Inventory management method and system based on demanded quantity of medical apparatus and instruments

Abstract

The embodiment of the invention provides an inventory management method and system based on the demand quantity of medical instruments, and the method comprises the steps: receiving the inventory information and real-time medical event information of the medical instruments in each medical institution, and obtaining the predicted demand quantity of the medical instruments corresponding to each medical institution; obtaining a consumption level coefficient and an advance period of each medical institution, determining a demand standard deviation and a historical daily average demand quantity corresponding to the medical institution, and determining a preset inventory safety value of the current medical institution according to a preset inventory safety calculation rule; and based on the inventory information, determining an inventory safety value corresponding to the medical apparatus in each medical institution, when the inventory safety value is smaller than a preset inventory safety value corresponding to the current medical institution, determining a supplement demand, a supply warehouse and a supplement route, and transporting the medical apparatus to the current medical institution according to the supplement route. According to the method, the defects of lack of real-time medical event information integration, multi-warehouse cooperative allocation and the like are effectively overcome, and the response capability of emergency replenishment is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of supply management, specifically to an inventory management method and system based on the demand for medical devices. Background Technology

[0002] In the process of medical device inventory management, the replenishment decision of medical institutions can be determined by static demand forecasting models and historical consumption data. However, it is difficult to take into account the geographical distribution characteristics of medical institutions through static demand forecasting models and historical consumption data, which may affect logistics efficiency and lead to delays in cross-regional transfer response. On the other hand, static demand forecasting models lack the ability to dynamically respond to real-time medical practices and are difficult to adapt to replenishment under scenarios of sudden demand changes.

[0003] In existing technologies, there are methods to enhance forecast accuracy through time series analysis and methods to optimize resource allocation through multi-warehouse collaboration. However, these methods lack modeling of the correlation between geospatial factors and demand fluctuations, as well as the ability to plan routes based on real-time traffic data. In other words, the coupling between geographical features and demand forecasting is insufficient, making it difficult to reflect the demand diffusion effect between regions. The forecasted replenishment volume lacks real-time data integration and multi-warehouse collaborative allocation, resulting in a lack of comparability of replenishment strategies among different medical institutions. Summary of the Invention

[0004] To address the problems in existing technologies, this application provides an inventory management method and system based on the demand for medical devices. This method effectively solves the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation. It significantly improves the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices.

[0005] To solve at least one of the above problems, this application provides the following technical solution:

[0006] In a first aspect, this application provides an inventory management method based on the demand for medical devices, including:

[0007] The system receives medical institution distribution information and map information, constructs a regional relationship map based on the medical institution distribution information, obtains medical institution grid information, and grids the map information to obtain grid map information. The medical institution distribution information is used to indicate the distribution of medical institutions and the distribution of warehouses corresponding to the warehouses storing medical devices.

[0008] It receives inventory information of medical devices and real-time medical event information from each medical institution, and processes them through time series baseline to obtain the predicted demand for medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0009] The consumption level coefficient and lead time of each medical institution are obtained. Based on inventory information, the standard deviation of demand and historical average daily demand for each medical institution are determined. According to the preset inventory safety calculation rules, the preset inventory safety value for the current medical institution is determined based on the consumption level coefficient, lead time, standard deviation of demand, and historical average daily demand. The preset inventory safety calculation rules include:

[0010] Among them, S jk Used to represent the current preset inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0011] Based on inventory information, the corresponding inventory safety value of medical devices in each medical institution is determined. If the inventory safety value is less than the preset inventory safety value corresponding to the current medical institution, the replenishment demand is determined based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of medical institutions, the supply warehouse and replenishment route are determined. The replenishment demand medical devices are extracted from the supply warehouse according to the replenishment route and transported to the current medical institution.

[0012] Furthermore, it also includes: setting map information for the distribution of medical institutions and warehouses in the distribution information of medical institutions, and marking the institutional level of medical institutions in the mapped map information;

[0013] Based on map information, the traffic network between any two medical institutions and warehouses is extracted, and the mapped map information and traffic network are determined as medical institution grid information;

[0014] The baseline grid division standard is received, and the baseline grid division standard is adjusted based on the distribution of institutions on the map information according to the institution level. The grid information of medical institutions is divided according to the adjusted baseline grid division standard to obtain grid map information.

[0015] Furthermore, it also includes: updating the inventory information of medical devices in each medical institution according to a preset update cycle; when there is no real-time medical event information, decomposing the inventory information into trend items, seasonal items and holiday items through time series baseline; processing the trend items, seasonal items and holiday items according to the oracle algorithm to obtain the predicted demand for medical devices in the medical institution.

[0016] When real-time medical event information exists, it is mapped to grid map information. Medical institutions within the mapped range are identified as event medical institutions, and real-time medical event information is identified as the corresponding emergency events for the event medical institutions. The predictive algorithm is used to process trend items, seasonal items, holiday items, and emergency events to obtain the predicted demand for medical devices for the event medical institutions.

[0017] Furthermore, it also includes: receiving the storage inventory of medical devices in each warehouse, comparing the storage inventory with the replenishment demand, and obtaining the comparison results;

[0018] When the comparison result shows that the storage inventory of the same medical institution grid as the current medical institution is greater than the replenishment demand, the warehouse that is less than the first preset distance threshold from the current medical institution is identified as the supply warehouse. The medical institution grid information includes multiple medical institution grids.

[0019] Based on grid map information, the replenishment route between the supply warehouse and the current medical institution is determined.

[0020] Furthermore, after receiving the inventory levels of medical devices in each warehouse, comparing the inventory levels with the replenishment requirements, and obtaining the comparison results, the process also includes:

[0021] When the comparison result shows that the storage inventory of the same medical institution grid as the current medical institution is not greater than the replenishment demand, the cross-regional collaboration algorithm is triggered, and the warehouses that are less than the second preset distance threshold and have a storage inventory greater than the replenishment demand are identified as supply warehouses.

[0022] Based on grid map information, the replenishment route between the supply warehouse and the current medical institution is determined.

[0023] Furthermore, it also includes: identifying medical institutions without inventory information as medical institutions awaiting processing;

[0024] In the medical institution grid information, identify the medical institution grid to be processed and the corresponding medical institution grid to be processed. In the current medical institution grid to be processed, identify other medical institutions besides the medical institution to be processed and collect the predicted demand and preset inventory safety value for the other medical institutions.

[0025] Receive information about the medical institutions to be processed and information about other medical institutions, determine the information similarity between the information about the medical institutions to be processed and the information about other institutions, and determine the predicted demand and preset inventory safety value for the medical institutions to be processed based on the information similarity, the predicted demand for other medical institutions and the preset inventory safety value.

[0026] Secondly, this application provides an inventory management system based on the demand for medical devices, comprising:

[0027] The module is used to receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and grid the map information to obtain grid map information. The medical institution distribution information is used to indicate the institutional distribution of medical institutions and the warehouse distribution corresponding to the warehouses storing medical devices.

[0028] The processing module is used to receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0029] The forecasting module is used to obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on inventory information. According to preset inventory safety calculation rules, it determines the current preset inventory safety value for the medical institution based on the consumption level coefficient, lead time, demand standard deviation, and historical average daily demand. The preset inventory safety calculation rules include:

[0030] Among them, S jk Used to represent the current preset inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0031] The replenishment module is used to determine the corresponding inventory safety value of medical devices in each medical institution based on inventory information. When the inventory safety value is less than the preset inventory safety value corresponding to the current medical institution, the replenishment demand is determined based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, the supply warehouse and replenishment route are determined. The replenishment demand medical devices are extracted from the supply warehouse according to the replenishment route and transported to the current medical institution.

[0032] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the inventory management method based on the demand of medical devices.

[0033] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the inventory management method based on the demand for medical devices.

[0034] Fifthly, this application provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the aforementioned inventory management method based on medical device demand.

[0035] As can be seen from the above technical solution, this application provides an inventory management method and system based on the demand of medical devices. It innovatively receives distribution information and map information from medical institutions to construct a regional relationship map, thereby generating grid information of medical institutions. The gridded map information is then used to obtain grid map information. The distribution information of medical institutions includes data on the distribution of medical institutions and warehouses. Medical device inventory information and real-time medical event information for each medical institution are obtained. The predicted demand is obtained through time series baseline processing. Inventory information includes historical consumption data and real-time inventory data. Based on consumption level coefficients, lead time, demand standard deviation, and historical daily average demand, a preset inventory safety calculation rule is used to determine the preset inventory level of each medical institution. The system establishes a safety margin. When the inventory safety margin falls below a preset safety margin, it calculates the replenishment demand based on predicted demand and real-time inventory data. Combining medical institution grid information and grid map information, it determines supply warehouses and replenishment routes, and executes the allocation and transportation of medical devices. This improves the accuracy and real-time performance of medical device demand forecasting. By combining medical institution distribution information, time-series baselines, and real-time medical event information data, it dynamically adjusts predicted demand to obtain the replenishment demand, effectively addressing regional sudden demand fluctuations while meeting daily replenishment needs. Simultaneously, by dynamically determining the preset safety margin for each medical institution and matching supply warehouses with corresponding replenishment routes, it reduces the risk of stockouts and expiration losses. This effectively addresses the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, significantly improving the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the inventory management method based on medical device demand in the embodiments of this application;

[0038] Figure 2 This is a structural diagram of the inventory management system based on medical device demand in the embodiments of this application;

[0039] Figure 3This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0040] Figure label:

[0041] Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver storage unit 9144, antenna 9111, speaker 9131, microphone 9132. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0044] In the existing technology, the supply and replenishment of medical devices have unique characteristics. For example, the demand for medical devices is affected by factors such as the geographical distribution of medical devices, population density, and seasonal epidemics. Due to factors such as the frequency of use, application scenarios, and medical methods used by medical institutions, the replenishment strategies of high-value consumables (such as cardiac stents) and low-value consumables (such as syringes) differ greatly among medical institutions. Furthermore, it is necessary to avoid shortages or waste due to expiration. Moreover, medical institutions located in remote areas have long delivery cycles and insufficient emergency replenishment response capabilities.

[0045] To effectively address the shortcomings of traditional technologies, such as neglecting geographical factors, lacking real-time medical event information integration, and failing to facilitate multi-warehouse collaborative allocation, and to significantly improve the allocation efficiency, quantity accuracy, and emergency replenishment responsiveness of medical devices, this application provides an embodiment of an inventory management method based on medical device demand. See [link to embodiment]. Figure 1 The inventory management method based on the demand for medical devices specifically includes the following:

[0046] Step S101: Receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and gridded map information to obtain grid map information.

[0047] Among them, the medical institution distribution information is used to indicate the institutional distribution of medical institutions and the warehouse distribution corresponding to the warehouses storing medical devices.

[0048] Optionally, this embodiment receives medical institution distribution information, including the institutional distribution of medical institutions and the warehouse distribution of medical device storage warehouses. The institutional distribution may include the location, scale, and service scope of various hospitals (general hospitals, specialized hospitals, etc.), clinics, health stations, etc., and the warehouse distribution may include the specific location, inventory capacity, and main categories of drugs and medical devices stored in each pharmaceutical warehouse within the region.

[0049] At the same time, it receives map information, which provides geographic data such as streets, blocks, topography, traffic routes, real-time traffic conditions, estimated travel time, and altitude.

[0050] Furthermore, it can also receive disaster early warning information (such as typhoon paths) and historical epicenter data, and generate regional risk heat maps based on the disaster early warning information and historical epicenter data. The regional risk heat map can be a seasonal heat map.

[0051] In addition, a regional relationship map is constructed based on the distribution information of medical institutions. That is, by analyzing factors such as the geographical distance, transportation convenience, and service coverage between medical institutions and warehouses, the entire region can be divided into several medical institution grids, thereby obtaining medical institution grid information.

[0052] At the same time, the area is gridded based on the map information to obtain grid map information, so that each grid corresponds to a specific geographical range and related medical equipment information, which can provide accurate spatial positioning basis for the allocation of medical equipment.

[0053] In the process of constructing the regional relationship map, each geographic grid can be used as a node, and the edge weights between each node can be determined. The edge weights are determined based on the actual logistics travel time or distance cost between the grid map information, thus obtaining the regional relationship map, which is used to represent the strength of logistics association between regions.

[0054] Furthermore, the number of medical institutions, number of beds, average daily operating room occupancy rate, number of delivery delays within a preset statistical period, reasons for delivery delays within a preset statistical period, and location information of competitor supply warehouses within each grid can be determined and marked in the grid map information, which can be marked in the form of codes.

[0055] This embodiment enhances the visualization and structured representation of the distribution of medical institution information, improves the accuracy of quantitative analysis of inter-regional logistics dependencies, and enhances the adaptability to the uneven location of medical institutions and warehouses.

[0056] Step S102: Receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution.

[0057] The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0058] Optionally, this embodiment receives real-time medical event information and inventory information of medical devices in each medical institution. The inventory information includes historical consumption data corresponding to the grid information of each medical institution, that is, the usage and consumption rate of various medical devices in the past preset consumption cycle, and real-time inventory data, that is, the actual inventory quantity of various drugs and medical devices in the current medical institution.

[0059] Real-time medical event information is used to represent external event data, such as various medical treatment activities currently being carried out by a medical institution, sudden medical emergencies, such as a large influx of injured people, or patients seeking medical treatment for sudden infectious diseases. It can also include procurement and bidding announcements. Real-time medical event information can be obtained through real-time web crawling.

[0060] Furthermore, real-time medical event information can be categorized into first-level medical event information, second-level medical event information, and third-level medical event information according to their urgency. The urgency of first-level medical event information is higher than that of second-level medical event information, and the urgency of second-level medical event information is higher than that of third-level medical event information.

[0061] Real-time medical event information can also be categorized into long-term, medium-term, and short-term medical event information based on the required treatment duration.

[0062] In addition, real-time medical event information can be distinguished according to other rules, and the characteristics of real-time medical event information can be determined according to multiple rules in order to update the predicted demand based on real-time medical event information.

[0063] In addition, inventory information and real-time medical event information can be analyzed through time series baseline processing to obtain the predicted demand for medical devices for each medical institution by combining historical inventory change patterns and consumption trends with real-time medical event information.

[0064] For example, if a major traffic accident causes a large number of injured people to flood into a medical institution, the demand for related drugs such as hemostatic drugs, analgesics, and surgical instruments in the short term can be estimated. Combined with inventory information, the predicted demand can be obtained, that is, the predicted demand for medical devices corresponding to each medical institution can be obtained, so as to make preparations for resource allocation in advance.

[0065] This embodiment enhances the ability to respond to sudden regional medical events and fluctuations in dynamic inventory information, improves the spatial and temporal granularity accuracy of medical device demand forecasting, thereby improving inventory management efficiency and the level of medical device supply.

[0066] Step S103: Obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on the inventory information. According to the preset inventory safety calculation rules, determine the preset inventory safety value for the current medical institution based on the consumption level coefficient, lead time, demand standard deviation and historical average daily demand.

[0067] The preset inventory safety calculation rules include:

[0068] Among them, S jk Used to represent the current preset inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0069] Optionally, this embodiment obtains the consumption level coefficient and lead time for each medical institution.

[0070] The consumption level coefficient indicates the intensity and efficiency of medical device usage by the medical institution in different medical scenarios. For example, the consumption level coefficient is relatively higher in large tertiary hospitals when performing complex surgeries, while it is relatively lower in small community clinics during routine medical care. The consumption level coefficient of large tertiary hospitals is consistently higher than that of small community clinics. Lead time represents the time required from issuing a replenishment request to the actual arrival of the medical device at the medical institution, and it is affected by various factors such as delivery efficiency, transportation distance, and traffic conditions.

[0071] In addition, based on inventory information, the standard deviation of demand and the historical average daily demand for medical institutions are determined. The standard deviation of demand is used to measure the degree of fluctuation in the medical institution's demand for medical devices. The degree of fluctuation is positively correlated with the standard deviation of demand. The standard deviation of demand can be determined based on historical consumption data and historical average daily demand. That is, the average of the squares of the difference between each historical consumption data (i.e., the actual daily consumption) and the historical average daily demand within the past preset consumption period is determined. The historical average daily demand is the average number of medical devices consumed each day within the past preset consumption period.

[0072] Based on the preset inventory safety calculation rules, and taking into account factors such as consumption level coefficient, lead time, demand standard deviation, and historical average daily demand, the preset inventory safety value of the current medical institution is determined.

[0073] The preset inventory safety calculation rules include:

[0074] Among them, S jk Used to represent the current preset inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This represents the historical average daily demand, thus deriving a reasonable and dynamic preset inventory safety value to ensure that the medical institution can meet its daily medical needs while also coping with a certain degree of sudden demand fluctuations.

[0075] This embodiment enhances the dynamism and environmental adaptability of the preset inventory safety value calculation by integrating external factors such as real-time geographical risks and traffic conditions, as well as internal demand fluctuation characteristics. This significantly improves the inventory management level of high-value / low-value medical devices, effectively balances the supply instability risks caused by regional differences and emergencies, thereby improving the service guarantee capacity and inventory turnover efficiency of medical institutions, while reducing the expiration loss of medical devices.

[0076] Step S104: Determine the inventory safety value of medical devices in each medical institution based on inventory information. If the inventory safety value is less than the preset inventory safety value of the current medical institution, determine the replenishment demand based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, determine the supply warehouse and replenishment route. Extract the medical devices required for replenishment from the supply warehouse according to the replenishment route and transport them to the current medical institution.

[0077] Optionally, this embodiment determines the inventory safety value of medical devices in each medical institution based on inventory information and monitors the inventory status of each medical institution in real time. If the inventory safety value of a certain medical device in a certain medical institution is less than the preset inventory safety value, it means that there is a risk of insufficient supply of this type of medical device in the medical institution, which may not be able to meet normal medical needs or cope with emergencies in the future, and will trigger the replenishment process.

[0078] Based on the predicted demand and real-time inventory data, the replenishment demand, i.e. the number of medical devices that need to be replenished, is determined by a simple subtraction operation: predicted demand minus real-time inventory data.

[0079] In addition, based on the established medical institution grid information and grid map information, intelligent path planning algorithms (such as Dijkstra's algorithm, ant colony algorithm, etc.) can be used to analyze the geographical distance, road conditions, traffic flow, etc. between each warehouse and the medical institution on the map information to determine the optimal supply warehouse, that is, the most suitable replenishment warehouse, which can be the warehouse that is closest and has sufficient inventory, as well as the replenishment path from the supply warehouse to the current medical institution. Among them, the replenishment path has advantages in terms of transportation time and transportation cost.

[0080] In addition, by following the replenishment route, medical devices to meet the required demand are retrieved from the supply warehouse and transported to the current medical institution, thereby ensuring the timeliness and sufficiency of the medical device supply to the medical institution and maintaining normal medical order.

[0081] Furthermore, multiple transportation modes can be integrated, such as drone delivery, cold chain transportation, and regular vehicle transportation, to select the most suitable mode based on the characteristics and urgency of the medical devices. For urgent needs, rapid delivery methods such as drone delivery can be prioritized, while for large quantities or items with special storage requirements, specialized transportation services such as cold chain transportation can be used.

[0082] This embodiment enables the determination of supply warehouses and replenishment routes by supplementing demand, which can effectively shorten transportation time and reduce transportation costs, improve the timeliness and efficiency of replenishment, reduce losses and risks during transportation, and enhance the reliability and stability of medical device supply.

[0083] This embodiment optimizes the spatial allocation structure of regional medical supplies, improves the accuracy of demand forecasting in response to sudden medical events, realizes the quantification of dynamic safety stock levels, reduces the risk of stockouts, and reduces redundant reserves.

[0084] In some embodiments, a regional relationship map is constructed based on the distribution information of medical institutions to obtain grid information of medical institutions, and the gridded map information is used to obtain grid map information, including:

[0085] Map information should be set for the distribution of medical institutions and warehouses in the distribution information of medical institutions, and the institution level of medical institutions should be marked in the mapped map information;

[0086] Based on map information, the traffic network between any two medical institutions and warehouses is extracted, and the mapped map information and traffic network are determined as medical institution grid information;

[0087] The baseline grid division standard is received, and the baseline grid division standard is adjusted based on the distribution of institutions on the map information according to the institution level. The grid information of medical institutions is divided according to the adjusted baseline grid division standard to obtain grid map information.

[0088] Optionally, this embodiment maps the distribution of medical institutions and warehouses in the distribution information to map information, that is, accurately locates the geographical location data of medical institutions and warehouses on the base map according to preset coordinate transformation rules.

[0089] In addition, the institutional level of medical institutions is marked on the mapped map information. The institutional level is used to reflect the characteristics of medical institutions such as scale, service capacity and service scope. For example, tertiary-level Class A hospitals usually have a high level of medical care and service capacity, while small clinics have a relatively small service scope.

[0090] When labeling the level of institutions, different colors, icon sizes, and numerical labels can be used to visually display the differences in level among medical institutions on the map. For example, a red five-pointed star icon can represent a Grade III Class A hospital, a blue circle icon can represent a Grade II hospital, and a small green triangle icon can represent a small clinic.

[0091] In addition, the transportation network between any two medical institutions and warehouses is extracted based on map information, namely the transportation network between warehouses and medical institutions, the transportation network between medical institutions and medical facilities, and the transportation network between warehouses. The transportation network is used to transport medical devices during the medical device allocation process.

[0092] In the process of extracting the traffic network, traffic data, such as the topology of the road network, road grade, road length, average vehicle speed, etc., can be used, and real-time traffic flow data can be combined to reflect changes in traffic conditions, so as to construct a traffic network map that includes medical institutions and warehouse nodes and the roads connecting them.

[0093] In addition, the mapped map information and transportation network are combined to form the medical institution grid information. The medical institution grid information not only includes the geographical location and level information of medical institutions and warehouses, but also incorporates the connectivity and accessibility information of the transportation network.

[0094] In addition, a baseline grid division standard is received, wherein the baseline grid division standard is used to represent the grid division rules initially determined based on a preset grid size (e.g., a side length of 1 kilometer or 5 kilometers), shape (e.g., a square or a rectangle), and taking into account a certain population density and geographical features.

[0095] Considering the differences in service area and coverage among medical institutions of different levels, and the geographical differences in their locations (e.g., the coverage of medical institutions in mountainous areas may differ from that in plains areas), directly using the baseline grid division standard may not accurately reflect the actual distribution and service capacity of medical facilities. Therefore, based on the distribution of institutions by level on the map, the baseline grid division standard is adjusted. For example, the grid area is appropriately expanded near tertiary-level hospitals to cover their larger service radius, while the grid area is appropriately reduced in areas with a high concentration of small clinics to achieve a more refined grid division.

[0096] According to the adjusted baseline grid division standard, the grid information of medical institutions is divided to obtain grid map information. The grid map information divides the entire area into multiple medical institution grid units. Each grid unit contains corresponding medical institutions, warehouses, and transportation network information, providing a clear spatial division basis for the allocation and management of medical equipment.

[0097] This embodiment realizes the construction of a regional relationship map based on the distribution information of medical institutions and obtains the grid information and grid map information of medical institutions. This enables the refined spatial management and efficient allocation of medical devices, improves the accuracy of medical device allocation, reduces resource waste, optimizes the layout planning of medical devices, and enhances the emergency response capability to deal with emergencies, ensuring the timely satisfaction of public health and medical needs.

[0098] In some embodiments, inventory information of medical devices and real-time medical event information in each medical institution are received and processed using a time-series baseline to obtain the predicted demand for medical devices corresponding to each medical institution, including:

[0099] The inventory information of medical devices in each medical institution is updated and received according to the preset update cycle. When there is no real-time medical event information, the inventory information is decomposed into trend items, seasonal items and holiday items through time series baseline. The trend items, seasonal items and holiday items are processed according to the oracle algorithm to obtain the predicted demand of medical devices in medical institutions.

[0100] When real-time medical event information exists, it is mapped to grid map information. Medical institutions within the mapped range are identified as event medical institutions, and real-time medical event information is identified as the corresponding emergency events for the event medical institutions. The predictive algorithm is used to process trend items, seasonal items, holiday items, and emergency events to obtain the predicted demand for medical devices for the event medical institutions.

[0101] Optionally, this embodiment receives inventory information from various medical institutions according to a preset update cycle. This information can be received through a secure and encrypted network communication protocol. The inventory information includes, but is not limited to, detailed information such as the real-time inventory quantity and inventory status of various supplies such as medicines and medical devices.

[0102] The preset update cycle is shown in Table 1:

[0103] Table 1 Preset Update Cycle Table

[0104] Data types Specific fields Update frequency Historical consumption data Product SKU, quantity consumed, department consuming, attending physician ID daily Inventory information Warehouse location, inventory quantity, expiration date / batch number, temperature and humidity sensor status Every minute (IoT) Real-time medical event information News of regional infectious disease outbreaks and medical device procurement tender notices Real-time web crawler

[0105] As shown in Table 1, the preset update cycle for inventory information is every minute, SKU (StockKeeping Unit) represents the smallest inventory unit, and ID (Identification) represents the identity identifier.

[0106] In the absence of real-time medical event information, i.e., under normal circumstances, the received inventory information is input into an analysis model built on a time series baseline. Through a mathematical decomposition algorithm, the inventory information is decomposed into trend, seasonal, and holiday components. The trend component represents the continuous growth or decline of medical device inventory over a long period. The seasonal component represents the regular demand fluctuations caused by seasonal changes, such as the increased demand for antiviral drugs during flu season. The holiday component represents the specific impact of statutory holidays or special cultural festivals on the demand for medical devices, such as the relatively lower demand for trauma drugs during holidays.

[0107] Furthermore, the Facebook Prophet algorithm is used to fit and analyze the decomposed trend items, using a nonlinear function to fit the long-term evolution trend of inventory. At the same time, it automatically identifies and handles outlier data points to ensure the robustness of trend prediction. For seasonal items, the Prophet algorithm constructs a periodic function model based on the periodic patterns in historical data to predict changes in medical device demand in different seasonal stages. In terms of holiday items, the algorithm treats holidays as holiday items, combines historical inventory fluctuation data corresponding to holidays to quantify the specific impact of holiday items on demand, and then generates corresponding prediction adjustment parameters. Finally, it integrates the trend items, seasonal items, and holiday items to obtain the predicted demand.

[0108] It can be expressed by the following formula: y(t)=g(t)+s(t)+h(t), where y(t) represents the predicted demand, g(t) represents the trend term, s(t) represents the seasonal term, and h(t) represents the holiday term.

[0109] In addition, upon detecting the occurrence of real-time medical event information, an emergency prediction process is triggered. The sources of real-time medical event information include, but are not limited to, emergency reporting systems of medical institutions, public health monitoring platforms, and social media channels. Natural language processing technology is used to crawl, parse, and integrate multi-source information to extract key features of the event, such as event type, time of occurrence, location, scope of impact, and expected number of people affected. Event types can include, but are not limited to, outbreaks of infectious diseases, mass casualties caused by natural disasters, and major traffic accidents.

[0110] In addition, real-time medical event information is mapped onto grid map information. That is, based on the geographical coordinates or regional description of the real-time medical event information, the specific location range of the real-time medical event information in the grid map is determined, and medical institutions within this location range are automatically converted into event medical institutions. Event medical institutions indicate that they are directly affected by the real-time medical event information and face drastic changes in the demand for medical devices.

[0111] Real-time medical event information is transformed into emergency event items corresponding to the medical institutions involved in the event. These emergency event items are added as independent variables and together with the original trend items, seasonal items, and holiday items, they constitute the input parameters of the prediction model. The Predictive Algorithm is used to comprehensively analyze these four elements to obtain the predicted demand.

[0112] In handling emergencies, the Predictive Algorithm can dynamically determine the impact and duration of real-time medical event information on the demand for medical devices based on historical data and experience of similar events, combined with the real-time dynamic characteristics of current medical event information.

[0113] For example, in the event of a sudden infectious disease outbreak, the Predictive Algorithm can refer to data such as the spread rate of similar infectious diseases in the past, the growth curve of the number of infected people, and the dosage of corresponding treatment drugs to predict the additional demand for related medical devices (such as antiviral drugs, test reagents, protective equipment, etc.). By coordinating the processing of trend items, seasonal items, holiday items, and emergency items, it can obtain the predicted demand for medical devices of medical institutions in emergency situations, providing timely and accurate demand basis for emergency material allocation and medical rescue operations.

[0114] It can be expressed by the following formula: y(t)=g(t)+s(t)+h(t)+ε, where y(t) represents the predicted demand, g(t) represents the trend term, s(t) represents the seasonal term, h(t) represents the holiday term, and ε represents the unexpected event term.

[0115] Furthermore, a graph neural network can be constructed, where the nodes of the graph neural network represent the historical demand, inventory level, and risk level of the region corresponding to each grid map information. The graph neural network can be used to simulate the degree and direction of diffusion of real-time medical event information in adjacent regions to verify the predicted demand. At the same time, the graph neural network can also be used to determine the regional deviation coefficient, which represents the overflow demand in the current region caused by the increase in demand in the adjacent regions.

[0116] Furthermore, when the difference between actual consumption and predicted demand exceeds a preset deviation threshold, enhanced training is triggered to adjust the parameters, where the actual consumption is obtained based on inventory information.

[0117] This embodiment achieves real-time reception, analysis, and dynamic prediction of medical device inventory information and real-time medical event information, thereby improving the accuracy of predicted demand for medical devices, the timeliness of emergency response, and the rationality of resource allocation, and enhancing the operational efficiency and service quality of medical institutions.

[0118] In some embodiments, determining supply warehouses and replenishment routes based on medical institution grid information and grid map information includes:

[0119] Receive the inventory of medical devices in each warehouse, compare the inventory with the replenishment demand, and obtain the comparison results;

[0120] When the comparison result shows that the storage inventory of the same medical institution grid as the current medical institution is greater than the replenishment demand, the warehouse that is less than the first preset distance threshold from the current medical institution is identified as the supply warehouse. The medical institution grid information includes multiple medical institution grids.

[0121] Based on grid map information, the replenishment route between the supply warehouse and the current medical institution is determined.

[0122] Optionally, this embodiment receives the storage inventory of each warehouse, wherein the storage inventory includes the real-time inventory levels of different types of medical devices. Simultaneously, it obtains the current replenishment demand of the medical institution, wherein the replenishment demand can be obtained based on the inventory information of medical devices and the response to real-time medical event information. The storage inventory is then compared with the replenishment demand to obtain a comparison result.

[0123] Furthermore, if the comparison results show that the inventory of warehouses within the same medical institution grid as the current medical institution exceeds the replenishment demand, then warehouses located less than a first preset distance threshold from the current medical institution will be designated as supply warehouses. This can be achieved by combining grid map information; that is, supply warehouses must not only be close to the current medical institution but also have high accessibility.

[0124] Furthermore, based on grid map information, supplementary routes between the supply warehouse and the current medical institution are determined. These routes can be derived by combining multiple factors, including real-time traffic information, road conditions, and restrictions imposed by specific areas. A hybrid intelligent algorithm combining ant colony optimization and genetic algorithms can be used to obtain these supplementary routes. The ant colony optimization algorithm, by simulating the foraging behavior of ants, can effectively find supplementary routes from the warehouse to the medical institution. The genetic algorithm further optimizes the supplementary routes by performing selection, crossover, and mutation operations on the path codes, ensuring that the supplementary routes have the shortest delivery time and the fewest traffic congestion points.

[0125] Furthermore, IoT sensor technology can be used to monitor changes in the quantity of medical devices in real time through smart sensors installed in each warehouse. The data is then transmitted to the data processing center via a secure network protocol, and the system can process data from multiple warehouses and medical institutions simultaneously, improving response speed and processing efficiency.

[0126] This embodiment achieves intelligent, efficient, and precise management of medical device supply by integrating medical institution grid information and grid map information, thereby improving the efficiency and quality of medical device supply, reducing supply costs, enhancing emergency response capabilities, and increasing the transparency and accuracy of management.

[0127] In some embodiments, after receiving the storage inventory of medical devices in each warehouse, comparing the storage inventory with the replenishment demand, and obtaining the comparison result, the method further includes:

[0128] When the comparison result shows that the storage inventory of the same medical institution grid as the current medical institution is not greater than the replenishment demand, the cross-regional collaboration algorithm is triggered, and the warehouses that are less than the second preset distance threshold and have a storage inventory greater than the replenishment demand are identified as supply warehouses.

[0129] Based on grid map information, the replenishment route between the supply warehouse and the current medical institution is determined.

[0130] Optionally, in this embodiment, after receiving the storage inventory of each warehouse and comparing it with the replenishment demand, if the storage inventory of the warehouse in the same medical institution grid as the current medical institution is not greater than the replenishment demand, a cross-regional collaboration algorithm is triggered, so that a backup supply method can be activated in a timely manner when local resources are insufficient.

[0131] In addition, after triggering the cross-regional collaboration algorithm, the search scope is expanded to the grid of adjacent medical institutions. Based on the geographical coordinates of the current medical institution, the straight-line or path distance between other warehouses and the medical institution can be determined, and a set of candidate warehouses with a distance less than a preset second distance threshold can be selected. From this candidate set, warehouses with a storage inventory greater than the replenishment demand of the medical institution are selected and marked as qualified supply warehouses.

[0132] In addition, the optimal or feasible supplementary route from the supply warehouse to the current medical institution is generated by using the regional traffic network topology data stored in the grid map information and the route planning algorithm.

[0133] Furthermore, in the absence of a supply warehouse that meets the preset constraints, the warehouse with the smallest difference from the constraints is determined as the supply warehouse.

[0134] The system obtains the storage inventory of the supply warehouse, determines the replenishment quantity based on the storage inventory and the replenishment demand, and sends a replenishment message to the supplier so that the supplier can transport the corresponding medical devices to the current medical institution based on the replenishment quantity in the replenishment message, and the supply warehouse can transport the medical devices to the current medical institution according to the replenishment route.

[0135] Among them, the preset constraints include first-in, first-out for medical devices in the stored inventory, and in the event of a real-time medical event, the replenishment time corresponding to the replenishment route shall not exceed the preset response time.

[0136] Furthermore, assuming that the inventory in each warehouse does not exceed the replenishment demand, the minimum value of the objective function is determined based on the data information of each warehouse, and the warehouse corresponding to the minimum value is determined as the supply warehouse. The data information includes total transportation cost, procurement cost, stockout penalty cost, and expiration loss cost.

[0137] The objective function is the sum of total transportation cost, procurement cost, stockout penalty cost, and expired loss cost.

[0138] This embodiment enables the determination of supply warehouses and replenishment routes from supply warehouses to current medical institutions based on grid map information, thereby improving the response efficiency of replenishment and reducing the logistics costs of cross-regional transfers.

[0139] In some embodiments, it also includes:

[0140] Medical institutions without inventory information are identified as medical institutions awaiting processing.

[0141] In the medical institution grid information, identify the medical institution grid to be processed and the corresponding medical institution grid to be processed. In the current medical institution grid to be processed, identify other medical institutions besides the medical institution to be processed and collect the predicted demand and preset inventory safety value for the other medical institutions.

[0142] Receive information about the medical institutions to be processed and information about other medical institutions, determine the information similarity between the information about the medical institutions to be processed and the information about other institutions, and determine the predicted demand and preset inventory safety value for the medical institutions to be processed based on the information similarity, the predicted demand for other medical institutions and the preset inventory safety value.

[0143] Optionally, this embodiment connects to the medical institution's inventory management database to monitor the inventory update status of each medical institution in real time. By setting a timestamp field for inventory information updates, it determines whether the medical institution's inventory information has been updated within the specified period.

[0144] If a medical institution's inventory information is not updated within a preset time or if the medical institution has no inventory information, it will be identified as a medical institution to be processed, and its unique identifier, location information, and basic information such as the medical institution grid to which it belongs will be recorded.

[0145] In addition, in the medical institution grid information, the grid corresponding to the medical institution to be processed can be determined by matching the grid code with the geographical location of the medical institution. Within the grid of the medical institution to be processed, other medical institutions besides the medical institution to be processed are identified, and the predicted demand and preset inventory safety value of other medical institutions are collected. The predicted demand is obtained based on the oracle algorithm combined with time series analysis, and the preset inventory safety value is calculated based on the consumption level coefficient, lead time, demand standard deviation and historical daily average demand.

[0146] In addition, it receives information on medical institutions awaiting processing, including hospital level, department setup, service population, historical disease spectrum, etc., and also collects information on other medical institutions.

[0147] The information of the institution to be processed and the information of other institutions are standardized to eliminate the difference in the units of measurement between different indicators. The similarity between the information of the institution to be processed and the information of other institutions is determined by the cosine similarity algorithm. By comparing the vector angles of different medical institutions in each information dimension, the information similarity value is obtained. The larger the value, the higher the similarity.

[0148] In addition, the predicted demand and preset safety stock value of the medical institutions to be processed are weighted based on the similarity value to obtain the predicted demand and preset safety stock value of the medical institutions to be processed. Among them, the demand and safety stock value of medical institutions with high similarity have a larger weight in the calculation.

[0149] This embodiment addresses the issue of missing inventory information in some medical institutions, improves the data foundation of the medical device allocation system, enhances the accuracy, rationality, and emergency response capabilities of resource allocation, optimizes the configuration efficiency of medical devices, reduces operating costs, ensures the stability and reliability of medical services, and improves the quality and efficiency of medical device allocation.

[0150] To effectively address the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and hindering multi-warehouse collaborative allocation, and to significantly improve the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices, this application provides an embodiment of a medical device demand-based inventory management system for implementing all or part of the aforementioned medical device demand-based inventory management. See [link to embodiment]. Figure 2 The inventory management based on medical device demand specifically includes the following:

[0151] Module 10 is used to receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and grid the map information to obtain grid map information. The medical institution distribution information is used to indicate the institutional distribution of medical institutions and the warehouse distribution corresponding to the warehouses storing medical devices.

[0152] The processing module 20 is used to receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0153] The prediction module 30 is used to obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on inventory information. According to preset inventory safety calculation rules, it determines the current preset inventory safety value for the medical institution based on the consumption level coefficient, lead time, demand standard deviation, and historical average daily demand. The preset inventory safety calculation rules include:

[0154] Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0155] The supplement module 40 is used to determine the corresponding inventory safety value of medical devices in each medical institution based on inventory information. When the inventory safety value is less than the preset inventory safety value corresponding to the current medical institution, the supplementary demand is determined based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, the supply warehouse and supplementary route are determined. The supplementary medical devices are extracted from the supply warehouse according to the supplementary route and transported to the current medical institution.

[0156] As described above, the inventory management system based on medical device demand provided in this application can innovatively construct a regional relationship map by receiving medical institution distribution information and map information, thereby generating medical institution grid information and obtaining grid map information by gridding the map information. The medical institution distribution information includes medical institution distribution and warehouse distribution data. It acquires medical device inventory information and real-time medical event information for each medical institution, and obtains predicted demand through time series baseline processing. The inventory information includes historical consumption data and real-time inventory data. Based on consumption level coefficients, lead time, demand standard deviation, and historical daily average demand, it determines the preset inventory of medical institutions through preset inventory safety calculation rules. This method establishes a safety margin. When the inventory safety margin falls below a preset safety margin, the replenishment demand is calculated based on predicted demand and real-time inventory data. By combining medical institution grid information and grid map information, supply warehouses and replenishment routes are determined, and medical device allocation and transportation are executed. This improves the accuracy and real-time performance of medical device demand forecasting. By combining medical institution distribution information, time-series baselines, and real-time medical event information data, the predicted demand is dynamically adjusted to obtain the replenishment demand, effectively addressing regional sudden demand fluctuations while meeting daily replenishment needs. Simultaneously, by dynamically determining the preset safety margin for each medical institution and matching supply warehouses with corresponding replenishment routes, the risk of stockouts and expiration losses is reduced. This method effectively addresses the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, significantly improving the allocation efficiency, quantity accuracy, and emergency replenishment response capability of medical devices.

[0157] From a hardware perspective, in order to effectively address the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, and to significantly improve the allocation efficiency, quantity accuracy, and emergency replenishment response capability of medical devices, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned inventory management method based on medical device demand. The electronic device specifically includes the following components:

[0158] The system comprises a processor, memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the medical device demand-based inventory management system and core business systems, user terminals, and related databases and other related devices; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the medical device demand-based inventory management method and the medical device demand-based inventory management system, the content of which is incorporated herein, and repeated details will not be described again.

[0159] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0160] In practical applications, the inventory management method based on medical device demand can be partially executed on the electronic device side as described above, or all operations can be completed on the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed on the client device, the client device may further include a processor.

[0161] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed system server structure.

[0162] Figure 3 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 3 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0163] In one embodiment, the inventory management method based on medical device demand can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following controls:

[0164] Step S101: Receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and gridded map information to obtain grid map information. The medical institution distribution information is used to indicate the distribution of medical institutions and the distribution of warehouses corresponding to the warehouses storing medical devices.

[0165] Step S102: Receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0166] Step S103: Obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on inventory information. According to the preset inventory safety calculation rules, determine the current preset inventory safety value for the medical institution based on the consumption level coefficient, lead time, demand standard deviation, and historical average daily demand. The preset inventory safety calculation rules include:

[0167] Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0168] Step S104: Determine the inventory safety value of medical devices in each medical institution based on inventory information. If the inventory safety value is less than the preset inventory safety value of the current medical institution, determine the replenishment demand based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, determine the supply warehouse and replenishment route. Extract the medical devices required for replenishment from the supply warehouse according to the replenishment route and transport them to the current medical institution.

[0169] As described above, the electronic device provided in this application embodiment innovatively constructs a regional relationship map by receiving medical institution distribution information and map information, thereby generating medical institution grid information and obtaining grid map information by gridding the map information. The medical institution distribution information includes medical institution distribution and warehouse distribution data. It acquires medical device inventory information and real-time medical event information for each medical institution, and obtains predicted demand through time series baseline processing. Inventory information includes historical consumption data and real-time inventory data. Based on consumption level coefficients, lead time, demand standard deviation, and historical daily average demand, it determines the preset inventory safety value for medical institutions through preset inventory safety calculation rules. When the inventory safety value falls below the preset safety value, the replenishment demand is calculated based on the predicted demand and real-time inventory data. This is combined with medical institution grid information and grid map information to determine supply warehouses and replenishment routes, enabling the allocation and transportation of medical devices. This improves the accuracy and real-time nature of medical device demand forecasting. By combining medical institution distribution information, time-series baselines, and real-time medical event information data, the predicted demand is dynamically adjusted to obtain the replenishment demand, effectively addressing regional sudden demand fluctuations while meeting daily replenishment needs. Simultaneously, by dynamically determining the preset safety value of each medical institution and matching supply warehouses with corresponding replenishment routes, the risk of stockouts and expiration losses is reduced. This effectively addresses the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, significantly improving the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices.

[0170] In another embodiment, the inventory management system based on medical device demand can be configured separately from the central processing unit 9100. For example, the inventory management system based on medical device demand can be configured as a chip connected to the central processing unit 9100, and the inventory management method function based on medical device demand can be implemented through the control of the central processing unit.

[0171] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 3 All components shown; in addition, the electronic device 9600 may also include Figure 3 For components not shown, please refer to existing technologies.

[0172] like Figure 3 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor system and / or logic system, which receives inputs and controls the operation of various components of the electronic device 9600.

[0173] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable systems. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0174] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input system. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0175] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of system. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0176] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0177] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0178] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored audio via the speaker 9131.

[0179] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the inventory management method based on medical device demand, where the execution subject is a server or client, as described in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the inventory management method based on medical device demand, where the execution subject is a server or client, as described in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0180] Step S101: Receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and gridded map information to obtain grid map information. The medical institution distribution information is used to indicate the distribution of medical institutions and the distribution of warehouses corresponding to the warehouses storing medical devices.

[0181] Step S102: Receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0182] Step S103: Obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on inventory information. According to the preset inventory safety calculation rules, determine the current preset inventory safety value for the medical institution based on the consumption level coefficient, lead time, demand standard deviation, and historical average daily demand. The preset inventory safety calculation rules include:

[0183] Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand.jk This indicates the historical average daily demand.

[0184] Step S104: Determine the inventory safety value of medical devices in each medical institution based on inventory information. If the inventory safety value is less than the preset inventory safety value of the current medical institution, determine the replenishment demand based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, determine the supply warehouse and replenishment route. Extract the medical devices required for replenishment from the supply warehouse according to the replenishment route and transport them to the current medical institution.

[0185] As described above, the computer-readable storage medium provided in this application embodiment innovatively constructs a regional relationship map by receiving medical institution distribution information and map information, thereby generating medical institution grid information and gridding the map information to obtain grid map information. The medical institution distribution information includes medical institution distribution and warehouse distribution data. It acquires medical device inventory information and real-time medical event information for each medical institution, and obtains predicted demand through time series baseline processing. The inventory information includes historical consumption data and real-time inventory data. Based on consumption level coefficients, lead time, demand standard deviation, and historical daily average demand, it determines the preset inventory safety value for medical institutions through preset inventory safety calculation rules. When the inventory safety value falls below the preset inventory safety value, the replenishment demand is calculated based on the predicted demand and real-time inventory data. By combining medical institution grid information and grid map information, supply warehouses and replenishment routes are determined, and the allocation and transportation of medical devices are executed. This improves the accuracy and real-time performance of medical device demand forecasting. By combining medical institution distribution information, time-series baselines, and real-time medical event information data, the predicted demand is dynamically adjusted to obtain the replenishment demand, effectively responding to regional sudden demand fluctuations while meeting daily replenishment needs. Simultaneously, by dynamically determining the preset inventory safety value for each medical institution and matching supply warehouses with corresponding replenishment routes, the risk of stockouts and expiration losses is reduced. This effectively addresses the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, significantly improving the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices.

[0186] Embodiments of this application also provide a computer program product capable of implementing all steps of the inventory management method based on medical device demand, where the execution subject is a server or client, as described in the above embodiments. When executed by a processor, this computer program / instruction implements the steps of the aforementioned inventory management method based on medical device demand. For example, the computer program / instruction implements the following steps:

[0187] Step S101: Receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and gridded map information to obtain grid map information. The medical institution distribution information is used to indicate the distribution of medical institutions and the distribution of warehouses corresponding to the warehouses storing medical devices.

[0188] Step S102: Receive the inventory information of medical devices and real-time medical event information of each medical institution, and process them through time series baseline to obtain the predicted demand of medical devices for each medical institution. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each medical institution.

[0189] Step S103: Obtain the consumption level coefficient and lead time for each medical institution, and determine the corresponding demand standard deviation and historical average daily demand based on inventory information. According to the preset inventory safety calculation rules, determine the current preset inventory safety value for the medical institution based on the consumption level coefficient, lead time, demand standard deviation, and historical average daily demand. The preset inventory safety calculation rules include:

[0190] Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk Indicates the lead time, σ jk D represents the standard deviation of demand. jk This indicates the historical average daily demand.

[0191] Step S104: Determine the inventory safety value of medical devices in each medical institution based on inventory information. If the inventory safety value is less than the preset inventory safety value of the current medical institution, determine the replenishment demand based on the predicted demand and real-time inventory data. Based on the grid information and grid map information of the medical institutions, determine the supply warehouse and replenishment route. Extract the medical devices required for replenishment from the supply warehouse according to the replenishment route and transport them to the current medical institution.

[0192] As described above, the computer program product provided in this application innovatively constructs a regional relationship map by receiving medical institution distribution information and map information, thereby generating medical institution grid information and gridding the map information to obtain grid map information. The medical institution distribution information includes medical institution distribution and warehouse distribution data. It acquires medical device inventory information and real-time medical event information for each medical institution, and obtains predicted demand through time series baseline processing. Inventory information includes historical consumption data and real-time inventory data. Based on consumption level coefficients, lead time, demand standard deviation, and historical daily average demand, it determines the preset inventory safety value for medical institutions through preset inventory safety calculation rules. When the inventory safety value falls below the preset safety value, the replenishment demand is calculated based on the predicted demand and real-time inventory data. This is combined with medical institution grid information and grid map information to determine supply warehouses and replenishment routes, and then the allocation and transportation of medical devices are executed. This improves the accuracy and real-time performance of medical device demand forecasting. By combining medical institution distribution information, time-series baselines, and real-time medical event information data, the predicted demand is dynamically adjusted to obtain the replenishment demand, effectively addressing regional sudden demand fluctuations while meeting daily replenishment needs. Simultaneously, by dynamically determining the preset inventory safety value for each medical institution and matching supply warehouses with corresponding replenishment routes, the risk of stockouts and expiration losses is reduced. This effectively addresses the shortcomings of traditional technologies, such as ignoring geographical factors, lacking real-time medical event information integration, and multi-warehouse collaborative allocation, significantly improving the allocation efficiency, quantity accuracy, and emergency replenishment response capabilities of medical devices.

[0193] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0195] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0197] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. An inventory management method based on the demand for medical devices, characterized in that, The method includes: The system receives medical institution distribution information and map information, constructs a regional relationship map based on the medical institution distribution information to obtain medical institution grid information, and grids the map information to obtain grid map information. The medical institution distribution information is used to indicate the distribution of medical institutions and the warehouse distribution corresponding to the warehouses storing medical devices. The system receives inventory information and real-time medical event information of the medical devices in each of the medical institutions, and processes them through a time series baseline to obtain the predicted demand for the medical devices corresponding to each of the medical institutions. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each of the medical institutions. The consumption level coefficient and lead time of each medical institution are obtained, and the demand standard deviation and historical average daily demand corresponding to the medical institution are determined based on inventory information. According to preset inventory safety calculation rules, the preset inventory safety value of the current medical institution is determined based on the consumption level coefficient, the lead time, the demand standard deviation, and the historical average daily demand. The preset inventory safety calculation rules include: Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk σ represents the lead time. jk D represents the standard deviation of the demand. jk This represents the historical average daily demand. Based on the inventory information, the inventory safety value corresponding to the medical devices in each medical institution is determined. If the inventory safety value is less than the preset inventory safety value corresponding to the current medical institution, the replenishment demand is determined based on the predicted demand and the real-time inventory data. Based on the medical institution grid information and the grid map information, the supply warehouse and replenishment route are determined. The medical devices required for the replenishment demand are extracted from the supply warehouse according to the replenishment route and transported to the current medical institution.

2. The method according to claim 1, characterized in that, The process of constructing a regional relationship map based on the distribution information of medical institutions to obtain grid information of medical institutions, and then gridding the map information to obtain grid map information, includes: The distribution of medical institutions and the distribution of warehouses in the distribution information of medical institutions should be set in the map information, and the institution level of the medical institutions should be marked in the mapped map information; Based on the map information, the traffic network between any two medical institutions and the warehouse is extracted, and the mapped map information and the traffic network are determined as medical institution grid information; The system receives a baseline grid division standard, adjusts the baseline grid division standard based on the distribution of institutions on the map information according to the institution level, and divides the medical institution grid information according to the adjusted baseline grid division standard to obtain the grid map information.

3. The method according to claim 1, characterized in that, The process of receiving inventory information and real-time medical event information of the medical devices in each of the medical institutions, and processing them through a time series baseline to obtain the predicted demand for the medical devices corresponding to each of the medical institutions includes: The inventory information of the medical devices in each medical institution is updated and received according to a preset update cycle. When there is no real-time medical event information, the inventory information is decomposed into trend items, seasonal items and holiday items through time series baseline. The trend items, seasonal items and holiday items are processed according to the oracle algorithm to obtain the predicted demand of the medical devices in the medical institution. When the real-time medical event information exists, the real-time medical event information is mapped to the grid map information, and the medical institutions within the mapping range are identified as event medical institutions. The real-time medical event information is identified as the corresponding emergency event item of the event medical institution. The trend item, the seasonal item, the holiday item and the emergency event item are processed according to the oracle algorithm to obtain the predicted demand for the medical devices of the event medical institution.

4. The method according to claim 1, characterized in that, The process of determining supply warehouses and replenishment routes based on the medical institution grid information and the grid map information includes: Receive the storage inventory of the medical devices in each of the warehouses, compare the storage inventory with the replenishment demand, and obtain the comparison result; When the comparison result shows that the storage inventory of the medical institution in the same medical institution grid as the current medical institution is greater than the replenishment demand, the warehouse that is less than a first preset distance threshold from the current medical institution is identified as a supply warehouse. The medical institution grid information includes multiple medical institution grids. Based on the grid map information, a replenishment route is determined between the supply warehouse and the current medical institution.

5. The method according to claim 4, characterized in that, After receiving the storage inventory of the medical devices in each of the warehouses, comparing the storage inventory with the replenishment demand, and obtaining the comparison result, the process further includes: When the comparison result shows that the storage inventory of the medical institution in the same medical institution grid as the current medical institution is not greater than the replenishment demand, a cross-regional collaboration algorithm is triggered to determine the warehouse corresponding to the distance between the current medical institution and the current medical institution being less than a second preset distance threshold and the storage inventory being greater than the replenishment demand as the supply warehouse. Based on the grid map information, a replenishment route is determined between the supply warehouse and the current medical institution.

6. The method according to claim 1, characterized in that, Also includes: The medical institutions that do not have the aforementioned inventory information are identified as medical institutions awaiting processing. In the medical institution grid information, determine the medical institution grid corresponding to the medical institution to be processed, determine other medical institutions besides the medical institution to be processed in the current medical institution grid, and collect the predicted demand and the preset inventory safety value corresponding to the other medical institutions. The system receives information about the medical institution to be processed and information about other medical institutions, determines the information similarity between the information about the medical institution to be processed and the information about other medical institutions, and determines the predicted demand and the preset inventory safety value for the medical institution to be processed based on the information similarity, the predicted demand corresponding to the other medical institutions, and the preset inventory safety value.

7. An inventory management system based on the demand for medical devices, characterized in that, The system includes: The module is used to receive medical institution distribution information and map information, construct a regional relationship map based on the medical institution distribution information, obtain medical institution grid information, and grid the map information to obtain grid map information. The medical institution distribution information is used to indicate the institutional distribution of medical institutions and the warehouse distribution corresponding to the warehouses storing medical devices. The processing module is used to receive the inventory information and real-time medical event information of the medical devices in each of the medical institutions, and process them through time series baseline to obtain the predicted demand of the medical devices corresponding to each of the medical institutions. The inventory information includes historical consumption data and real-time inventory data corresponding to the grid information of each of the medical institutions. The prediction module is used to obtain the consumption level coefficient and lead time for each medical institution, and determine the demand standard deviation and historical average daily demand for the corresponding medical institution based on inventory information. According to preset inventory safety calculation rules, it determines the current preset inventory safety value for the medical institution based on the consumption level coefficient, the lead time, the demand standard deviation, and the historical average daily demand. The preset inventory safety calculation rules include: Among them, S jk Used to represent the current inventory safety value, Z represents the consumption level coefficient, and L... jk σ represents the lead time. jk D represents the standard deviation of the demand. jk This represents the historical average daily demand. The replenishment module is used to determine the inventory safety value of medical devices in each of the medical institutions based on the inventory information. If the inventory safety value is less than the preset inventory safety value corresponding to the current medical institution, the module determines the replenishment demand based on the predicted demand and the real-time inventory data. Based on the medical institution grid information and the grid map information, the module determines the supply warehouse and replenishment route, and retrieves the medical devices required for the replenishment demand from the supply warehouse according to the replenishment route and transports them to the current medical institution.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the inventory management method based on the demand for medical devices as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the inventory management method based on the demand for medical devices as described in any one of claims 1 to 6.

10. A computer program product for use in computer programs / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the inventory management method based on the demand for medical devices as described in any one of claims 1 to 6.

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