Co-medical multi-hospital collaborative material purchasing and supply chain management method, platform, equipment and medium

Abstract

The invention provides a medical multi-hospital collaborative material purchasing and supply chain management method, platform, equipment and medium. The method comprises the following steps: S1, demand integration: collecting material purchasing demand information of hospitals in a medical system in real time and integrating the material purchasing demand information; s2, demand analysis: based on historical purchase data and material consumption conditions, generating a unified purchase plan through a demand prediction model, and based on big data analysis, generating a batch purchase scheme; s3, intelligent matching: dynamically matching proper suppliers through a price comparison algorithm according to the batch purchase scheme; s4, centralized purchase execution: submitting a batch purchase scheme to a supplier to simplify an intermediate link; s5, supply chain monitoring: monitoring the transportation, inventory and distribution states of the supply chain in real time; in the process, data sharing, transparent management and feedback optimization closed loop are implemented. Through a four-in-one technical architecture of platform integration, an intelligent algorithm, block chain transparency and closed-loop optimization, the core pain point in co-medical multi-hospital collaborative purchase is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical community material procurement, and in particular to a method, device, equipment and medium for collaborative material procurement and supply chain management of multiple hospitals in a medical community. Background Art

[0002] With the rapid development of the medical industry, medical communities, as a new form of medical service organization, have played an important role in improving the quality of medical services and optimizing resource allocation. However, most medical communities currently use traditional decentralized procurement models, relying on manual coordination or basic information tools, and lack an intelligent platform for cross-hospital collaboration. Currently, medical community material procurement and supply chain management have the following shortcomings:

[0003] 1. Serious information silos: Hospital procurement needs are scattered across their respective systems, mostly managed separately by different departments or personnel. There is a lack of a unified data platform, making real-time sharing impossible, leading to delayed decision-making.

[0004] 2. Insufficient resource sharing and inefficient resource integration: The procurement needs of different hospitals have not been fully integrated, and some hospitals have duplicate procurement, resulting in waste of resources. At the same time, the procurement needs of some small hospitals are small, and they are unable to centrally purchase with other hospitals, thus missing the opportunity to reduce procurement costs.

[0005] 3. Complex intermediate links: There are multiple intermediate links in the procurement process, including communication and coordination between hospitals and suppliers, order confirmation, transportation and distribution, etc. These links increase procurement costs and time, resulting in inefficient resource utilization.

[0006] 4. Inadequate supply chain management: In the supply chain management process, the transmission and connection of information are not accurate enough, resulting in untimely matching of supply and demand, unscientific inventory management, and frequent shortages or surpluses of medicines or consumables. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a method, platform, equipment and medium for collaborative material procurement and supply chain management of multiple hospitals in a medical community. Through the four-in-one technical architecture of "platform integration + intelligent algorithm + blockchain transparency + closed-loop optimization", the core pain points in the collaborative procurement of multiple hospitals in the medical community are solved, and system-level innovations such as efficient resource utilization, precise cost control, and reliable information sharing are achieved, providing a scalable intelligent solution for medical supply chain management.

[0008] In a first aspect, the present invention provides a method for collaborative material procurement and supply chain management among multiple hospitals in a medical community, comprising the following steps:

[0009] S1. Demand integration: Real-time collection and integration of material procurement demand information from hospitals within the medical community;

[0010] S2. Demand Analysis: Based on historical procurement data and material consumption, a unified procurement plan is generated through demand forecasting models, and batch procurement plans are generated based on big data analysis.

[0011] S3. Intelligent Matching: Based on the bulk purchasing plan, we use a price comparison algorithm to select the best quote and dynamically match suitable suppliers.

[0012] S4. Centralized Procurement Execution: Submit bulk procurement plans to suppliers and directly connect with them;

[0013] S5. Supply chain monitoring: Real-time monitoring of supply chain transportation, inventory, and distribution status;

[0014] In the above process, the following are also implemented:

[0015] Data sharing and transparent management: Unified and open access to each hospital's procurement needs, inventory information, and procurement process data enables real-time cross-hospital data sharing and transparency throughout the supply chain.

[0016] Feedback optimization closed loop: Receive feedback from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed loop.

[0017] Furthermore, the demand forecasting model in S2 is a combined model based on time series analysis and machine learning algorithms;

[0018] The generation of the procurement plan specifically includes the following steps:

[0019] a. Use LSTM neural network to predict the trend of historical procurement data and obtain the prediction results;

[0020] b. Taking into account the cyclical characteristics of hospital material consumption, the ARIMA model is used to modify the forecast results;

[0021] c. Dynamically adjust forecast weights based on real-time updated demand data to generate a unified procurement plan;

[0022] Furthermore, in S2, generating the batch purchase plan specifically includes the following steps:

[0023] a. Build a multi-dimensional scoring system based on suppliers' historical fulfillment rates, quotation volatility, and delivery timeliness;

[0024] b. Based on a hybrid optimization model of greedy algorithm and genetic algorithm, select the supplier combination with the highest comprehensive score;

[0025] c. Automatically match the tiered pricing rules based on the purchase scale to generate the bulk purchase plan.

[0026] Furthermore, the price comparison algorithm in S3 is a multi-factor weighted scoring model, including the following parameter weights: supplier quotation weight, historical delivery on-time rate weight, after-sales service score weight, and geographical location distribution cost weight; ultimately, the highest weighted total score is selected as the optimal quotation, and the appropriate supplier is dynamically matched.

[0027] Furthermore, the data sharing is achieved through blockchain technology, specifically including:

[0028] a. Store each hospital’s procurement needs, inventory information, and procurement process data on the blockchain in real time;

[0029] b. Automatically trigger inventory transfer instructions through smart contracts to ensure data cannot be tampered with and is fully traceable;

[0030] c. Each hospital node authorizes access to on-chain data through private key authorization, enabling real-time sharing of data across hospitals.

[0031] Furthermore, the transparent management specifically includes:

[0032] Upload order generation, supplier quotation, contract signing, logistics status and inventory information in the procurement process to the blockchain distributed ledger in real time;

[0033] Based on the identity authentication information of hospitals and suppliers, differentiated data access rights are defined through smart contracts. For example, hospitals can view the quotation history, order execution progress, and cross-hospital inventory sharing data of all related suppliers; suppliers can only view the details of orders in which they participated and the corresponding logistics tracking information.

[0034] A visual interface provides hospitals and suppliers with real-time status updates on each step of the procurement process, including an order progress bar showing the current stage, an inventory map dynamically annotating the distribution and allocation paths of supplies for each hospital, and a price fluctuation trend chart combined with market data to provide a reference for procurement decisions.

[0035] When the procurement process deviates from the preset time threshold or the inventory level exceeds the safe range, a multi-level early warning mechanism is triggered: SMS or email notifications are sent to the hospital procurement manager; abnormal nodes are highlighted on the platform interface and an optimization suggestion report is generated.

[0036] Furthermore, the specific implementation of the feedback optimization closed loop includes:

[0037] a. Analyze text feedback from hospitals and suppliers using natural language processing technology to extract key optimization points;

[0038] b. Dynamically adjust the weight parameters in the procurement strategy based on the reinforcement learning model;

[0039] c. Generate supply chain performance reports every month and send them to the management of each hospital and supplier, forming a continuous feedback and optimization closed loop.

[0040] In a second aspect, the present invention provides a medical community multi-hospital collaborative material procurement and supply chain management platform, comprising:

[0041] The demand integration module is used to collect the material procurement demand information of each hospital in the medical community in real time and integrate it into the centralized procurement platform;

[0042] The demand analysis module is used to generate a unified procurement plan based on historical procurement data and material consumption through demand forecasting models, and to generate batch procurement plans based on big data analysis;

[0043] The intelligent matching module is used to screen the best quotation based on the bulk purchase plan through a price comparison algorithm and dynamically match the appropriate supplier;

[0044] Centralized procurement execution module, used to submit bulk procurement plans to suppliers and connect directly with suppliers to simplify intermediate links;

[0045] Supply chain monitoring module, used to monitor the transportation, inventory and distribution status of the supply chain in real time;

[0046] The data sharing and transparency management module is used to unify and open up the procurement needs, inventory information, and procurement process data of each hospital, realizing real-time cross-hospital data sharing and transparency of the entire supply chain process;

[0047] The feedback optimization closed-loop module is used to receive feedback information from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed-loop.

[0048] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.

[0049] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the method described in the first aspect when the program is executed by a processor.

[0050] One or more technical solutions provided by the present invention have at least the following technical effects or advantages:

[0051] 1. Reduce procurement costs: Through centralized procurement, the procurement needs of multiple hospitals are integrated, and suppliers are directly connected, which reduces the intermediate links, improves procurement bargaining power, and thus significantly reduces procurement costs.

[0052] 2. Improve procurement efficiency: Form a unified medical community multi-hospital collaborative material procurement and supply chain management platform, reduce the time of information transmission and cumbersome intermediate links, improve the efficiency of procurement decision-making and execution, and shorten the procurement cycle.

[0053] 3. Optimize resource allocation: Through intelligent matching, demand forecasting and other means, accurate resource allocation is achieved, avoiding shortages or backlogs of materials and improving the efficiency of inventory management.

[0054] 4. Enhance the collaborative capabilities of hospitals: Through information sharing and collaborative management, hospitals within the medical community can better coordinate and cooperate, resource sharing becomes smoother, and overall operational efficiency is improved.

[0055] 5. Enhance supply chain transparency: Transparency in the supply chain allows suppliers and hospitals to understand procurement status in real time, ensuring timely communication and response of information, and helping to reduce friction and misunderstandings that may occur in the supply chain.

[0056] 6. Sustainable optimization: The optimization mechanism based on feedback and data analysis enables the system to continuously adapt to new changes in demand and market, ensuring the long-term effectiveness of the procurement and supply chain management system.

[0057] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0059] Figure 1 This is a flowchart of the method in Example 1 of the present invention;

[0060] Figure 2 This is a schematic diagram of the structure of the platform in the second embodiment of the present invention;

[0061] Figure 3 This is a schematic structural diagram of an electronic device in a third embodiment of the present invention;

[0062] Figure 4 Schematic diagram of the structure of the medium in the fourth embodiment of the present invention. DETAILED DESCRIPTION

[0063] The embodiments of this application provide a method, platform, equipment and medium for collaborative material procurement and supply chain management among multiple hospitals in a medical community. Through the four-in-one technical architecture of "platform integration + intelligent algorithm + blockchain transparency + closed-loop optimization", it solves the core pain points in collaborative procurement among multiple hospitals in a medical community, realizes system-level innovation in efficient resource utilization, precise cost control, and reliable information sharing, and provides a scalable intelligent solution for medical supply chain management.

[0064] The technical solution in the embodiment of the present application has the following overall idea: the present invention integrates the material procurement needs of multiple independent hospitals by building a unified cross-medical community collaborative procurement platform, breaks the information islands in the traditional decentralized management model, realizes the centralized management and collaborative deployment of resources of multiple hospitals within the medical community, reduces duplicate procurement and intermediate links, and reduces operating costs. A demand forecasting model (hybrid forecasting model: LSTM+ARIMA) is used to combine historical procurement data with real-time consumption information to generate an accurate procurement plan; dynamic matching of suitable suppliers (through a multi-factor weighted scoring algorithm, factors including price, fulfillment rate, geographic location, etc.) is performed to achieve intelligent demand forecasting and dynamic optimization, and optimize procurement costs and efficiency. Through full-process transparency and trusted data management (based on blockchain technology to achieve tamper-proof storage and real-time sharing of procurement process, price, inventory and other data, and define differentiated permissions through smart contracts), hospitals and suppliers are ensured to transparently access data within the scope of their authority, thereby enhancing the credibility of the supply chain. Intelligent algorithms are used to predict demand and optimize supplier matching to avoid inventory shortages or backlogs. A closed-loop feedback and continuous optimization mechanism (using natural language processing and reinforcement learning models) is established to analyze user feedback and dynamically adjust procurement strategies, forming a closed-loop management of "execution-feedback-optimization" to enhance the system's adaptability. Through the four-in-one technical architecture of "platform integration + intelligent algorithms + blockchain transparency + closed-loop optimization", the core pain points of collaborative procurement among multiple hospitals in the medical community are resolved, achieving system-level innovations in efficient resource utilization, precise cost control, and trusted information sharing, providing a scalable and intelligent solution for medical supply chain management.

[0065] Example 1

[0066] like Figure 1 As shown, this embodiment provides a method for collaborative material procurement and supply chain management of multiple hospitals in a medical community, including the following steps:

[0067] S1. Demand integration: Real-time collection and integration of material procurement demand information of each hospital in the medical community.

[0068] S2. Demand Analysis: Based on historical procurement data and material consumption, a unified procurement plan is generated using a demand forecasting model, and a batch procurement plan is generated based on big data analysis. The demand forecasting model is a combination of time series analysis and machine learning algorithms.

[0069] The generation of the procurement plan specifically includes the following steps:

[0070] a. Use LSTM neural network to predict the trend of historical procurement data and obtain the prediction results;

[0071] b. Taking into account the cyclical characteristics of hospital material consumption, the ARIMA model is used to modify the forecast results;

[0072] c. Dynamically adjust forecast weights based on real-time updated demand data to generate a unified procurement plan;

[0073] The generation of the bulk purchase plan specifically includes the following steps:

[0074] a. Build a multi-dimensional scoring system based on suppliers' historical fulfillment rates, quotation volatility, and delivery timeliness;

[0075] b. Based on a hybrid optimization model of greedy algorithm and genetic algorithm, select the supplier combination with the highest comprehensive score;

[0076] c. Automatically match the tiered pricing rules based on the purchase scale to generate the bulk purchase plan.

[0077] S3. Intelligent Matching: Based on the bulk purchasing plan, we use a price comparison algorithm to select the best quote and dynamically match suitable suppliers.

[0078] The price comparison algorithm is a multi-factor weighted scoring model that includes the following parameter weights: supplier quotation weight, historical delivery on-time rate weight, after-sales service score weight, and geographical location distribution cost weight; ultimately, the highest weighted total score is selected as the optimal quotation, and the appropriate supplier is dynamically matched.

[0079] For example: the weight of supplier quotation accounts for 40%; the weight of historical delivery on-time rate accounts for 30%; the weight of after-sales service score accounts for 20%; the weight of geographical location delivery cost accounts for 10%; and finally the supplier with the highest weighted total score is selected for matching.

[0080] S4. Centralized procurement execution: Bulk procurement plans are submitted to suppliers and directly connected with them, simplifying the intermediary link and ensuring transparent and reasonable procurement prices.

[0081] S5. Supply Chain Monitoring: Real-time monitoring of the transportation, inventory, and distribution status of the supply chain to ensure that materials arrive on time and avoid inventory backlogs or shortages.

[0082] In the above process, the following are also implemented:

[0083] Data Sharing and Transparent Management: All hospitals' procurement needs, inventory information, and procurement process data are centrally accessible, enabling real-time cross-hospital data sharing and transparency across the entire supply chain. Hospitals can use the platform to view and understand the procurement needs and inventory status of other hospitals, avoiding duplicate purchases and improving resource sharing efficiency. All procurement process, pricing, inventory, and other information are transparent, allowing suppliers and hospitals to track procurement status in real time, enhancing supply chain transparency.

[0084] Feedback optimization closed loop: Receive feedback from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed loop.

[0085] The data sharing is achieved through blockchain technology, specifically including:

[0086] a. Store each hospital’s procurement needs, inventory information, and procurement process data on the blockchain in real time;

[0087] b. Automatically trigger inventory transfer instructions through smart contracts to ensure data cannot be tampered with and is fully traceable;

[0088] c. Each hospital node authorizes access to on-chain data through private key authorization, enabling real-time sharing of data across hospitals.

[0089] The transparent management specifically includes:

[0090] Upload order generation, supplier quotation, contract signing, logistics status and inventory information in the procurement process to the blockchain distributed ledger in real time;

[0091] Based on the identity authentication information of hospitals and suppliers, differentiated data access rights are defined through smart contracts. For example, hospitals can view the quotation history, order execution progress, and cross-hospital inventory sharing data of all related suppliers; suppliers can only view the details of orders in which they participated and the corresponding logistics tracking information.

[0092] A visual interface provides hospitals and suppliers with real-time status updates on each step of the procurement process, including an order progress bar showing the current stage, an inventory map dynamically annotating the distribution and allocation paths of supplies for each hospital, and a price fluctuation trend chart combined with market data to provide a reference for procurement decisions.

[0093] When the procurement process deviates from the preset time threshold or the inventory level exceeds the safe range, a multi-level early warning mechanism is triggered: SMS or email notifications are sent to the hospital procurement manager; abnormal nodes are highlighted on the platform interface and an optimization suggestion report is generated.

[0094] The specific implementation of the feedback optimization closed loop includes:

[0095] a. Analyze text feedback from hospitals and suppliers using natural language processing technology to extract key optimization points;

[0096] b. Dynamically adjust the weight parameters in the procurement strategy based on the reinforcement learning model;

[0097] c. Generate supply chain performance reports every month and send them to the management of each hospital and supplier, forming a continuous feedback and optimization closed loop.

[0098] Example 2

[0099] Based on the same inventive concept, this application also provides a device corresponding to the method in Example 1, see Example 2 for details.

[0100] like Figure 2 As shown, in this embodiment, a medical community multi-hospital collaborative material procurement and supply chain management platform is provided, including a demand integration module, a demand analysis module, an intelligent matching module, a centralized procurement execution module, a supply chain monitoring module, a data sharing and transparency management module, and a feedback optimization closed-loop module.

[0101] The demand integration module is used to collect the material procurement demand information of each hospital in the medical community in real time and integrate it into the centralized procurement platform;

[0102] A demand analysis module, which generates a unified procurement plan based on historical procurement data and material consumption using a demand forecasting model, and generates batch procurement plans based on big data analysis; the demand forecasting model is a combination of time series analysis and machine learning algorithms;

[0103] The generation of the procurement plan specifically includes the following steps:

[0104] a. Use LSTM neural network to predict the trend of historical procurement data and obtain the prediction results;

[0105] b. Taking into account the cyclical characteristics of hospital material consumption, the ARIMA model is used to modify the forecast results;

[0106] c. Dynamically adjust forecast weights based on real-time updated demand data to generate a unified procurement plan;

[0107] The generation of the bulk purchase plan specifically includes the following steps:

[0108] a. Build a multi-dimensional scoring system based on suppliers' historical fulfillment rates, quotation volatility, and delivery timeliness;

[0109] b. Based on a hybrid optimization model of greedy algorithm and genetic algorithm, select the supplier combination with the highest comprehensive score;

[0110] c. Automatically match the tiered pricing rules based on the purchase scale to generate the bulk purchase plan.

[0111] An intelligent matching module is used to screen the best quote based on the bulk purchasing plan using a price comparison algorithm and dynamically match suitable suppliers. The price comparison algorithm is a multi-factor weighted scoring model that includes the following parameter weights: supplier quote weight, historical delivery on-time rate weight, after-sales service score weight, and geographical location distribution cost weight. Ultimately, the quote with the highest weighted total score is selected as the best quote and dynamically matched with suitable suppliers.

[0112] Centralized procurement execution module, used to submit bulk procurement plans to suppliers and connect directly with suppliers to simplify intermediate links;

[0113] Supply chain monitoring module, used to monitor the transportation, inventory and distribution status of the supply chain in real time;

[0114] The data sharing and transparency management module is used to unify and open up the procurement needs, inventory information, and procurement process data of each hospital, realizing real-time cross-hospital data sharing and transparency of the entire supply chain process;

[0115] The data sharing is achieved through blockchain technology, specifically including:

[0116] a. Store each hospital’s procurement needs, inventory information, and procurement process data on the blockchain in real time;

[0117] b. Automatically trigger inventory transfer instructions through smart contracts to ensure data cannot be tampered with and is fully traceable;

[0118] c. Each hospital node authorizes access to on-chain data through private key authorization, enabling real-time sharing of data across hospitals.

[0119] The transparent management specifically includes:

[0120] Upload order generation, supplier quotation, contract signing, logistics status and inventory information in the procurement process to the blockchain distributed ledger in real time;

[0121] Based on the identity authentication information of hospitals and suppliers, differentiated data access rights are defined through smart contracts. For example, hospitals can view the quotation history, order execution progress, and cross-hospital inventory sharing data of all related suppliers; suppliers can only view the details of orders in which they participated and the corresponding logistics tracking information.

[0122] A visual interface provides hospitals and suppliers with real-time status updates on each step of the procurement process, including an order progress bar showing the current stage, an inventory map dynamically annotating the distribution and allocation paths of supplies for each hospital, and a price fluctuation trend chart combined with market data to provide a reference for procurement decisions.

[0123] When the procurement process deviates from the preset time threshold or the inventory level exceeds the safe range, a multi-level early warning mechanism is triggered: SMS or email notifications are sent to the hospital procurement manager; abnormal nodes are highlighted on the platform interface and an optimization suggestion report is generated.

[0124] The feedback optimization closed-loop module is used to receive feedback information from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed-loop.

[0125] The specific implementation of the feedback optimization closed loop includes:

[0126] a. Analyze text feedback from hospitals and suppliers using natural language processing technology to extract key optimization points;

[0127] b. Dynamically adjust the weight parameters in the procurement strategy based on the reinforcement learning model;

[0128] c. Generate supply chain performance reports every month and send them to the management of each hospital and supplier, forming a continuous feedback and optimization closed loop.

[0129] Since the device described in the second embodiment of the present invention is used to implement the method of the first embodiment of the present invention, those skilled in the art will be able to understand the specific structure and variations of the device based on the method described in the first embodiment of the present invention, and therefore will not be described in detail here. All devices used in the method of the first embodiment of the present invention fall within the scope of protection of the present invention.

[0130] Example 3

[0131] Based on the same inventive concept, this application provides an electronic device embodiment corresponding to the first embodiment, see the third embodiment for details.

[0132] like Figure 3 As shown, this embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any implementation method in the first embodiment can be implemented.

[0133] Since the electronic device described in this embodiment is the device used to implement the method in Example 1 of this application, based on the method described in Example 1 of this application, those skilled in the art will be able to understand the specific implementation of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of this application will not be described in detail here. As long as the device used by those skilled in the art to implement the method in the embodiment of this application falls within the scope of protection to be provided by this application.

[0134] Example 4

[0135] Based on the same inventive concept, this application provides a storage medium corresponding to Example 1, see Example 4 for details.

[0136] like Figure 4 As shown, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any implementation method in the first embodiment can be implemented.

[0137] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, 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. 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.

[0138] 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 processor, 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 device that provides the functions specified in a block or multiple blocks.

[0139] 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 comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] 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.

[0141] Although the specific embodiments of the present invention are described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and are not intended to limit the scope of the present invention. Equivalent modifications and changes made by those skilled in the art in accordance with the spirit of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A method for collaborative material procurement and supply chain management among multiple hospitals in a medical community, characterized by: The following steps are involved: S1. Demand integration: Real-time collection and integration of material procurement demand information from hospitals within the medical community; S2. Demand Analysis: Based on historical procurement data and material consumption, a unified procurement plan is generated through demand forecasting models, and batch procurement plans are generated based on big data analysis. S3. Intelligent Matching: Based on the bulk purchasing plan, we use a price comparison algorithm to select the best quote and dynamically match suitable suppliers. S4. Centralized Procurement Execution: Submit bulk procurement plans to suppliers and connect directly with them to simplify intermediary processes. S5. Supply chain monitoring: Real-time monitoring of supply chain transportation, inventory, and distribution status; In the above process, the following are also implemented: Data sharing and transparent management: Unified and open access to each hospital's procurement needs, inventory information, and procurement process data enables real-time cross-hospital data sharing and transparency throughout the supply chain. Feedback optimization closed loop: Receive feedback from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed loop.

2. A method according to claim 1, characterized in that: The demand forecasting model in S2 is a combination model based on time series analysis and machine learning algorithm; The generation of the procurement plan specifically includes the following steps: a. Use LSTM neural network to predict the trend of historical procurement data and obtain the prediction results; b. Taking into account the cyclical characteristics of hospital material consumption, the ARIMA model is used to modify the forecast results; c. Dynamically adjust forecast weights based on real-time updated demand data to generate a unified procurement plan.

3. A method according to claim 1, characterized in that: In S2, the generation of the bulk purchase plan specifically includes the following steps: a. Build a multi-dimensional scoring system based on suppliers' historical fulfillment rates, quotation volatility, and delivery timeliness; b. Based on a hybrid optimization model of greedy algorithm and genetic algorithm, select the supplier combination with the highest comprehensive score; c. Automatically match the tiered pricing rules based on the purchase scale to generate the bulk purchase plan.

4. A method according to claim 1, characterized in that: The price comparison algorithm in S3 is a multi-factor weighted scoring model, which includes the following parameter weights: supplier quotation weight, historical delivery on-time rate weight, after-sales service score weight, and geographical location delivery cost weight. Ultimately, the highest weighted total score is selected as the optimal quotation, and the appropriate supplier is dynamically matched.

5. A method according to claim 1, characterized in that: The data sharing in S6 is achieved through blockchain technology, specifically including: a. Store each hospital’s procurement needs, inventory information, and procurement process data on the blockchain in real time; b. Automatically trigger inventory transfer instructions through smart contracts to ensure data cannot be tampered with and is fully traceable; c. Each hospital node authorizes access to on-chain data through private key authorization, enabling real-time sharing of data across hospitals.

6. A method according to claim 1, characterized in that: The transparent management specifically includes: Upload order generation, supplier quotation, contract signing, logistics status and inventory information in the procurement process to the blockchain distributed ledger in real time; Based on the identity authentication information of hospitals and suppliers, differentiated data access rights are defined through smart contracts. For example, hospitals can view the quotation history, order execution progress, and cross-hospital inventory sharing data of all related suppliers; suppliers can only view the details of orders in which they participated and the corresponding logistics tracking information. A visual interface provides hospitals and suppliers with real-time status updates on each step of the procurement process, including an order progress bar showing the current stage, an inventory map dynamically annotating the distribution and allocation paths of supplies for each hospital, and a price fluctuation trend chart combined with market data to provide a reference for procurement decisions. When the procurement process deviates from the preset time threshold or the inventory level exceeds the safe range, a multi-level early warning mechanism is triggered: SMS or email notifications are sent to the hospital procurement manager; abnormal nodes are highlighted on the platform interface and an optimization suggestion report is generated.

7. A method according to claim 1, characterized in that: The specific implementation of the feedback optimization closed loop includes: a. Analyze text feedback from hospitals and suppliers using natural language processing technology to extract key optimization points; b. Dynamically adjust the weight parameters in the procurement strategy based on the reinforcement learning model; c. Generate supply chain performance reports every month and send them to the management of each hospital and supplier, forming a continuous feedback and optimization closed loop.

8. A medical community multi-hospital collaborative material procurement and supply chain management platform, characterized by: include: The demand integration module is used to collect the material procurement demand information of each hospital in the medical community in real time and integrate it into the centralized procurement platform; The demand analysis module is used to generate a unified procurement plan based on historical procurement data and material consumption through demand forecasting models, and to generate batch procurement plans based on big data analysis; The intelligent matching module is used to screen the best quotation based on the bulk purchase plan through a price comparison algorithm and dynamically match the appropriate supplier; Centralized procurement execution module, used to submit bulk procurement plans to suppliers and connect directly with suppliers to simplify intermediate links; Supply chain monitoring module, used to monitor the transportation, inventory and distribution status of the supply chain in real time; The data sharing and transparency management module is used to unify and open up the procurement needs, inventory information, and procurement process data of each hospital, realizing real-time cross-hospital data sharing and transparency of the entire supply chain process; The feedback optimization closed-loop module is used to receive feedback information from hospitals and suppliers in real time, optimize procurement strategies and adjust matching algorithms based on feedback data, and form a continuous feedback optimization closed-loop.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images