving collaborative control of the entire procurement process and the immediate and reliable transformation of resource value. Example 1

[0024] Realizing the closed-loop value of automotive aluminum alloy recycling: Purpose of implementation: This addresses the issues of data silos, traceability difficulties, and lagging value accounting in the recycling, circulation, and reprocessing procurement of aluminum alloy scraps generated during automobile manufacturing, enabling real-time, reliable, and automated conversion of waste resources into valuable resources.

[0025] Implementation System: Deploy a system between a car manufacturer and its Tier 1 component suppliers.

[0026] Sensing layer: Install passive RFID tags and readers on the supplier's CNC machine tools, packing machines, and weighbridges.

[0027] Trusted layer: Deploy palm vein recognition terminals in supplier warehouses and OEM receiving areas.

[0028] Analysis and decision-making level: deploy edge servers in supplier factories and build a virtual mapping model of the entire supply chain in the OEM data center.

[0029] Data security layer and value conversion layer: The system software modules are deployed on the cloud of both parties and connected to the nationally recognized carbon benefit platform interface.

[0030] Implementation steps: a) Data Acquisition and Binding: After the machine tools of the component suppliers complete the processing of parts, they automatically generate scrap material data packages containing batch, weight, and time information, which are uploaded via the workshop's self-organizing network. After warehouse manager Wang verifies his identity using palm vein recognition, he scans the machine tool's RFID tag, and the system binds the material data to his identity.

[0031] b) Cross-enterprise transfer and verification: Scrap materials are packaged and transported to the OEM (Original Equipment Manufacturer). At the OEM's receiving area, receiving clerk Mr. Li verifies the palm vein pattern and scans the RFID tags on the material packages. The system automatically checks the incoming materials against the data sent by the supplier, completing the handover.

[0032] c) Intelligent Analysis and Decision-Making: The edge server analyzes data in real time to determine if the output of this batch is within the normal range. The virtual mapping model updates the material status to "received" and triggers a simulation calculation of the value of recycled aluminum.

[0033] d) Safety Collaboration and Value Settlement: The OEM initiates a request to evaluate the supplier's overall performance for the month. Both systems, through federated learning, jointly optimize the evaluation model without exchanging original operational data. Simultaneously, the value conversion module automatically calculates a 6% deduction for the current procurement cost based on the third-party tested purity of the recycled aluminum (compliance rate 98%) and the compliance records of Mr. Wang and Mr. Li's operations, and generates corresponding carbon credit pre-allocation vouchers. Implementation effect

[0034] The cycle from production to value settlement of scrap materials has been shortened from an average of 45 days to within 3 days.

[0035] It achieves 100% digital traceability of material flow, eliminating human error and omissions.

[0036] Through automated accounting, both parties save approximately 300,000 yuan annually in labor costs for financial reconciliation, and also gain substantial additional carbon credits. Example 2

[0037] Cross-domain cold chain drug traceability and network self-healing: Purpose of implementation: Ensure that the cold chain remains unbroken and data is not lost throughout the cross-regional, multimodal transport of biological agents, and maintain basic system functions during partial communication interruptions to meet stringent drug regulatory requirements.

[0038] Implementation System: It is applied to the cold chain logistics network from a pharmaceutical company to multiple regional distribution centers.

[0039] Sensing layer: Each medicine transport box integrates a special passive RFID tag to monitor temperature and humidity in real time; the transport vehicle acts as a mobile gateway.

[0040] Perception layer network: Low-power self-organizing network is formed between tags and between tags and vehicle gateway.

[0041] Implementation steps: a) Departure: Medicines are loaded onto vehicles in the warehouse. The RFID of each transport box begins to record data such as time, temperature, and packer ID. The data is then aggregated to the vehicle gateway via a self-organizing network, and the gateway transmits the data back to the cloud via a cellular network.

[0042] b) Self-healing en route: When a vehicle enters a long tunnel or underground parking garage, cellular signals are interrupted. At this time, the RFID tags on the transport container stop attempting to connect to the cloud and instead maintain interconnection with tags on adjacent containers, continuing to record data. The data is temporarily stored in the tag with the relatively strongest signal within the network through multiple relays between tags.

[0043] c) Network Recovery: After the vehicle leaves the signal blind spot, the vehicle gateway reconnects. The temporarily stored relay data is quickly synchronized to the gateway and then retransmitted to the cloud server. The cloud data stream presents a complete time series, with only a slight delay during the signal interruption and no data loss.

[0044] d) Reliable handover: Upon arrival at the distribution center, the receiving clerk verifies their identity via iris scanning and opens the package for scanning. The system automatically checks whether the temperature and humidity records throughout the process (including during signal interruptions) meet the standards and binds the receiving clerk's information to complete the digital signature. Implementation effect

[0045] On logistics routes with unstable communication environments, it achieves 100% data integrity for critical temperature and humidity, far exceeding the approximately 85% data integrity rate of traditional GPS temperature controllers.

[0046] The self-organizing network's self-healing function avoids the risk of rejecting entire batches of drugs due to data loss, and is estimated to reduce potential losses by millions of yuan annually.

[0047] It provides an immutable, fully reliable electronic traceability chain for drug regulation. Example 3

[0048] Precise control and credit linkage of hazardous chemicals in laboratories: Purpose of implementation: This enables precise control over the entire lifecycle of hazardous chemicals in laboratories, including procurement, storage, use, and disposal. It links individual operational behaviors with safety credit, dynamically adjusts permissions, and prevents safety risks.

[0049] Implementation System: It is deployed in a university chemistry laboratory and a platform of partner hazardous chemical suppliers.

[0050] Sensing layer: Intelligent reagent cabinets and intelligent waste liquid collection bins have built-in RFID and weighing sensors.

[0051] Trust Layer: Laboratories and procurement platforms are connected to two-factor biometric authentication (iris + palm vein).

[0052] Implementation steps: a) Trusted Procurement: Doctoral student Zhao needs to apply for the use of acetone. He submits his application within the system and completes iris recognition on a dedicated terminal. After verifying his project qualifications and training records (good credit), the system automatically generates an order for the cooperating supplier.

[0053] b) Precise Requisition: Acetone was delivered to the laboratory's intelligent reagent cabinet. Zhao used his palm vein unlocking method to retrieve it. The cabinet's RFID system automatically recorded the recipient's information: Zhao, time, reagent bottle ID, and weight retrieved.

[0054] c) Use and Disposal Association: After the experiment, Zhao is required to pour the waste liquid into the designated smart waste liquid collection bucket. The RFID on the bucket body scans the waste liquid label and associates it with Zhao's identity and the original reagent bottle ID.

[0055] d) Credit Dynamic Closed Loop: The system analyzes the closed loop of this operation: matching the application volume, usage volume, and waste liquid registration volume. This compliance result serves as positive feedback, improving Zhao's credit rating. If the system detects unregistered waste liquid, it will trigger an alert and downgrade his credit rating, potentially restricting his future application quota. Implementation effect

[0056] It has achieved full-process digital management of hazardous chemicals, making their source traceable, their destination trackable, and their responsibility accountable.

[0057] By dynamically linking personal safety credit with operating permissions, the reporting rate of potential laboratory safety incidents has increased by 70%.

[0058] This significantly reduced the manual workload for hazardous chemical inventory and compliance audits. Example 4

[0059] Real-time decision-making and simulation optimization for construction waste recycling: Purpose of implementation: Enhance the real-time fault response capability and production scheduling optimization level of the construction waste recycling center, and improve equipment utilization and output value through local intelligent decision-making and global simulation.

[0060] Implementation System: Deployed in a construction waste crushing and screening plant.

[0061] Analysis and decision-making level: Edge computing AI boxes are deployed next to key equipment such as crushers and sorters. The plant control center runs a 3D digital twin model.

[0062] Implementation steps: a) Real-time edge warning: The AI ​​box at the edge of the crusher continuously analyzes vibration and current data. One day, the model identifies abnormal characteristics in the vibration spectrum, immediately determines that the risk of hammer wear or breakage is increasing, flashes a red warning light on the local control panel, and suggests arranging preventive maintenance.

[0063] b) Global Simulation and Prediction: The plant's digital twin model simultaneously receives the early warning. The model immediately runs multiple contingency plans in the virtual space: 1) Immediate shutdown for 2 hours of maintenance; 2) Reduced load until the end of the shift before maintenance. The model simulation found that contingency plan 1 would result in the subsequent sorting line being idle, reducing the total daily processing volume by 15%. Although contingency plan 2 carries a small risk, it can complete the daily plan, and the model calculates that the purity of the finished aggregate can be maintained by fine-tuning the subsequent sorting parameters.

[0064] c) Decision Execution: The control center adopts the model-recommended plan 2 and issues adjustment instructions to the sorting machine's edge controller. The sorting machine dynamically adjusts the wind speed and screen amplitude based on the real-time material characteristics.

[0065] d) Results Feedback: This shift has ended. The equipment is running smoothly, the total processing capacity has met the target, and the purity of the finished aggregate has improved by 0.5% compared to the previous day. The entire event was recorded and used to optimize the parameters of the edge AI model and digital twin model. Implementation effect

[0066] The early warning of critical equipment failures has shifted from being discovered after the fact to being predicted in advance, reducing unplanned downtime by 60%.

[0067] By optimizing production scheduling through global simulation, the overall equipment utilization rate was increased by 18%, and the average selling price of recycled aggregates increased by 5%. Example 5

[0068] The Plastic Recycling Alliance's privacy-protecting collaboration and dynamic incentives: Purpose of implementation: Establish a collaborative mechanism among multiple independently operated plastic recycling companies that can share data value (enhancing overall credit and bargaining power) while strictly protecting their respective trade secrets (such as sources of goods, costs, and refined operational data), and achieve fair and transparent dynamic value distribution.

[0069] Implementation System: A consortium of five regional plastic recycling companies has been formed to jointly supply brands. Each company deploys an independent system, interconnected through a secure protocol.

[0070] Core: Employs a federated learning-based joint credit assessment model and a configurable value accounting rule engine.

[0071] Implementation steps: a) Building Credit with Privacy Protection: Brands want to assess the long-term stability of each recycler. The five companies are not required to upload any customer lists, detailed financial data, or employee performance reports. Each company uses only its own internal data to train an operational health model locally. Weekly, each company sends encrypted model updates (a set of numerical parameters) to a secure, neutral server.

[0072] b) Joint Model Optimization: A neutral server aggregates all encrypted updates, performs aggregate calculations, and generates a more robust global credit model. This new model is then returned to the five companies. This process is repeated, and the global model becomes increasingly accurate, but the raw data of any participating party never leaves its own server.

[0073] c) Dynamic Value Allocation: When an order arrives from a brand, the value conversion system automatically allocates the purchase share and premium according to preset rules. For example: Company A's average PET sorting purity this month reached 99.3% (Grade 1), with a base premium of 5%; its federated learning contribution ranked second, and its credit rating was A, resulting in an additional 2% bonus; the total premium is 7%. All rules and parameters are open and transparent to alliance members.

[0074] d) Dynamic adjustment of rules: When the country issues new carbon emission reduction accounting standards for recycled plastics, the alliance administrator can update the carbon credit factor parameters in the system with one click through the management interface, and all subsequent accounting will be executed immediately according to the new standards. Implementation effect

[0075] With zero risk of data breach, the alliance as a whole obtained a 20% premium in procurement from brand owners, which is something no single company could negotiate.

[0076] Transparent and dynamic incentive rules encourage member companies to proactively improve sorting quality and operational standardization, thus creating a healthy competitive ecosystem.

[0077] Systematic rule management enables the alliance to respond quickly to policy changes and maintain market competitiveness.

[0078] Comparative Example 1 Traditional paper-based and siloed system management model: Implementation scenario: A medium-sized manufacturing enterprise uses a traditional ERP system + paper documents + manual communication model to manage scrap steel recycling and procurement.

[0079] Implementation methods and problems: a) Data collection: Workshop workers manually fill out the "Daily Scrap Steel Production Report," relying on estimations for weight and making batch mixing easy. Data is entered into the computer one day late.

[0080] b) Identity and permissions: The warehouse uses keys and combination locks, and multiple people share the same account to log in to the ERP system, making it impossible to trace the responsible party in case of problems.

[0081] c) Collaboration and Decision-Making: When suppliers come to pick up goods, paper documents need to be manually checked, and the workshop needs to be contacted by phone for confirmation. At the end of the month, the finance department manually calculates the deductible amount based on the summary reports and purchase contracts, which is a time-consuming and labor-intensive process.

[0082] d) Security and Value: Companies and suppliers exchange Excel spreadsheets containing sensitive data via email. Carbon credit applications require dedicated personnel to study policies, compile year-round data, and submit a single application, resulting in a lengthy process and a high risk of errors.

[0083] Comparison results: Compared with traditional methods, the system of this invention exhibits revolutionary advantages: Efficiency: The value settlement cycle has been shortened from days / weeks to minutes / hours.

[0084] Trustworthiness: It has realized the transformation from people managing people and people managing data to the interconnection of things based on trusted identities and data speaking.

[0085] Security: It eliminates the risk of sensitive data leakage and enables secure collaboration where data remains stationary while its value is realized.

[0086] Intelligentization: Transforming human experience-based decision-making into predictive and optimization-based decision-making based on real-time data and artificial intelligence models.

[0087] Value Depth: Transforming the waste disposal process, which was originally seen as a cost and burden, into a new value creation link for enterprises to reduce costs, increase efficiency, and acquire carbon assets.

[0088] Compared to Examples 1-5 and Comparative Example 1, this invention clearly reveals the fundamental differences in their technical architecture and operating modes by juxtaposing five cross-industry examples with a traditional comparative example. In the traditional comparative example, the system exhibits typical characteristics of centralized silos: data relies on manual estimation and delayed entry, creating information gaps; identity verification is based on easily shared and easily impersonated static passwords; decision-making processes rely entirely on human experience and post-event responses, resulting in low efficiency and a high risk of errors; inter-enterprise collaboration has to come at the cost of exchanging raw data in plaintext, leading to a high risk of privacy leaks; and value accounting degenerates into lengthy manual reporting and financial reconciliation work, with a cycle of up to tens of days. In stark contrast, this invention constructs a new paradigm of distributed intelligent closed-loop: through passive RFID and self-organizing network technology, it achieves millisecond-level automatic data collection and uninterrupted synchronization throughout the entire lifecycle of materials from production to disposal, ensuring the originality, real-time nature, and integrity of the data; by integrating biometric features such as palm vein and iris scans, it strongly binds personnel identity, permissions, and dynamic credit ratings, achieving non-repudiable traceability and reliable quantifiable management of operational behavior; by utilizing edge computing and digital twin technology, it pushes intelligent analysis and decision-making capabilities down to the field, realizing pre-failure prediction of equipment failures and global simulation optimization of production scheduling, transforming passive response into proactive intervention; it innovatively adopts a federated learning model, achieving collaborative training and optimization of cross-enterprise credit models without the need to collect raw data, achieving secure collaboration where data remains stationary but value moves; finally, through automatic docking with the carbon credit system and the built-in two-factor dynamic accounting rules, it transforms the waste treatment process into visualized cost deductions and carbon asset benefits in real time and automatically, completing an instant closed loop of resource value.

[0089] The commercial effects of this paradigm shift are disruptive. Traditional models, as opposed to traditional ones, view waste management as a purely cost center and compliance burden, with a fragmented, ambiguous, and lagging value chain. The system of this invention, however, reshapes it into a highly efficient value creation center. Specifically, through end-to-end digitalization and automation, it compresses the resource value settlement cycle from tens of days to hours or even real-time, significantly improving capital turnover efficiency. Its tamper-proof and reliable traceability not only completely eliminates material loss and liability disputes but also builds core data assets for enterprises in a green supply chain, enhancing brand reputation and market credibility. Predictive maintenance and production optimization based on real-time data and artificial intelligence directly improve equipment utilization and product quality, resulting in significant cost reductions and added value. The federated learning collaboration model enables SMEs to gain greater market bargaining power and compliance credit through alliances while protecting trade secrets, changing the industry's competitive landscape. Ultimately, the system quantifies previously abstract environmental behaviors in real-time into financial gains that can directly offset procurement costs and be monetized in the carbon market, providing enterprises with built-in, sustainable economic incentives for practicing green development. In summary, this invention is not only a set of technical tools, but also an operational architecture that drives the supply chain toward a fundamental transformation toward transparency, trustworthiness, intelligence, and value co-creation.

[0090] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0091] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A collaborative management system for enterprise procurement supply chain, characterized in that, It includes a perception layer, a trust layer, an analysis and decision-making layer, a data security layer, and a value transformation layer that are connected in sequence. The perception layer is deployed at each node of the procurement supply chain to collect data on the flow and processing of renewable resource materials through passive RFID tags built into heterogeneous devices, and to perform data verification and synchronization through an inter-device self-organizing network. The trust layer is used to bind the operator's biometric information to their identity, operating permissions and initial credit rating, and to dynamically manage credit and cross-enterprise permissions based on their operating behavior and equipment feedback. The analysis and decision-making layer is used to analyze and issue early warnings on the perceived data through the edge computing module, and to simulate the value transformation path under different working conditions through the virtual mapping model; The data security layer is used to achieve collaborative optimization of cross-enterprise credit assessment models through a federated learning model, provided that participating enterprises retain their data locally. The value conversion layer is used to connect with external carbon credit systems and, based on perceived data and personnel credit ratings, outputs cost deductions and carbon credit monetization amounts through preset accounting rules.

2. The enterprise procurement supply chain collaborative management system as described in claim 1, characterized in that, In the sensing layer, passive RFID tags store equipment operating status parameters, material compatibility parameters, and communication protocols; the collected data is accompanied by time sequence and node markers, and is associated with the corresponding operator identification and equipment verification results to form a traceable data chain.

3. A collaborative management system for enterprise procurement supply chain as described in claim 2, characterized in that, The device-to-device self-organizing network in the perception layer has a self-healing function. When the communication of any node in the network is interrupted, its data can be relayed through adjacent nodes to ensure the continuity of critical data flow.

4. The enterprise procurement supply chain collaborative management system as described in claim 1, characterized in that, The biometric information collected by the trusted layer includes palm vein features and / or iris features; dynamic management specifically involves: collecting personnel operation behavior data and equipment verification results in real time, using them as input to dynamically update their credit rating, and adjusting their cross-enterprise operation permissions and transaction credit limits accordingly.

5. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, In the analysis and decision-making layer, the edge computing module is deployed locally at the supply chain node. By running edge computing algorithms, it can independently identify at least one of the following: equipment component wear, material purity deviation, and material compatibility anomaly, and trigger an alert.

6. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, The virtual mapping model is constructed based on the equipment layout, parameters, and flow rules of physical nodes. It maintains real-time synchronization with the physical nodes and is used to simulate and predict new operating conditions after changes in suppliers, equipment, or environmental policies.

7. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, The data security layer adopts a horizontal federated learning model, in which each participating enterprise only sends the parameter gradient of the credit assessment model to the coordinator for aggregation and optimization, and all raw data is stored on local servers.

8. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, The preset accounting rule for the value conversion layer is a two-factor rule. The first factor is a tiered deduction rule based on the waste resource utilization compliance rate, and the second factor is a fixed value superposition rule based on personnel credit rating. The tiered standard and superposition value can be dynamically configured according to policy updates.

9. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, Two-way binding verification is achieved between the perception layer and the trust layer: the passive RFID tag in the perception layer is uniquely bound to the biometric information of the authorized operator in the trust layer; the device operation record is sent back to the trust layer as feedback data to correct the credit rating of the corresponding person.

10. A collaborative management system for enterprise procurement supply chain as described in claim 1, characterized in that, Heterogeneous equipment includes at least two of the following: waste treatment equipment, recycled material processing equipment, cold chain transportation equipment, and agricultural harvesting equipment; the data acquisition fields and internal verification logic configured on the passive RFID tags corresponding to different types of equipment are different.

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