Clinical nutrition preparation supply chain full-link flow traceability management system and clinical nutrition preparation supply chain full-link flow traceability management method

Abstract

The invention provides a clinical nutrition preparation supply chain full-link flow traceability management system and method, and the system comprises a clinical nutrition preparation supply chain full-link flow traceability management system. The system comprises system configuration, basic data setting, nutrition preparation operation management, nutrition diagnosis and treatment management, an out-of-hospital platform, a nutrition rehabilitation big data platform, a nutrition quality control workbench and a data verification module. The invention further comprises a traceability management method suitable for the system, the method comprises the steps of basic data setting, order processing, inventory management, charging, distribution, signing and the like, and the mechanisms of inventory early warning, data evidence storage and the like are involved. The technical effects of effectively tracing and managing the whole link flow of the clinical nutrition preparation supply chain, realizing data intercommunication, real-time updating and multi-dimensional analysis, monitoring flow compliance and guaranteeing data accuracy and integrity are achieved.

Description

Technical Field

[0001] This invention relates to the field of medical supply chain management and data traceability technology, and in particular to a full-chain traceability management system and method for clinical nutrition preparation supply chain. Background Technology

[0002] In medical institutions, the supply chain management of clinical nutrition preparations is a data-intensive process involving multiple stages such as access approval, procurement, warehousing, clinical use, and patient delivery.

[0003] Currently, there are the following technical shortcomings in processing this process data: Data silos and latency issues: Existing technologies typically employ independent, single-function software systems (such as standalone inventory management systems or electronic medical record systems) or simply add functions to hospital HIS systems. These systems lack standardized data interfaces (APIs) and unified data models, resulting in data being unable to be synchronized and effectively transferred between different computer systems in real time, creating "data silos." When cross-system information queries are needed (e.g., looking up procurement batches based on clinical consumption), manual export and comparison are required, leading to high data processing latency and poor consistency.

[0004] The passivity and high error rate of process-driven systems: Existing systems are mostly passive information recording tools. Process progression relies on manual instructions; for example, a warehouse clerk manually notifies the purchasing department after discovering insufficient inventory. This process-driven approach, based on manual judgment and information transmission, manifests in computer systems as non-real-time data updates and high latency in process triggering. Furthermore, data errors are highly likely to occur during manual data entry and transmission, lacking system-level automated verification and closed-loop feedback mechanisms.

[0005] Weak data traceability and susceptibility to tampering: Traditional database recording methods, while capable of storing data, suffer from fragile traceability links. When problems arise (such as formulation quality issues) requiring retrospective analysis, the query process is complex, and the data records in the database are at risk of being maliciously or accidentally modified. Therefore, it is technically impossible to provide a tamper-proof and highly reliable data evidence chain.

[0006] Therefore, designing a new computer system architecture to solve the aforementioned technical problems of data processing latency, silos, passive triggering, and low traceability reliability is a technical challenge that urgently needs to be addressed in this field. Summary of the Invention

[0007] This invention solves the problem of data silos between heterogeneous systems and proposes a full-chain traceability management system and method for the clinical nutrition preparation supply chain.

[0008] To achieve the above objectives, the following technical solution is proposed: A full-chain traceability management system for clinical nutrition preparation supply chains includes: The system configuration module is used to set basic information for other modules; The basic data settings module is used to store the basic data required for the operation of medical special foods; The nutritional preparation operation management module is used to control each node in the operation process; The nutrition diagnosis and treatment management module is used to record key node data and process medical documents; An external platform is used to provide interactive interfaces for multiple terminals; A nutritional rehabilitation big data platform for multi-dimensional data analysis; Nutrition quality control workbench is used to monitor process compliance; The data verification module is used to compare key data with basic data.

[0009] Preferably, the nutritional preparation operation management module includes a preparation access management submodule, a procurement management submodule, a centralized distribution submodule, an warehousing and storage submodule, a goods information submodule, an order forecast submodule, a billing submodule, an order delivery submodule, a delivery and receipt submodule, and a repurchase submodule, and the data of each submodule is interconnected.

[0010] Preferably, the nutrition diagnosis and treatment management module includes a report management unit and an outpatient and inpatient diagnosis and treatment management unit. The report management unit updates key node data in real time, and the outpatient and inpatient diagnosis and treatment management unit processes structured medical documents.

[0011] Preferably, the basic data setting module includes basic information on medical special foods, supplier information, routine concepts and calculation formulas for nutritional diagnosis and treatment, food composition table, nutrient intake standards and assessment scales.

[0012] Preferably, the off-site platform also includes a system operation module and a medical specialty food mall.

[0013] A method for full-chain traceability management of clinical nutrition preparation supply chains includes the following steps: S1. The system's basic data setting module receives basic data input by the administrator through the terminal. The access management module receives procurement intention data reported from terminals of various departments. After aggregation, it generates an access data list to be approved. The procurement management sub-module generates a digital procurement plan based on the access list data. After signing a contract with a supplier, the contract information is associated with the supplier ID and stored in the database. S2. When the contracted service provider delivers the goods, the warehouse manager scans the barcode on the accompanying delivery note to verify the goods for storage. After the verification is successful, data storage based on hash chain is performed, a genesis record is generated and the hash value is calculated and stored in the digital storage chain. S3. Clinical department terminals issue preparation usage orders through the nutrition diagnosis and treatment management module. The order processing module parses the orders and sends out delivery instructions to the inventory sub-module. The inventory sub-module updates the inventory data and triggers the dynamic inventory threshold prediction calculation engine that integrates multi-source data in real time to recalculate the dynamic inventory threshold. S4. The order processing module publishes a pending billing event. After the billing submodule subscribes to the event, it communicates with the billing system of the hospital HIS system through the standard interface, obtains the billing voucher, and associates it with the digital evidence storage chain. After confirming that the billing is successful, the order processing module publishes a "pending delivery" event and generates a task. After the delivery personnel pick up the preparation and scan it, the system updates the order status to "delivering". S5. When staff scan the code to sign for the package, they verify the patient's wristband and order information. After verification, the system receives the final confirmation signal, generates the most important block on the digital evidence chain, and generates the final hash value, completing the entire process of data binding.

[0014] Preferably, in step S3, if the current inventory is lower than the threshold, the cross-module asynchronous communication and automated triggering mechanism based on the event bus is automatically triggered to issue an "inventory warning" event containing the formulation ID and the recommended purchase quantity to the event bus.

[0015] Preferably, in step S3, if the current inventory is lower than the threshold, an outbound record is added to the digital evidence chain of the corresponding batch. The added outbound record includes the outbound department, operator, time, and hash value of the previous block, and a new hash value is generated.

[0016] Preferably, in step S4, if the HIS system returns a billing success response message and a failure response message, the billing submodule updates the billing status of the order accordingly.

[0017] Preferably, the change in order status and the delivery person's ID are recorded in a new block of the digital evidence chain.

[0018] The beneficial effects of this invention are: Improved real-time performance of data processing and system response speed: Through event-driven architecture, the process that originally required manual serial processing was transformed into parallel and asynchronous communication between system modules, realizing the automated triggering of key business processes and significantly reducing the data processing delay from demand generation to system response.

[0019] Improved the accuracy and intelligence of supply chain management forecasts: By integrating multi-source heterogeneous data such as historical consumption, real-time clinical needs and supplier delivery cycles to build a dynamic inventory forecasting model, inventory warnings and procurement decisions have been transformed from passive experience-based judgments to proactive data-driven forecasts, thereby improving the accuracy of inventory turnover and material support.

[0020] Enhanced security and traceability of critical data: By introducing a hash chain-based data storage method, tamper-proof technical protection is provided for data at every critical node in the supply chain. In the event of a security incident, a highly reliable and rapidly verifiable digital evidence chain can be provided, fundamentally solving the technical pain points of traditional database records being easily modified and difficult to trace.

[0021] This invention achieves a closed-loop and interconnected business data process: It seamlessly integrates in-hospital supply chain operations, clinical diagnosis and treatment, and out-of-hospital patient rehabilitation management into a unified data platform, breaking down data silos and realizing a closed-loop data process from the supply end of the preparation to the consumption end of the patient, providing a complete data foundation for refined operations and clinical effect analysis. Attached Figure Description

[0022] Figure 1 This is a system structure diagram of the present invention.

[0023] Figure 2 for Figure 1 Structure diagram of the operation and management module for Chinese nutritional preparations.

[0024] Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments are only possible technical implementations of the present invention, but are not limited thereto. Those skilled in the art can obtain other embodiments without creative effort by combining them with the embodiments of the present invention, which are also within the protection scope of the present invention. This application provides a full-link traceability management system and method for the supply chain of clinical nutritional preparations. It mainly adopts the construction of a full-link traceability management system and method, which achieves the effect of improving the efficiency and accuracy of clinical nutritional preparation supply chain management. The following is a further detailed description of this application. Example 1: The clinical nutrition preparation supply chain end-to-end traceability management system provided in this application includes a system configuration module, a basic data setting module, a nutrition preparation operation management module, a nutrition diagnosis and treatment management module, an off-site platform, a nutrition rehabilitation big data platform, a nutrition quality control workbench, and a data verification module. The system configuration module sets the basic information for other modules; the basic data setting module stores the basic data required for the operation of medical special foods; the nutrition preparation operation management module manages each node in the operation process; the nutrition diagnosis and treatment management module records key node data and processes medical documents; the off-site platform provides multi-party interactive interfaces; the nutrition rehabilitation big data platform performs multi-dimensional data analysis; the nutrition quality control workbench monitors process compliance; and the data verification module compares key data with basic data. This achieves end-to-end traceability management of the clinical nutrition preparation supply chain, improving management efficiency and accuracy. This is because each module has a clear division of labor and collaborates with each other, enabling comprehensive management and monitoring of all aspects of the supply chain, avoiding problems such as data errors and poor communication in traditional management methods. Specifically, the system configuration module includes a server and configuration software. The server can be a high-performance commercial server or a cloud server to provide stable computing and storage capabilities. The configuration software can be a web-based application, allowing administrators to operate it via a browser. The server connects to other modules via a network, and the configuration software can set and adjust basic information such as parameters and permissions for other modules. For example, administrators can use the configuration software to set access permissions for each sub-module in the nutritional preparation operation management module, ensuring that only authorized personnel can operate it. The basic data setting module includes a database and a data entry interface. The database can use a relational database, such as MySQL or Oracle, to ensure structured data storage and efficient retrieval. The data entry interface can be a web form or a desktop application, allowing administrators to input basic data such as basic information on medical special foods, supplier information, routine concepts and calculation formulas for nutritional diagnosis and treatment, food composition tables, nutrient intake standards, and assessment scales. This data forms the foundation for the entire system's operation, providing accurate data for subsequent operation and treatment management. For example, during nutritional diagnosis and treatment, doctors can develop reasonable nutritional plans for patients based on food composition tables and nutrient intake standards. The nutritional preparation operations management module includes sub-modules for preparation access management, procurement management, centralized distribution, warehousing, goods information, order forecasting, billing, order delivery, delivery and receipt, and repeat purchase. Data is interconnected among these sub-modules. The preparation access management sub-module includes an access review system and data acquisition equipment. The access review system can be a rule-based review software that reviews procurement intention data according to preset access standards.Data acquisition devices, such as barcode scanners and RFID readers, are used to collect information related to the formulation. The procurement management submodule includes procurement plan generation software and a supplier management system. The procurement plan generation software generates digital procurement plans based on the access list data and inventory status. The supplier management system manages supplier information, contracts, etc. The centralized distribution submodule includes distribution scheduling software and logistics tracking equipment. The distribution scheduling software performs reasonable distribution scheduling based on order information and vehicle availability. The logistics tracking equipment tracks the location and status of delivery vehicles in real time. The warehousing and storage submodule includes a warehouse management system and inventory monitoring equipment. The warehouse management system manages the warehousing, storage, and outbound processes of the formulation. The inventory monitoring equipment monitors inventory quantity and status in real time. The goods information submodule includes information entry devices and an information query system. The information entry devices input detailed information about the formulation into the system. The information query system allows users to easily query relevant information about the formulation. The order forecasting submodule includes forecasting analysis software and a data interface. The forecasting analysis software predicts future order conditions based on historical order data and market demand. The data interface allows data interaction with other systems. The billing submodule includes a billing rules engine and a payment interface. The billing rules engine calculates charges based on order information and billing standards. The payment interface communicates with the hospital's HIS (Hospital Information System) billing system to complete payment settlement. The order delivery submodule includes order allocation software and a delivery personnel management system. The order allocation software assigns orders to appropriate delivery personnel. The delivery personnel management system manages delivery personnel information and tasks. The delivery and receipt submodule includes a receipt confirmation device and a data upload interface. The receipt confirmation device allows staff to confirm receipt. The data upload interface uploads the receipt data to the system. The repeat purchase submodule includes repeat purchase reminder software and a customer relationship management system. The repeat purchase reminder software reminds patients to make repeat purchases based on their usage and historical order data. The customer relationship management system manages patient information and purchase records. These submodules work together to achieve end-to-end management of nutritional preparations from initial access to repeat purchases. For example, when a clinical department needs to purchase nutritional preparations, the preparation access management submodule reviews the purchasing intention. Once approved, the purchasing management submodule generates a purchasing plan, which is then distributed through the centralized distribution submodule. The warehousing and storage submodule manages the warehousing, the goods information submodule records preparation information, the order forecasting submodule predicts order status, the billing submodule calculates charges, the order delivery submodule handles delivery, the delivery and receipt submodule completes the receipt process, and the repeat purchase submodule reminds patients to repurchase. The nutrition and treatment management module includes a report management unit and an outpatient and inpatient treatment management unit. The report management unit updates key node data in real time, while the outpatient and inpatient treatment management unit processes structured medical documents. The report management unit includes a data collector and report generation software.The data acquisition unit can collect key node data from various modules, such as order quantity, inventory quantity, and patient information. Report generation software can generate various reports based on the collected data, such as statistical reports and analytical reports. The outpatient and inpatient treatment management unit includes an electronic medical record system and medical document templates. The electronic medical record system records patient medical information, facilitating diagnosis and treatment by doctors. Medical document templates standardize the format and content of medical documents, improving their quality and processing efficiency. For example, during outpatient or inpatient treatment, doctors can use the outpatient and inpatient treatment management unit to process structured medical documents, while the report management unit updates relevant data in real time, providing decision support for doctors. The off-site platform includes a system operation module and a medical specialty food marketplace. The system operation module includes operation management software and a user feedback system. The operation management software manages the operation of the off-site platform, such as user management and activity management. The user feedback system collects user opinions and suggestions for platform optimization and improvement. The medical specialty food marketplace includes a product display interface and an online payment system. The product display interface showcases information and images of various medical specialty foods. The online payment system allows users to make payments online, facilitating the purchase of medical specialty foods. For example, patients can purchase necessary nutritional supplements through an off-site platform's medical specialty food mall, while the system's operations module manages and monitors the mall's operation. The nutrition rehabilitation big data platform includes a data warehouse and data analysis tools. The data warehouse stores large amounts of data from various modules, such as order data, patient data, and treatment data. The data analysis tools perform multi-dimensional analysis of the data in the data warehouse, such as statistical analysis, correlation analysis, and predictive analysis. Through data analysis, potential problems and optimization points in the supply chain can be identified, providing a basis for decision-making. For example, analyzing patients' nutritional rehabilitation data can reveal the effects of different nutritional supplements, providing a reference for clinical treatment. The nutrition quality control workbench includes monitoring software and an early warning system. The monitoring software can monitor the compliance of the supply chain processes in real time, such as whether the procurement process complies with regulations and whether delivery times are on time. The early warning system can issue timely alerts when violations are detected, reminding managers to take action. For example, when the nutrition quality control workbench detects an abnormal purchase price for a batch of nutritional supplements, it will issue an early warning, reminding managers to investigate. The data verification module includes a comparison algorithm and a data interface. The comparison algorithm compares key data with basic data, such as order information versus inventory information, patient information versus supplier information, etc. The data interface allows data interaction with other modules to obtain the data to be compared. Data verification ensures the accuracy and consistency of the data. For example, during warehousing, the data verification module compares the information on the accompanying shipping document with the basic data to ensure the accuracy of the formulation information.The implementation principle of this embodiment is as follows: Through the collaborative work of various modules, the system achieves traceability management of the entire supply chain of clinical nutritional preparations. The system configuration module provides basic settings for other modules; the basic data setting module stores the basic data required for operation; the nutritional preparation operation management module controls the operation process; the nutritional diagnosis and treatment management module records key data and processes medical documents; the external platform provides multi-party interactive interfaces; the nutritional rehabilitation big data platform performs multi-dimensional data analysis; the nutritional quality control workbench monitors process compliance; and the data verification module ensures data accuracy. These interconnected modules form a complete management system, improving the efficiency and accuracy of supply chain management, avoiding many problems in traditional management methods, and providing strong support for the supply and management of clinical nutritional preparations.

[0026] Example 2: A method for full-chain traceability management of clinical nutrition preparation supply chains includes the following steps: S1. The system's basic data setting module receives basic data input by the administrator through the terminal, including basic preparation information, supplier information, nutritional diagnosis and treatment calculation formulas, etc., and stores it in a structured manner in the database. The access management module receives procurement intention data reported from terminals of various departments, aggregates it, and generates a list of access data to be approved.

[0027] The procurement management submodule generates a digital procurement plan based on the admission list data, including budget and quantity. After signing a contract with a supplier, the contract information is associated with the supplier ID and stored in the database. When a delivery order instruction is received, the system sends a structured data message containing order details to the supplier's computer system via a network interface.

[0028] S2. The contracted service provider delivers the goods. The warehouse manager scans the barcode on the accompanying delivery note for inbound storage verification. The inbound storage submodule receives the data and automatically compares and verifies it with the purchase order data. After successful verification, data notarization based on a hash chain is executed, generating a genesis record containing information such as batch number, expiration date, supplier ID, and original order number, and calculating its hash value to store it in the digital notarization chain list for that batch.

[0029] Regarding the lightweight key data anti-tampering and traceability method based on hash chains, this invention introduces a data storage technology based on hash chains to address the problem that traceability information is easily tampered with in existing technologies.

[0030] Technical Implementation: In the system database, an independent "digital evidence chain" is established for each formulation batch that requires strong traceability.

[0031] Genesis Record: When a formulation is put into storage, the system collects key information of the batch (such as batch number, supplier ID, production date, storage time, operator ID), packages this information into a data block, calculates its hash value (Hash_0), and stores it in a linked list as the "genesis record".

[0032] Chained appending: Subsequently, for each critical status change of this batch of preparations (e.g., outbound to department A, prescription issued by doctor B, delivery to patient C), the system generates a new data block. This data block not only contains information about the current operation but must also contain the hash value (Hash_n-1) of the previous data block. Then, the hash value (Hash_n) of the entire current data block is calculated and stored together.

[0033] Workflow and Verification: When tracing is required, the system starts from the latest record and recursively verifies whether the hash value of each data block matches its content and the hash value of the previous node. If the content of any historical data block is modified, its hash value will change, causing a mismatch with the hash value stored in the next node. The verification chain will be immediately broken, and the tampering will be detected instantly.

[0034] Technical effect: This method achieves immutability and high reliability traceability of key flow data with low computational and storage costs, effectively solving the data traceability vulnerability problem in the background technology.

[0035] S3. Clinical department terminals issue preparation usage orders through the nutrition diagnosis and treatment management module. The order processing module parses the orders and sends outbound instructions to the inventory sub-module. The inventory sub-module updates the inventory data and triggers the dynamic inventory threshold prediction calculation engine that integrates multi-source data in real time to recalculate the dynamic inventory threshold.

[0036] Regarding dynamic inventory threshold prediction models and methods that integrate multi-source data, existing inventory early warning technologies mostly use static thresholds. This invention proposes a dynamic and intelligent early warning model.

[0037] Technical Implementation: The system incorporates a predictive computing engine. The engine's server executes a predictive algorithm periodically or triggered by events. The input to this algorithm is not a single inventory level, but rather a fusion of at least three types of heterogeneous data: Historical consumption data: Consumption rate data of this preparation in the same period in the past, retrieved from its own database.

[0038] Real-time clinical demand data: Data on the number of newly admitted patients who meet the indications for use of this preparation and the estimated dosage, which are retrieved in real-time or near real-time from the hospital's HIS / EMR system via API interface.

[0039] Supplier data: Average lead time data for this formulation supplier stored in the base database.

[0040] Workflow: The prediction engine calculates the dynamic safety stock threshold using a preset weighted algorithm (e.g., dynamic threshold = (historical average daily consumption * α + real-time clinical estimated increment * β) * supply cycle * safety factor).

[0041] Technical effects: Compared with static thresholds, this method can predict future material consumption in advance and accurately based on real and dynamically changing clinical needs, realizing a technological shift from "passive replenishment" to "proactive stocking", and significantly improving the intelligence level and accuracy of computer systems in inventory management.

[0042] If the current inventory is below the threshold, the cross-module asynchronous communication and automated triggering mechanism based on the event bus will be automatically triggered to publish an "inventory warning" event containing the preparation ID and the recommended purchase quantity to the event bus, and an outbound record will be added to the digital evidence chain of the corresponding batch. The added outbound record includes the outbound department, operator, time and hash value of the previous block, and a new hash value will be generated.

[0043] Regarding the cross-module asynchronous communication and automatic triggering mechanism based on the event bus, this invention is not a simple superposition of functional modules. Its core innovation lies in the design of an event-driven architecture (EDA).

[0044] Technical implementation: The system deploys a central message queue (such as RabbitMQ or Kafka) as an event bus. Each business sub-module (such as inventory, diagnosis, and procurement) acts as a producer and consumer.

[0045] Workflow: When the inventory module's server detects that the inventory data for a certain preparation is below a dynamic threshold, it no longer simply displays a warning on the interface, but proactively publishes a structured "Inventory Warning" event message to the event bus. The procurement module's server, as a subscriber to this event, will immediately receive this message and automatically execute preset business logic, such as generating a draft purchase order data object and triggering notifications to the computer terminals of relevant personnel.

[0046] Technical benefits: This design transforms the process that originally required manual connection into asynchronous communication and automated triggering between modules within the system, which greatly reduces the latency of data processing, avoids errors and omissions that may occur when information is manually transmitted, and solves the problem of passive data processing that relies on manual driving in existing technologies.

[0047] S4. Simultaneously or subsequently, the order processing module publishes a pending billing event. The billing submodule subscribes to this event, receives data containing order details and patient ID, and communicates with the hospital's HIS billing system via a standard interface (such as HL7) to perform the deduction operation. If the HIS system returns a successful billing response or a failed response, the billing submodule updates the order's billing status accordingly, obtains the billing voucher (ID or summary), and associates it as a new block with the corresponding batch's digital evidence chain.

[0048] After confirming successful billing, the order processing module publishes a "pending delivery" event and generates a task. The delivery personnel subscribe to this event on their mobile terminal device, collect the preparation and scan it, and receive a task containing data such as the delivery destination and patient information. The system updates the order status to "delivering". The change in order status and the delivery personnel's ID are recorded in a new block of the digital evidence chain.

[0049] S5. After the delivery personnel deliver the preparation to the ward, the staff will scan the code to sign for the delivery using their workstation terminal or mobile terminal to verify the patient's wristband and order information. After verification, the system will receive the final confirmation signal of "delivery completed" and generate the most important block on the digital evidence chain. This block strictly contains the final patient ID, the signing nurse ID, the precise delivery time, and the hash value of the previous block, and generates the final hash value. At this point, this single preparation in this batch has completed the tamper-proof full-process data binding from warehousing to the specific patient's use.

[0050] When the HIS system transmits a patient's "discharge" event via an interface, the system's external platform automatically subscribes to this event and adds the patient to the discharge follow-up management list. The system pushes nutritional rehabilitation guidance to the patient through an external workbench (such as a patient app or mini-program) and provides an online consultation portal. When a patient initiates a repurchase request through the medical special food mall on the external platform, the system generates a new sales order linked to their historical hospitalization ID and initiates a new, simplified "outbound-delivery-receipt" process, similarly establishing and recording an independent digital evidence chain for the new batch of goods.

[0051] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A clinical nutrition formulation supply chain end-to-end process traceability management system, characterized in that, The system comprises: a system configuration module for setting basic information of other modules; a basic data setting module for storing basic data required for the operation of medical special food; a nutritional preparation operation management module for controlling various nodes in the operation process; a nutritional diagnosis and treatment management module for recording key node data and processing medical documents; an off-site platform for providing an interactive interface for multiple parties; a nutritional rehabilitation big data platform for multidimensional data analysis; a nutritional quality control workstation for monitoring process compliance; and a data verification module for comparing key data with basic data.

2. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 1, wherein, The nutritional preparation operation management module comprises a preparation access management submodule, a procurement management submodule, a centralized distribution submodule, a warehouse storage submodule, a cargo information submodule, an order forecasting submodule, a billing submodule, an order distribution submodule, a delivery and receipt submodule, and a repeat purchase submodule, and the data of each submodule is interconnected.

3. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 2, wherein, The nutritional diagnosis and treatment management module comprises a report management unit and an outpatient and inpatient diagnosis and treatment management unit, the report management unit updates key node data in real time, and the outpatient and inpatient diagnosis and treatment management unit processes structured medical documents.

4. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 3, wherein, The basic data setting module includes medical special food basic information, supplier information, nutritional diagnosis and treatment routine concepts and calculation formulas, food composition table, nutrient intake standard and evaluation scale.

5. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 1, wherein, The off-site platform further comprises a system operation module and a medical special food mall.

6. A method for managing process traceability of a clinical nutrition preparation supply chain throughout the entire chain, characterized in that, The clinical nutritional preparation supply chain whole-link process traceability management system according to any one of claims 1 to 5 comprises the following steps: S1, the basic data setting module of the system receives the basic data input by the administrator through the terminal, the access management module receives the procurement intention data reported from each department terminal, aggregates the data to generate a list of access data to be approved, and the procurement management submodule generates a digital procurement plan based on the access list data. After signing a contract with the supplier, the contract information and the supplier ID are associated and stored in the database; S2, the service provider delivers the goods, the warehouse administrator scans the barcode on the goods accompanying single for warehouse storage verification, and after verification, performs data storage based on the hash chain, generates a genesis record and calculates the hash value and stores it in the digital storage chain table; S3, the clinical department terminal issues a preparation use order through the nutritional diagnosis and treatment management module, the order processing module analyzes the order and sends a warehouse-out instruction to the inventory submodule, the inventory submodule updates the inventory data and triggers a dynamic inventory threshold prediction operation engine that fuses multiple source data in real time to recalculate the dynamic inventory threshold; S4, the order processing module issues a billing event, the billing submodule subscribes to the event and communicates with the billing system of the hospital HIS system through a standard interface, associates the billing voucher to the digital storage chain table after obtaining the billing voucher, and the order processing module issues a "to be distributed" event and generates a task after confirming the successful billing. The staff receives the preparation and scans it, and the system updates the order status to "in distribution"; S5, the staff scans the code and signs for receipt, checks the patient wristband and order information, and after checking that there is no error, the system receives the final confirmation signal, generates a most important block on the digital storage chain table, and generates a final hash value, completing the whole-process data binding.

7. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 6, wherein, In step S3, if the current inventory is lower than the threshold, an event bus-based cross-module asynchronous communication and automatic triggering mechanism is automatically triggered, and a "stock warning" event containing the preparation ID and the recommended purchase quantity is published to the event bus.

8. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 6 or 7, characterized in that, In step S3, if the current inventory is lower than the threshold, a delivery record is added to the digital chain table of the corresponding batch, the added delivery record includes the delivery department, the operator, the time, and the hash value of the previous block, and a new hash value is generated.

9. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 6, wherein, In step S4, if the HIS system returns a response message indicating that the charging is successful and a response message indicating that the charging fails, the charging submodule updates the charging state of the order accordingly.

10. The clinical nutrition formulation supply chain end-to-end process traceability management system according to claim 6, wherein, The change of the order state and the ID of the delivery person are recorded in the new block of the digital chain table.

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