Medical digital supply chain management method and system based on SaaS cloud service

Abstract

The invention belongs to the technical field of supply chain management, and aims to provide a medical digital supply chain management method and system based on SaaS cloud service. The method is executed based on an SaaS cloud platform, and comprises the steps of obtaining historical demand quantity of specified medical supplies of a specified hospital in a historical specified time period, and obtaining daily average demand predicted quantity; obtaining the current stock of the specified hospital, obtaining the maximum stock and the replenishment advance time length from the current moment to the next replenishment day when the current stock is smaller than a preset stock threshold value, and making a replenishment decision to obtain the replenishment amount at the current moment; and judging whether the replenishment amount is greater than a preset replenishment amount, if so, generating a purchase order of the specified medical supplies according to the replenishment amount, and then sending the purchase order to a supply end of the specified supplier through the SaaS cloud platform, so that the specified supplier completes replenishment operation. According to the invention, information islands of all parties can be broken, real-time updating and sharing of data are realized, and the efficiency of a medical supply chain is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of supply chain management, and specifically relates to a medical digital supply chain management method and system based on SaaS cloud service. Background Art

[0002] With the rapid development of the medical industry, especially in the context of the global outbreak, the supply chain management of medical supplies is facing increasing challenges. Traditional medical supply chain management technology usually relies on manual inventory and manual records, mainly managing inventory through paper records or electronic spreadsheets, which has high labor costs and delayed data updates. Especially in the case of sudden epidemics or emergencies, the response speed of material supply is greatly restricted.

[0003] In order to overcome the technical defects of manual management, technologies have emerged in the prior art that use enterprise resource planning (ERP) systems, supply chain management (SCM) systems, and inventory management systems to achieve medical supply chain management. For example, a Chinese patent with publication number CN117172710A discloses a digital material supply chain data intelligent management system synchronization method and system, which achieves material supply chain data management by using SCM systems and ERP systems.

[0004] However, in the process of using the prior art, the inventors found that the prior art has at least the following problems:

[0005] First, ERP systems, SCM systems, and inventory management systems are usually deployed locally, making the existing medical supply chain management dependent on separate information systems at each link, resulting in the inability to share data in real time between suppliers, distributors, hospitals and other participants. This information island phenomenon makes the transparency of the medical supply chain low, resulting in poor communication and collaboration at each link, delaying the timeliness of supply chain decisions, and thus affecting the timely allocation of materials, making it difficult to adapt to the increasingly complex needs and environmental changes in the medical industry. Secondly, in the existing technology, medical supply chain management usually makes procurement plans on a monthly or quarterly basis, while the demand for medical supplies is often highly seasonal and sudden. The traditional periodic procurement and update method is difficult to accurately grasp demand fluctuations, resulting in inflexible material scheduling and low supply chain efficiency. Summary of the invention

[0006] The present invention aims to solve the above-mentioned technical problems at least to a certain extent. The present invention provides a medical digital supply chain management method and system based on SaaS cloud service.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a medical digital supply chain management method based on SaaS cloud service, which is executed based on a SaaS cloud platform, and the method includes:

[0009] Obtain the historical demand for the designated medical supplies of the designated hospital within a designated historical time period, and use a preset demand forecasting model to obtain the daily average demand forecast for the designated medical supplies of the designated hospital within a future designated time period based on the historical demand;

[0010] Obtain the current inventory of the designated hospital, and when the current inventory is less than a preset inventory threshold, obtain the maximum inventory and the replenishment lead time from the current moment to the next replenishment day, and make a replenishment decision based on the average daily demand forecast, the current inventory, the maximum inventory and the replenishment lead time to obtain the replenishment quantity at the current moment;

[0011] Determine whether the replenishment quantity is greater than the preset replenishment quantity, if so, proceed to the next step;

[0012] A purchase order for the designated medical supplies is generated based on the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation.

[0013] In one possible design, the SaaS cloud platform adopts a distributed database.

[0014] In a possible design, the daily average demand forecast is:

[0015] Y t =μ+φ1·Y t-1 +φ2·Y t-2 +…+φ p ·Y t-p +θ1·∈ t-1 +θ2·∈ t-2 +…+θ q ∈ t-q +∈ t ;

[0016] In the formula, μ is the preset constant term, p is the preset autoregressive order, q is the preset sliding average order, and Y t-1 ,Y t-2 ,…,Y t-p is the historical demand 1, 2, …, p days before the current moment, φ1, φ2, …, φ p is the historical demand Y t-1 ,Y t-2 ,…,Y t-p The daily average demand forecast Yt The influence coefficient of t-1 ,∈ t-2 ,…,∈ t-q is the demand error term 1, 2, …, q days before the current moment, θ1, θ2, …, θ q is the demand error term∈ t-1 ,∈ t-2 ,…,∈ t-q The daily average demand forecast Y t The influence coefficient of t is the preset error term of demand at the current moment.

[0017] In one possible design, the current replenishment quantity is:

[0018] R t =max(Y t ·L, S max -I t );

[0019] Where Y t is the daily average demand forecast, L is the replenishment lead time, S max is the maximum inventory, I t is the current inventory quantity.

[0020] In a possible design, after generating a purchase order for the designated medical supplies according to the replenishment quantity, the method further includes:

[0021] Obtaining supplier information of multiple candidate suppliers matching the designated medical supplies;

[0022] According to the supplier information of multiple candidate suppliers, the recommendation scores of the multiple candidate suppliers are calculated respectively;

[0023] The candidate supplier with the highest recommendation score is recommended as the designated supplier, so that the purchase order can be sent to the supply end of the designated supplier through the SaaS cloud platform.

[0024] In a possible design, the supplier information includes average delivery time, mean price deviation, and fulfillment rate; correspondingly, the recommendation score of any candidate supplier is:

[0025] Score i =w1·T i +w2·C i +w3·R i ;

[0026] Where, T i is the average delivery time of any candidate supplier, w1 is the preset delivery time weight, Ci is the mean price deviation between any candidate supplier and other candidate suppliers, w2 is the preset price advantage weight, R i is the fulfillment rate of any candidate supplier, and w3 is the preset fulfillment rate weight.

[0027] In a second aspect, the present invention provides a medical digital supply chain management system based on SaaS cloud service, comprising:

[0028] A demand forecasting module is used to obtain the historical demand of the designated medical supplies of the designated hospital within a designated historical time period, and use a preset demand forecasting model to obtain the daily average demand forecast of the designated medical supplies of the designated hospital within a future designated time period based on the historical demand;

[0029] a replenishment quantity acquisition module, which is in communication connection with the demand quantity prediction module, and is used to obtain the current inventory quantity of the designated hospital, and when the current inventory quantity is less than a preset inventory quantity threshold, obtain the maximum inventory quantity and the replenishment lead time from the current moment to the next replenishment day, and make a replenishment decision based on the average daily demand forecast, the current inventory quantity, the maximum inventory quantity and the replenishment lead time to obtain the replenishment quantity at the current moment;

[0030] The replenishment information processing module is in communication with the replenishment quantity acquisition module, and is used to determine whether the replenishment quantity is greater than the preset replenishment quantity. If so, a purchase order for the designated medical supplies is generated according to the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation.

[0031] In a third aspect, the present invention provides an electronic device, comprising:

[0032] a memory for storing computer program instructions; and,

[0033] A processor is used to execute the computer program instructions to complete the operation of a medical digital supply chain management method based on SaaS cloud service as described in any one of the above.

[0034] In a fourth aspect, the present invention provides a computer program product, including a computer program or instructions, which, when executed by a computer, implements a medical digital supply chain management method based on SaaS cloud service as described in any one of the above.

[0035] In a fifth aspect, the present invention provides a computer-readable storage product having instructions stored thereon. When the instructions are run on a computer, a medical digital supply chain management method based on SaaS cloud service as described in any one of the above is executed.

[0036] The beneficial effects of the present invention are:

[0037] The present invention discloses a medical digital supply chain management method and system based on SaaS cloud service, which can break the information islands of all parties, realize real-time update and sharing of data, and improve the efficiency of medical supply chain. Specifically, the present invention is executed based on SaaS cloud platform. In the implementation process, first, the historical demand of the designated medical supplies of the designated hospital in the historical specified time period is obtained, and the daily average demand forecast of the designated hospital for the designated medical supplies in the future specified time period is obtained according to the historical demand using a preset demand forecast model; then, the current inventory of the designated hospital is obtained, and when the current inventory is less than the preset inventory threshold, the maximum inventory and the replenishment lead time from the current moment to the next replenishment day are obtained, and replenishment decision is made according to the daily average demand forecast, the current inventory, the maximum inventory and the replenishment lead time to obtain the replenishment quantity at the current moment; then, it is determined whether the replenishment quantity is greater than the preset replenishment quantity. If so, a purchase order for the designated medical supplies is generated according to the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation. Based on the above scheme, the present invention adopts the technical architecture of SaaS cloud service, which can break the information islands of each party and realize real-time updating and sharing of data, thereby improving the transparency and response speed of the supply chain. By utilizing the advantages of cloud computing of the SaaS cloud platform, the present invention realizes unified real-time management and sharing of data in various links of the medical supply chain. The supply chain has high transparency and flexibility, which is conducive to improving the supply guarantee capability of medical supplies. In addition, based on the present invention, the hospital can monitor inventory in real time, make demand forecasts and automatically generate purchase orders, which greatly improves the efficiency and response speed of the supply chain and provides a strong guarantee for the timely supply of medical supplies.

[0038] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 is a flow chart of a medical digital supply chain management method based on SaaS cloud service in an embodiment;

[0040] Figure 2 It is a module block diagram of a medical digital supply chain management system based on SaaS cloud service in an embodiment;

[0041] Figure 3 It is a module block diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in combination with the drawings and the description of the embodiments or the prior art. Obviously, the following description of the structure of the drawings is only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0043] Embodiment 1:

[0044] This embodiment discloses a medical digital supply chain management method based on SaaS cloud service, which is executed based on SaaS cloud platform. It should be noted that the SaaS (Software as a Service, a mode of deploying software on cloud servers and providing application software services to users through the Internet) cloud platform is an emerging technical architecture with high scalability, real-time data sharing and flexible collaboration functions. Specifically, in this embodiment, the business modules such as inventory management, order processing, and supplier management in medical supply chain management are integrated on the SaaS cloud platform, and the SaaS cloud platform is connected to the external systems of various participants related to the medical supply chain (such as hospitals, suppliers, warehouse managers and logistics companies, etc.) through API (Application Programming Interface). Each participant can access, upload and update data in real time through the SaaS cloud platform, including inventory information, order status and supplier delivery progress, etc., thereby ensuring that the information in the supply chain is always up to date and supports concurrent access by multiple parties.

[0045] This embodiment can be, but is not limited to, executed by a computer device or a virtual machine with certain computing resources in the SaaS cloud platform, such as an electronic device such as a personal computer, a smart phone, a personal digital assistant or a wearable device, or by a virtual machine.

[0046] In this embodiment, the SaaS cloud platform adopts a distributed database. Specifically, in this embodiment, the data of each participant related to the medical supply chain is centrally managed through the distributed database of the SaaS cloud platform. The distributed database adopts Hadoop (an open source distributed computing platform that can process and store massive data) or Cassandra (an open source, distributed and decentralized / distributed storage system for managing large amounts of structured data distributed around the world) to ensure high availability and security of data. Further, in the implementation process, this embodiment adopts real-time stream data processing technology, such as Apache Kafka (a distributed data storage optimized for real-time extraction and processing of stream data) or Apache Flink (an open source stream processing framework for stateful computing on unbounded (unbounded stream) and bounded (bounded stream) data streams) to ensure the real-time nature of data transmission.

[0047] like Figure 1 As shown, a medical digital supply chain management method based on SaaS cloud service may include but is not limited to the following steps:

[0048] S1. Obtain the historical demand for the designated medical supplies of the designated hospital within the historical specified time period, and use the preset demand forecasting model to obtain the daily average demand forecast for the designated medical supplies of the designated hospital within the future specified time period based on the historical demand. It should be noted that in this embodiment, the demand forecasting model is deployed on the SaaS cloud platform, which can be called by the SaaS cloud platform at any time as needed. Based on this, the SaaS cloud platform can accurately predict the demand in the next few days or weeks based on the demand forecasting model; during the implementation process, the demand forecasting of the designated medical supplies based on the historical demand data can help hospitals make inventory planning and procurement decisions in advance, facilitate the subsequent automatic adjustment of procurement plans and inventory levels, and thus optimize supply chain efficiency.

[0049] Since the demand for medical supplies usually has the characteristics of seasonal changes, in order to improve the prediction accuracy of medical supplies, in this embodiment, the demand forecasting model adopts a demand forecasting model based on time series. Specifically, the demand forecasting model adopts ARIMA (Autoregressive Integrated Moving Average Model), which is conducive to the rapid calculation of the daily average demand forecast. In the implementation process, multi-source sample data such as historical demand and inventory within a specified historical period are obtained in advance, and the multi-source sample data is pre-processed by missing value filling, time series feature extraction and normalization, and then the pre-processed data is divided into a training set, a validation set and a test set (such as 7:2:1) in proportion, and then the initial model is trained using an optimization algorithm such as the gradient descent method to minimize the loss function, and the parameters in the model are tuned (such as the autoregressive order and the sliding average order, etc.), so as to obtain a trained model, that is, the demand forecasting model.

[0050] Specifically, in step S1, the daily average demand forecast is:

[0051] Y t =μ+φ1·Y t-1 +φ2·Y t-2 +…+φ p ·Y t-p +θ1·∈ t-1 +θ2·∈ t-2 +…+θ q ∈ t-q +∈ t ;

[0052] In the formula, μ is the preset constant term, p is the preset autoregressive order, q is the preset sliding average order, and Y t-1 ,Y t-2 ,…,Y t-p is the historical demand 1, 2, …, p days before the current moment, φ1, φ2, …, φ p is the historical demand Y t-1 ,Y t-2 ,…,Y t-p The daily average demand forecast Y t The influence coefficient of , which can be called the autoregressive coefficient, ∈ t-1 ,∈ t-2 ,…,∈ t-q is the demand error term 1, 2, …, q days before the current moment, θ1, θ2, …, θ q is the demand error term∈ t-1 ,∈ t-2 ,…,∈ t-q The daily average demand forecast Y tThe influence coefficient of can be called the sliding average coefficient, ∈ t is the preset error term of demand at the current moment.

[0053] In addition, in this embodiment, the demand forecasting model may also adopt an LSTM (Long Short Term Memory) model, which can be used to process time series data with complex demand patterns and long dependencies, and is suitable for demand forecasting in complex scenarios.

[0054] S2. Obtain the current inventory of the designated hospital, and when the current inventory is less than the preset inventory threshold, obtain the maximum inventory and the replenishment lead time from the current time to the next replenishment day, and make a replenishment decision based on the daily average demand forecast, the current inventory, the maximum inventory and the replenishment lead time to obtain the replenishment quantity at the current time. The current inventory is extracted from the SaaS cloud platform; the maximum inventory is a value preset according to the actual situation of the hospital; for example, if a supplier promises to supply regularly every Monday, the replenishment lead time is the time difference between the current time and the next Monday.

[0055] In this embodiment, all medical supplies are equipped with RFID (Radio Frequency Identification Devices) tags or barcodes to achieve automated inventory management. Before running this embodiment, all medical supplies information in the hospital inventory can be collected through the Internet of Things device, and all medical supplies information can be uploaded to the SaaS cloud platform to form inventory data. In addition, each time the inventory changes (such as warehousing, outbound or transfer, etc.), the relevant data is uploaded to the SaaS cloud platform in real time through the Internet of Things device to ensure the immediacy and accuracy of the inventory data; wherein the medical supplies information includes detailed information of medical supplies such as vaccines and medicines, such as product ID (Identity document, identity number), batch number, expiration date, inventory quantity, storage location and inventory update time. The use of Internet of Things devices can realize the automatic upload of data such as medical supplies information, reduce manual intervention, and achieve accurate inventory management. Specifically, the Internet of Things devices in this embodiment include devices such as RFID tag scanners and barcode scanners, which are not limited here.

[0056] Specifically, in step S2, the replenishment quantity at the current moment is:

[0057] R t =max(Y t ·L, S max -I t );

[0058] Where Y t is the daily average demand forecast, L is the replenishment lead time, S max is the maximum inventory, I t is the current inventory quantity.

[0059] As an example, assume that the current inventory of the designated hospital is t =500, maximum inventory S max =1000, daily average demand forecast Y t =50, the replenishment lead time L = 5 days. According to the formula: R t =max(50×5,1000-500)=max(250,500)=500, so the replenishment quantity R t =500, based on this, it is convenient to generate a replenishment order in the subsequent steps.

[0060] Based on the above replenishment quantity calculation method, the calculation amount is small, and a rapid prediction of the replenishment quantity can be achieved.

[0061] S3. Determine whether the replenishment quantity is greater than the preset replenishment quantity. If so, proceed to the next step; if not, do nothing; wherein the preset replenishment quantity is a natural number greater than or equal to 0.

[0062] S4. Generate a purchase order for the designated medical supplies based on the replenishment quantity, and then send the purchase order to the supply side of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation. In this embodiment, after receiving the purchase order, the designated supplier automatically responds and starts processing the order.

[0063] In step S4, after generating a purchase order for the designated medical supplies according to the replenishment quantity, the method further includes:

[0064] S401. Obtain supplier information of multiple candidate suppliers matching the designated medical supplies.

[0065] Specifically, in step S401, the supplier information includes average delivery time, mean price deviation and fulfillment rate.

[0066] S402. Calculate recommendation scores for the multiple candidate suppliers based on their supplier information.

[0067] In step S402, the recommendation score of any candidate supplier is:

[0068] Score i =w1·T i +w2·C i +w3·R i;

[0069] Where, T i is the average delivery time of any candidate supplier, w1 is the preset delivery time weight, C i is the average price deviation between any candidate supplier and other candidate suppliers, which represents the price advantage of any candidate supplier, w2 is the preset price advantage weight, R i is the fulfillment rate of any candidate supplier, which is also the proportion of orders completed by any candidate supplier, and w3 is the preset fulfillment rate weight.

[0070] S403. Recommend the candidate supplier with the highest score as the designated supplier, so that the purchase order can be sent to the supply end of the designated supplier through the SaaS cloud platform.

[0071] It should be noted that, in this embodiment, when replenishment operations are required, recommending suppliers through the above steps S401-S403 can help reduce procurement costs while ensuring the timeliness of material supply.

[0072] In this embodiment, after a purchase order is generated, the order information will be recorded in the blockchain to ensure the transparency and non-tamperability of the information.

[0073] To further improve the efficiency of the supply chain, in this embodiment, each order generation, delivery, receipt, payment and other operations will be automatically executed through smart contracts, and smart contract technology will be used to realize automated payment and settlement operations. Specifically, during the implementation process, when specific conditions such as order delivery, arrival at the delivery time node and acceptance are met, payment and settlement actions are automatically triggered. Smart contracts ensure that all parties in the supply chain fulfill their obligations in accordance with the terms of the contract through pre-set rules, reduce the risk of default and improve payment efficiency.

[0074] In this embodiment, the execution logic of the smart contract can be expressed in the following way:

[0075] IF(S delivery AND T arrival ≤T max )THEN P payment =true;

[0076] Among them, S delivery Indicates that medical supplies have been shipped, T arrival Indicates the actual delivery time of medical supplies, T max represents the maximum allowed arrival time of medical supplies, P payment Indicates payment status.

[0077] This embodiment can break the information islands of all parties, realize real-time updating and sharing of data, and improve the efficiency of the medical supply chain. Specifically, this embodiment is executed based on the SaaS cloud platform. During the implementation process, first, the historical demand of the designated medical supplies of the designated hospital in the historical specified time period is obtained, and the preset demand forecast model is used to obtain the daily average demand forecast of the designated medical supplies of the designated hospital in the future specified time period according to the historical demand; then, the current inventory of the designated hospital is obtained, and when the current inventory is less than the preset inventory threshold, the maximum inventory and the replenishment lead time from the current moment to the next replenishment day are obtained, and the replenishment decision is made according to the daily average demand forecast, the current inventory, the maximum inventory and the replenishment lead time to obtain the replenishment quantity at the current moment; then, it is determined whether the replenishment quantity is greater than the preset replenishment quantity. If so, a purchase order for the designated medical supplies is generated according to the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation. Based on the above scheme, this embodiment adopts the technical architecture of SaaS cloud service, which can break the information islands of each party and realize real-time updating and sharing of data, thereby improving the transparency and response speed of the supply chain. By utilizing the advantages of cloud computing of the SaaS cloud platform, this embodiment realizes unified real-time management and sharing of data in various links of the medical supply chain. The supply chain has high transparency and flexibility, which is conducive to improving the supply guarantee capability of medical supplies. In addition, based on this embodiment, the hospital can monitor inventory in real time, make demand forecasts and automatically generate purchase orders, which greatly improves the efficiency and response speed of the supply chain and provides a strong guarantee for the timely supply of medical supplies.

[0078] Embodiment 2:

[0079] This embodiment discloses a medical digital supply chain management system based on SaaS cloud service for implementing the medical digital supply chain management method based on SaaS cloud service in Embodiment 1; Figure 2 As shown, the medical digital supply chain management system based on SaaS cloud service includes:

[0080] A demand forecasting module is used to obtain the historical demand of the designated medical supplies of the designated hospital within a designated historical time period, and use a preset demand forecasting model to obtain the daily average demand forecast of the designated medical supplies of the designated hospital within a future designated time period based on the historical demand;

[0081] a replenishment quantity acquisition module, which is in communication connection with the demand quantity prediction module, and is used to obtain the current inventory quantity of the designated hospital, and when the current inventory quantity is less than a preset inventory quantity threshold, obtain the maximum inventory quantity and the replenishment lead time from the current moment to the next replenishment day, and make a replenishment decision based on the average daily demand forecast, the current inventory quantity, the maximum inventory quantity and the replenishment lead time to obtain the replenishment quantity at the current moment;

[0082] The replenishment information processing module is in communication with the replenishment quantity acquisition module, and is used to determine whether the replenishment quantity is greater than the preset replenishment quantity. If so, a purchase order for the designated medical supplies is generated according to the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation.

[0083] It should be noted that the working process, working details and technical effects of the medical digital supply chain management system based on SaaS cloud service provided by this embodiment 2 can be found in embodiment 1 and will not be repeated here.

[0084] Embodiment 3:

[0085] Based on Embodiment 1 or 2, this embodiment discloses an electronic device, which may be a smart phone, a tablet computer, a laptop computer, or a desktop computer. The electronic device may be referred to as a user terminal, a portable terminal, a desktop terminal, etc. Figure 3 As shown, the electronic equipment includes:

[0086] a memory for storing computer program instructions; and,

[0087] A processor is used to execute the computer program instructions to complete the operation of a medical digital supply chain management method based on SaaS cloud service as described in any one of Example 1.

[0088] Specifically, the processor 301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 301 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen.

[0089] The memory 302 may include one or more computer-readable storage media, which may be non-transitory. The memory 302 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments, the non-transitory computer-readable storage medium in the memory 302 is used to store at least one instruction, which is used to be executed by the processor 301 to implement the medical digital supply chain management method based on SaaS cloud service provided in Example 1 of the present application.

[0090] In some embodiments, the terminal may further optionally include: a communication interface 303 and at least one peripheral device. The processor 301, the memory 302 and the communication interface 303 may be connected via a bus or a signal line. Each peripheral device may be connected to the communication interface 303 via a bus, a signal line or a circuit board. Specifically, the peripheral device includes: at least one of a radio frequency circuit 304, a display screen 305 and a power supply 306.

[0091] The communication interface 303 may be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 301 and the memory 302. In some embodiments, the processor 301, the memory 302, and the communication interface 303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 301, the memory 302, and the communication interface 303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0092] The radio frequency circuit 304 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 304 communicates with a communication network and other communication devices through electromagnetic signals.

[0093] The display screen 305 is used to display a UI (User Interface). The UI may include any combination of graphics, text, icons, and videos.

[0094] The power supply 306 is used to supply power to various components in the electronic device.

[0095] Embodiment 4:

[0096] Based on any one of Embodiments 1 to 3, this embodiment discloses a computer program product, including a computer program or an instruction, which, when executed by a computer, implements a medical digital supply chain management method based on SaaS cloud service as described in any one of Embodiments 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0097] Embodiment 5:

[0098] Based on any one of Embodiments 1 to 3, this embodiment discloses a computer-readable storage product, on which instructions are stored. When the instructions are run on a computer, a medical digital supply chain management method based on SaaS cloud service as described in any one of Embodiments 1 is executed. The computer-readable storage product refers to a carrier for storing data, which may include but is not limited to computer-readable storage media such as floppy disks, optical disks, hard disks, flash memories, USB flash drives, and / or memory sticks, and the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0099] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A medical digital supply chain management method based on SaaS cloud service, characterized in that: Executed based on the SaaS cloud platform, the method includes: Obtain the historical demand for the designated medical supplies of the designated hospital within a designated historical time period, and use a preset demand forecasting model to obtain the daily average demand forecast for the designated medical supplies of the designated hospital within a future designated time period based on the historical demand; Obtain the current inventory of the designated hospital, and when the current inventory is less than a preset inventory threshold, obtain the maximum inventory and the replenishment lead time from the current moment to the next replenishment day, and make a replenishment decision based on the average daily demand forecast, the current inventory, the maximum inventory and the replenishment lead time to obtain the replenishment quantity at the current moment; Determine whether the replenishment quantity is greater than the preset replenishment quantity, if so, proceed to the next step; A purchase order for the designated medical supplies is generated based on the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation.

2. According to the SaaS cloud service-based medical digital supply chain management method of claim 1, it is characterized in that: The SaaS cloud platform adopts a distributed database.

3. According to the SaaS cloud service-based medical digital supply chain management method of claim 1, it is characterized in that: The average daily demand forecast is: Y t =μ+φ1·Y t-1 +φ2·Y t-2 +…+φ p ·Y t-p +θ1·∈ t-1 +θ2·∈ t-2 +…+θ q ·∈ t-q +∈ t ; In the formula, μ is the preset constant term, p is the preset autoregressive order, q is the preset sliding average order, and Y t-1 ,Y t-2 ,…,Y t-p is the historical demand 1, 2, …, p days before the current moment, φ1, φ2, …, φ p is the historical demand Y t-1 ,Y t-2 ,…,Y t-p The daily average demand forecast Y t The influence coefficient of t-1 ,∈ t-2 ,…,∈ t-q is the demand error term 1, 2, …, q days before the current moment, θ1, θ2, …, θ q is the demand error term∈ t-1 ,∈ t-2 ,…,∈ t-q The daily average demand forecast Y t The influence coefficient of t is the preset error term of demand at the current moment.

4. According to the SaaS cloud service-based medical digital supply chain management method of claim 1, it is characterized in that: The current replenishment quantity is: R t =max(Y t ·L,S max -I t ); Where Y t is the daily average demand forecast, L is the replenishment lead time, S max is the maximum inventory, I t is the current inventory quantity.

5. According to the SaaS cloud service-based medical digital supply chain management method of claim 1, it is characterized in that: After generating a purchase order for the designated medical supplies according to the replenishment quantity, the method further includes: Obtaining supplier information of multiple candidate suppliers matching the designated medical supplies; According to the supplier information of multiple candidate suppliers, the recommendation scores of the multiple candidate suppliers are calculated respectively; The candidate supplier with the highest recommendation score is used as the designated supplier so that the purchase order can be sent to the supply end of the designated supplier through the SaaS cloud platform.

6. A medical digital supply chain management method based on SaaS cloud service according to claim 5, characterized in that: The supplier information includes average delivery time, price deviation mean and fulfillment rate; correspondingly, the recommended score of any candidate supplier is: Score i =w1·T i +w2·C i +w3·R i ; Where, T i is the average delivery time of any candidate supplier, w1 is the preset delivery time weight, C i is the mean price deviation between any candidate supplier and other candidate suppliers, w2 is the preset price advantage weight, R i is the fulfillment rate of any candidate supplier, and w3 is the preset fulfillment rate weight.

7. A medical digital supply chain management system based on SaaS cloud service, characterized in that: include: A demand forecasting module is used to obtain the historical demand of the designated medical supplies of the designated hospital within a designated historical time period, and use a preset demand forecasting model to obtain the daily average demand forecast of the designated medical supplies of the designated hospital within a future designated time period based on the historical demand; a replenishment quantity acquisition module, which is in communication connection with the demand quantity prediction module, and is used to obtain the current inventory quantity of the designated hospital, and when the current inventory quantity is less than a preset inventory quantity threshold, obtain the maximum inventory quantity and the replenishment lead time from the current moment to the next replenishment day, and make a replenishment decision based on the average daily demand forecast, the current inventory quantity, the maximum inventory quantity and the replenishment lead time to obtain the replenishment quantity at the current moment; The replenishment information processing module is in communication with the replenishment quantity acquisition module, and is used to determine whether the replenishment quantity is greater than the preset replenishment quantity. If so, a purchase order for the designated medical supplies is generated according to the replenishment quantity, and then the purchase order is sent to the supply end of the designated supplier through the SaaS cloud platform so that the designated supplier can complete the replenishment operation.

8. An electronic device, characterized in that: include: a memory for storing computer program instructions; as well as, A processor is used to execute the computer program instructions to complete the operation of a medical digital supply chain management method based on SaaS cloud service as described in any one of claims 1 to 6.

9. A computer program product comprising a computer program or instructions, characterized in that When executed by a computer, the computer program or the instruction implements a medical digital supply chain management method based on SaaS cloud service as described in any one of claims 1 to 6.

10. A computer readable storage product, characterized in that: The computer-readable storage product stores instructions, and when the instructions are executed on a computer, a medical digital supply chain management method based on SaaS cloud service as described in any one of claims 1 to 6 is executed.

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