An emergency medical material dynamic replenishment method and system

Abstract

The application discloses an emergency medical material dynamic replenishment method and system, relates to the technical field of medical material data management, and comprises the following steps: acquiring the actual consumption rate of medical materials of a medical institution at the current time; acquiring an emergency response factor according to the risk level of a current emergency situation; acquiring a time limit and constraint factor according to the inventory situation of the medical materials of the medical institution at the current time; acquiring a geographical optimization factor according to the distribution time limit of each medical material distribution warehouse; and calculating the safe replenishment quantity of this time according to the actual consumption rate, the emergency response factor, the time limit and constraint factor and the geographical optimization factor. The application combines algorithm collaborative optimization through a mathematical model innovation, constructs a medical supply chain management system with elastic response capability, and is obviously superior to a traditional method in the dimensions of emergency timeliness, resource utilization rate and data security.

Description

Technical Field

[0001] This invention relates to the field of medical supplies data management technology, specifically a method and system for dynamic replenishment of emergency medical supplies. Background Technology

[0002] Currently, most medical institutions in China automatically generate replenishment plans based on existing inventory. The basic principle of this automatic replenishment plan generation is that the system automatically checks inventory levels, and when inventory falls below a certain threshold, replenishment is deemed necessary. Under normal circumstances, this generally meets the needs of medical institutions. However, in the event of an emergency, this model fails to meet the requirements, primarily due to the following shortcomings:

[0003] 1. Traditional replenishment strategies, based on fixed threshold triggers, cannot adapt to surges in demand caused by sudden medical events;

[0004] 2. The unique shelf-life constraints and product category differences inherent in medical consumables were not fully considered;

[0005] 3. Lack of dynamic planning capabilities for collaborative optimization of multi-level warehousing networks. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for dynamic replenishment of emergency medical supplies. Through innovative mathematical models combined with algorithmic collaborative optimization, a medical supply chain management system with elastic response capabilities is constructed, which is significantly superior to traditional methods in terms of emergency timeliness, resource utilization, and data security.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a method for dynamic replenishment of emergency medical supplies, comprising:

[0008] Obtain the actual consumption rate of medical supplies at the medical institution at any given moment;

[0009] Based on the risk level of the current emergency, obtain emergency response factors;

[0010] Based on the current inventory of medical supplies at medical institutions, obtain the timeliness constraint factor;

[0011] Based on the delivery time of each medical supply distribution warehouse, obtain geographical optimization factors;

[0012] Based on the actual consumption rate, emergency response factor, time constraint factor, and geographical optimization factor mentioned above, the quantity of safe replenishment for this operation is calculated.

[0013] Based on the above technical solution, the emergency response factor is calculated as follows:

[0014] ;

[0015] in, As an emergency response factor; This is the emergency gain coefficient, with a value range of 0.2-0.5, which controls the replenishment increment during emergency situations. The time decay constant has a value range of ≤6 / ≤12 / ≤24 hours, representing the duration of the emergency gain effect; This is the duration of the emergency response.

[0016] Based on the above technical solution, the emergency response factor is calculated as follows:

[0017]

[0018] in, As a time-constrained factor; The current average shelf life indicates the average remaining shelf life of similar materials in inventory. This is the standard expiration date threshold, representing the standard expiration date of the material. The attenuation coefficient represents the inhibitory effect of the control period on replenishment volume.

[0019] Based on the above technical solution, the geographical optimization factor is calculated as follows:

[0020]

[0021] in, Geographic optimization factor; The distance to the supply node represents the physical distance from the target warehouse to the y-th supply node. For real-time transportation speed, it represents the average speed of the transportation route from the current point to the y-th supply node.

[0022] Based on the above technical solution, the safe replenishment quantity is calculated as follows:

[0023]

[0024] in, This is the quantity of stock needed for this safe replenishment; This represents the actual rate of consumption of medical supplies. As an emergency response factor; As a time-constrained factor; This is a geographical optimization factor.

[0025] Based on the above technical solution, the risk level is divided into three levels, among which...

[0026] Time decay constant of first-order response ≤6h, emergency gain coefficient =0.5, attenuation coefficient =0.2, activation condition is a major public health event;

[0027] Time decay constant of second-order response ≤12h, emergency gain coefficient =0.35, attenuation coefficient =0.1, the activation condition is a surge in regional epidemic / emergency room visits;

[0028] The time decay constant of a third-order response ≤24h, emergency gain coefficient =0.2, attenuation coefficient =0.05, activation condition is normal operation.

[0029] This invention also discloses an emergency medical supplies dynamic replenishment system for implementing the aforementioned emergency medical supplies dynamic replenishment method, comprising: an actual consumption rate acquisition module for acquiring the actual consumption rate of medical supplies at the current moment in a medical institution; an emergency response factor calculation module for acquiring an emergency response factor based on the risk level of the current emergency; a timeliness constraint factor calculation module for acquiring a timeliness constraint factor based on the current inventory status of medical supplies in a medical institution; a geographic optimization factor calculation module for acquiring a geographic optimization factor based on the delivery timeliness of each medical supplies distribution warehouse; and a replenishment quantity calculation module for calculating the safe replenishment quantity for this operation based on the aforementioned actual consumption rate, emergency response factor, timeliness constraint factor, and geographic optimization factor.

[0030] Based on the above technical solution, a replenishment quantity estimation module is also included, which is used to calculate the possible replenishment quantity for this emergency based on the actual consumption rate of medical supplies at the current time and the duration of the event.

[0031] Based on the above technical solution, a risk level setting module is also included for setting risk levels.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention constructs a medical supply chain management system with flexible response capabilities by combining innovative mathematical models with collaborative algorithm optimization. It significantly outperforms traditional methods in terms of emergency response timeliness, resource utilization, and data security. Attached Figure Description

[0034] Figure 1 This is a flowchart of the dynamic replenishment method for emergency medical supplies in this invention;

[0035] Figure 2 This is a schematic diagram of the dynamic replenishment system for emergency medical supplies in this invention. Detailed Implementation

[0036] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0037] The following description, in conjunction with the accompanying drawings, further illustrates specific embodiments of the present invention, making the technical solution and its beneficial effects clearer and more explicit. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, but should not be construed as limiting the invention.

[0038] See Figure 1 As shown, this embodiment of the invention provides a method for dynamic replenishment of emergency medical supplies, including:

[0039] Step S1. Obtain the actual consumption rate of medical supplies at the current moment in the medical institution; specifically, based on the actual consumption rate of medical supplies at the current moment in the medical institution and the duration of the event, calculate the possible replenishment quantity for this emergency.

[0040] Based on the risk level of the current emergency, an emergency response factor is obtained; specifically, the emergency response factor is calculated as follows:

[0041]

[0042] in,

[0043] As an emergency response factor;

[0044] This is the emergency gain coefficient, with a value range of 0.2-0.5, which controls the replenishment increment during emergency situations.

[0045] The time decay constant has a value range of ≤6 / ≤12 / ≤24 hours, representing the duration of the emergency gain effect; The duration of the emergency is defined as t. Based on the urgency of the event at the medical institution, the gain coefficient and time decay constant for the emergency are determined to quantify the sustained impact of the emergency. Dynamic gain control is achieved through an exponential function to prevent replenishment volume from increasing indefinitely with the duration of the emergency. Clinical administrators can directly determine the stage of the emergency by using the value of t. The duration of the emergency, t, is a crucial bridge connecting "event identification - response decision-making." By quantifying the impact of the time dimension, the replenishment strategy can both quickly respond to sudden demands and avoid long-term resource misallocation. This can be achieved by adjusting... The value is adapted to different levels of emergency events.

[0046] Emergency response factors Analysis of Computational Advantages

[0047] Table 1. Comparison Table between This Solution and Traditional Threshold Method

[0048] Dimension This scheme E(t) Traditional threshold method Response speed First hour gain > 6% (configurable) All or none (0 / 1 trigger) Continuous Management Automatically adapt to short / medium / long-term events The status needs to be manually reset. Excessive inhibition Mathematical convergence guarantee This can easily lead to inventory backlog. Explainability Parameters are related to actual business metrics Relying on empirical rules

[0049] As shown in Table 1, compared with the traditional threshold method, the solution of this application has the following features:

[0050] 1. Quantification of Time Accumulation Effect

[0051] Advantages:

[0052] Progressive response: By using an exponential function to convert the emergency duration t into a non - linear gain, the following is achieved:

[0053] Short - term emergency (t < d): Rapidly increase the replenishment quantity (e.g., the gain reaches 63.2% in the first 6 hours)

[0054] Long - term emergency (t > 3d): Automatically converge to a steady state (preventing over - replenishment)

[0055] Clear physical meaning: t is directly related to the actual duration of the emergency event, and clinical staff can understand the replenishment situation in real - time.

[0056] Comparison with the threshold method: The traditional fixed - threshold trigger is independent of time and cannot distinguish and handle sudden large - scale medical material demands in emergency situations, resulting in either insufficient replenishment in the initial stage or a large backlog in the later stage.

[0057] 2. Multi - level Response Adaptability

[0058] Advantages:

[0059] Classification of decay constant d:

[0060] Ordinary emergency (d ≤ 6h): Quick response, short - term saturation

[0061] Regional epidemic (d ≤ 12h): Medium - term stability

[0062] Major disaster (d ≤ 24h): Long - term continuation

[0063] Adjustable gain coefficient K: Dynamically adjusted according to the event level (e.g., k = 0.3 for ordinary emergency, k = 0.6 for disaster events).

[0064] Example: In a certain hospital during the epidemic outbreak period (d = 12h, k = 0.5):

[0065] When t = 12h, E(t)=1.316 (replenishment quantity + 31.6%)

[0066] At t=24h, E(t)=1.393 (replenishment quantity +39.3%).

[0067] 3. Over-response prevention mechanism

[0068] Advantages:

[0069] Mathematical convergence guarantee: when For example, when k=0.4, the limit is 1.4, to avoid the replenishment quantity from growing indefinitely.

[0070] Automatic decay design: After the emergency ends (t=0), the factor is immediately reset to 1 to prevent interference from historical data.

[0071] Comparison with linear models: linear growth This could lead to a long-term loss of control over replenishment volume.

[0072] 4. Computational efficiency and feasibility

[0073] Advantages:

[0074] Low computational complexity: Only the exponential function and one multiplication need to be calculated, with a single run time of <100ms.

[0075] Parameter interpretability: Both k and d can be calibrated using historical data (e.g., fitting consumption curves from past epidemics).

[0076] Compared to machine learning models: black-box algorithms are difficult to verify and require a large amount of training data.

[0077] Summarize

[0078] The emergency response factor E(t) achieves the following through time-cumulative quantization, multi-level decay control, and dynamic priority adjustment:

[0079] Precise and gradual response: Avoid "under-response" or "over-hoarding";

[0080] Flexible resource allocation: adaptable to different levels of emergency events;

[0081] System self-stability: Mathematical convergence guarantees long-term reliability;

[0082] Clinical operability: The parameter settings are intuitive and easy to manually intervene.

[0083] Step S2. Obtain the timeliness constraint factor based on the current inventory status of medical supplies at the medical institution; specifically, the calculation of the timeliness constraint factor is as follows:

[0084]

[0085] in, As a time-constrained factor; The current average shelf life indicates the average remaining shelf life of similar materials in inventory. The standard expiration threshold indicates the standard shelf life of the material (e.g., 2 years for masks). The attenuation coefficient controls the degree to which the shelf life inhibits replenishment. By examining the shelf life factor in current inventory, a quadratic function is used to accelerate the attenuation of replenishment for near-expiration materials, thereby controlling the replenishment quantity of near-expiration materials and controlling costs.

[0086] Time constraint factor Analysis of computational advantages

[0087] Table 2. Comparison of this scheme with the traditional threshold method

[0088] Dimension This scheme S(T) Traditional threshold method Control precision Nonlinear continuous decay Strictly prohibit supplementation (e.g., do not supplement if the expiration date is less than 30 days). flexibility The parameters can be dynamically adjusted (max). Fixed rules synergy Intelligent interaction with emergency response factors isolated judgment Expiration loss Significantly reduce expired losses benchmark value

[0089] As shown in Table 2, compared with the traditional threshold method, the time constraint factor of this application's scheme is... The core module in the material replenishment algorithm that ensures the safety of material shelf life has significant advantages in the following aspects:

[0090] 1. Strict control over near-expiry products.

[0091] Advantages:

[0092] Secondary accelerated decay: through the square term The funds are nearing their expiration date. Tc → T When the maximum value is reached, the replenishment quantity will decrease sharply and non-linearly.

[0093] Table 3. Example Comparison (assuming g=0.1):

[0094]

[0095] Referring to the example comparison shown in Table 3, it has the following characteristics:

[0096] Key difference: The quadratic model significantly enhances the inhibition of near-expiry materials (>70%), reducing the risk of expiration.

[0097] Business value: Compared to the linear model, quadratic decay can reduce expired losses.

[0098] 2. Intelligent protection of the shelf-life safety zone

[0099] Advantages:

[0100] Three-stage dynamic control:

[0101] safe zone ( Tc <0.3 T max): S(T)≈1 S (No degradation, normal restocking)

[0102] Warning zone (0.3) T max≤ Tc <0.7 T max): Replenishment volume gradually decreases (e.g., decreases by 4.9% when the shelf life is 70%).

[0103] High-risk areas ( Tc ≥0.7 T max): Replenishment volume decreases rapidly (e.g., 8.1% reduction when 90% of the shelf life is reached).

[0104] 3. Parameters are flexible and configurable.

[0105] Advantages:

[0106] Adjustability of the attenuation coefficient g:

[0107] Standard consumables: g=0.05 (relatively controlled)

[0108] High-value consumables: g=0.2 (strictly controlled)

[0109] Shelf life threshold T MAX's product category compatibility: Pharmaceuticals, medical devices, and dressings can have different standard expiration dates set.

[0110] Application scenario: A hospital's installation of coronary stents (high unit price, sensitive to expiration date):

[0111] when Tc =360 days:

[0112]

[0113] Summarize

[0114] The time constraint factor S(T) is determined by:

[0115] Secondary decay model – precisely suppressing the replenishment of near-expiration materials;

[0116] Three-stage zoning – intelligently distinguishing between safe / alert / high-risk inventory;

[0117] Dynamic parameter configuration – adapting to the characteristics of different types of consumables.

[0118] Step S3. Obtain the geographical optimization factor based on the delivery time of each medical supply distribution warehouse; specifically, the geographical optimization factor is calculated as follows:

[0119]

[0120] in, Geographic optimization factor; The distance to the supply node represents the physical distance from the target warehouse to the y-th supply node. Real-time transport speed represents the average speed of the transport route from the current point to the y-th supply node (combined with real-time traffic data). Based on real-time transport speed and distance Calculate the optimal supply node, optimize multi-node paths using the integrated Dijkstra algorithm, calculate delivery times for multiple warehouses, and select the warehouse with the fastest delivery time.

[0121] For example: A top-tier hospital's restocking decision during a rainstorm.

[0122] Input data:

[0123] Node A: D1=40, V1=20 km / h (Speed ​​decreased due to heavy rain)

[0124] Node B: D2=80, V2=60km / h (Highway is normal)

[0125] Calculation results:

[0126]

[0127] in conclusion:

[0128] The system automatically prioritizes node B (despite its greater distance, its speed advantage is significant).

[0129] The replenishment volume is increased to 1 / 0.14 of the baseline value, approximately 7.1 times (to compensate for potential risks associated with transportation delays).

[0130] Geographic optimization factor Analysis of computational advantages

[0131] Table 4. Comparison of this scheme with the traditional distance weighting method

[0132] Evaluation Dimensions Geographic optimization factor L(G) Traditional distance weighting method Real-time Dynamically responds to changes in traffic conditions (updated every minute). Fixed quarterly updates computational complexity O(n) (where n is the number of nodes) O(n²) (Cross weights need to be calculated) Anti-interference Product form suppresses the influence of outliers A single point of failure leads to overall failure. Business adaptability Automatically match the timeliness requirements of medical supplies Distance threshold needs to be manually adjusted

[0133] As shown in Table 4, the proposed scheme has the following advantages compared to the traditional distance weighting method:

[0134] 1. Dynamically respond to real-time traffic conditions

[0135] Advantages:

[0136] Speed-sensitive compensation: When the transport speed When factors such as traffic congestion, weather effects, traffic control, and pandemics are reduced, the factor It automatically reduces and then increases the replenishment volume to compensate for the risk of transportation delays.

[0137] Example: If a certain node When the speed is reduced from 60 km / h to 30 km / h, L(G) decreases from 0.85 to 0.67, and the replenishment volume increases by approximately 27%.

[0138] Multi-node collaboration: Supports simultaneous calculation of geographical constraints of multiple supply nodes and automatically selects the optimal path (minimum path length). / ).

[0139] Compared to traditional methods, static distance models cannot reflect real-time road condition changes, which may lead to replenishment delays.

[0140] 2. Nonlinear attenuation is more in line with reality.

[0141] Advantages:

[0142] High sensitivity to distance-velocity ratio: using the reciprocal form , making when / When the factor increases (i.e., when transportation efficiency decreases), the factor value decays rapidly.

[0143] Example:

[0144] / =1 (e.g., distance is 50 km, speed is 50 km / h) → L(G) = =0.5

[0145] / =2 (e.g., distance is 100 km, speed is 50 km / h) → L(G) = ≈0.33

[0146] Transportation efficiency decreased by 50%, and replenishment volume decreased by 34% (non-linear response).

[0147] Compared to traditional linear models: If the traditional linear form is used... This approach cannot accurately reflect the increasing marginal cost effect of long-distance transportation.

[0148] 3. Multi-node scalability and flexibility

[0149] Advantages:

[0150] Product form compatible with multiple nodes: through It calculates the combined effects of multiple nodes, naturally supporting distributed warehousing networks.

[0151] Example: With 3 supply nodes:

[0152]

[0153] Automatic weight allocation: Inefficient nodes ( / Larger (larger) factors have a greater impact on the overall factors and do not require manual weighting.

[0154] Compared to the fixed-weight method: Traditional weighted average requires preset node priorities, which makes it difficult to dynamically adapt to network changes.

[0155] The geographic optimization factor L(G) achieves the following core values ​​through dynamic road condition integration, nonlinear decay design, and multi-node product:

[0156] Accurate compensation for transportation risks: Real-time speed changes are directly reflected in the replenishment quantity calculation;

[0157] Reduce losses due to expiration: Strictly limit the replenishment volume for long-haul shipments;

[0158] Improve system robustness: Naturally adapt to the addition or removal of nodes or sudden interruptions;

[0159] Reduced human intervention: No need to pre-set complex weight rules.

[0160] Step S4. Based on the actual consumption rate, emergency response factor, time constraint factor, and geographical optimization factor mentioned above, calculate the safe replenishment quantity for this operation. Specifically, the safe replenishment quantity is calculated as follows:

[0161]

[0162] in, This is the quantity of stock needed for this safe replenishment; This represents the actual rate of consumption of medical supplies. As an emergency response factor; As a time-constrained factor; This is a geographical optimization factor.

[0163] Table 5. Three-Tier Emergency Response Matrix

[0164]

[0165] As shown in Table 5, the risk levels are divided into three levels, with the time decay constant of the Level 1 response being... ≤6h, emergency gain coefficient =0.5, attenuation coefficient =0.2, activation condition is a major public health event; time decay constant of Level II response ≤12h, emergency gain coefficient =0.35, attenuation coefficient =0.1, activation condition is a surge in regional outbreaks / emergency cases; the time decay constant of a Level 3 response. ≤24h, emergency gain coefficient =0.2, attenuation coefficient =0.05, activation condition is normal operation.

[0166] See Figure 2 As shown, this invention also provides an emergency medical supplies dynamic replenishment system for implementing the aforementioned emergency medical supplies dynamic replenishment method. The system includes: an actual consumption rate acquisition module for acquiring the actual consumption rate of medical supplies at the current time; an emergency response factor calculation module for acquiring an emergency response factor based on the risk level of the current emergency; a time constraint factor calculation module for acquiring a time constraint factor based on the current inventory status of medical supplies at the medical institution; a geographic optimization factor calculation module for acquiring a geographic optimization factor based on the delivery timeliness of each medical supply distribution warehouse; and a replenishment quantity calculation module for calculating the safe replenishment quantity based on the actual consumption rate, emergency response factor, time constraint factor, and geographic optimization factor. Specifically, the emergency medical supplies dynamic replenishment system also includes a replenishment quantity estimation module for calculating the possible replenishment quantity for this emergency event based on the actual consumption rate of medical supplies at the current time and the duration of the emergency. The emergency medical supplies dynamic replenishment system also includes a risk level setting module for setting risk levels.

[0167] The present invention will be further described below through specific embodiments.

[0168] A hospital needs to replenish its stock of N95 masks during the flu season (regional epidemic status), under a Level 2 response.

[0169] D(t) = 30 units / h

[0170] T c =80 days, T max =180 days, g=0.1

[0171] Supply nodes:

[0172] Node 1: D1=40km, V1=50km / h

[0173] Node 2: D2=100km, V2=80km / h

[0174] Emergency parameters: t=10h, k=0.35, d=12h

[0175] Emergency response factors:

[0176]

[0177] E(t) = 1 + 0.35⋅(1− )≈1.198

[0178] Time constraint factor:

[0179]

[0180] S(T) = 1 − 0.1 ⋅ (80 / 180) 2 ≈0.96

[0181] Geographic optimization factors:

[0182]

[0183] L(G) = ≈0.56×0.44≈0.25

[0184] Final replenishment quantity:

[0185]

[0186] Q(t) = 30 × 1.198 × 0.98 × 0.25 ≈ 8.67 units

[0187] Based on the product specifications, the quantity to be replenished is 10 units, which is sufficient to meet the hospital's needs.

[0188] In the description of this specification, references to terms such as "an embodiment," "preferred," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. Illustrative expressions of the above terms in this specification do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0189] This invention is not limited to the embodiments described above. Those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications are also considered within the scope of protection of this invention. Contents not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for dynamic replenishment of emergency medical supplies, characterized in that, include: Obtain the actual consumption rate of medical supplies at the medical institution at any given moment; Based on the risk level of the current emergency, obtain emergency response factors; Based on the current inventory of medical supplies at medical institutions, obtain the timeliness constraint factor; Based on the delivery time of each medical supply distribution warehouse, obtain geographical optimization factors; Based on the actual consumption rate, emergency response factor, time constraint factor, and geographical optimization factor mentioned above, the quantity of safe replenishment for this operation is calculated. The emergency response factor is calculated as follows: ； in, As an emergency response factor; This is the emergency gain coefficient, with a value range of 0.2-0.5, which controls the replenishment increment during emergency situations. The time decay constant has a value range of ≤6 / ≤12 / ≤24 hours, representing the duration of the emergency gain effect; This is the duration of the emergency response; The time constraint factor is calculated as follows: ； in, As a time-constrained factor; The current average shelf life indicates the average remaining shelf life of similar materials in inventory. This is the standard expiration date threshold, representing the standard expiration date of the material. The attenuation coefficient represents the inhibitory effect of the shelf life on replenishment volume. The geographical optimization factor is calculated as follows: ； in, Geographic optimization factor; The distance to the supply node represents the physical distance from the target warehouse to the y-th supply node. For real-time transportation speed, it represents the average speed of the transportation route from the current point to the y-th supply node; The quantity of safe replenishment for this transaction is calculated as follows: ； in, This is the quantity of stock needed for this safe replenishment; This represents the actual rate of consumption of medical supplies. As an emergency response factor; As a time-constrained factor; This is a geographical optimization factor.

2. The method for dynamic replenishment of emergency medical supplies as described in claim 1, characterized in that: The risk levels are divided into three levels, among which Time decay constant of first-order response ≤6h, emergency gain coefficient =0.5, attenuation coefficient =0.2, activation condition is a major public health event; Time decay constant of second-order response ≤12h, emergency gain coefficient =0.35, attenuation coefficient =0.1, the activation condition is a surge in regional epidemic / emergency room visits; The time decay constant of a third-order response ≤24h, emergency gain coefficient =0.2, attenuation coefficient =0.05, activation condition is normal operation.

3. An emergency medical supplies dynamic replenishment system, used to implement the emergency medical supplies dynamic replenishment method according to any one of claims 1-2, characterized in that, include: The actual consumption rate acquisition module is used to acquire the actual consumption rate of medical supplies in medical institutions at the current moment. The emergency response factor calculation module is used to obtain emergency response factors based on the risk level of the current emergency situation. The timeliness constraint factor calculation module is used to obtain the timeliness constraint factor based on the current inventory status of medical supplies in medical institutions. The geographic optimization factor calculation module is used to obtain geographic optimization factors based on the delivery time of each medical supply distribution warehouse. The replenishment quantity calculation module is used to calculate the safe replenishment quantity based on the actual consumption rate, emergency response factor, time constraint factor, and geographical optimization factor mentioned above.

4. The emergency medical supplies dynamic replenishment system as described in claim 3, characterized in that: It also includes a replenishment quantity estimation module, which is used to calculate the possible replenishment quantity for this emergency based on the actual consumption rate of medical supplies at the medical institution at the current moment and the duration of the event.

5. The emergency medical supplies dynamic replenishment system as described in claim 3, characterized in that: It also includes a risk level setting module for setting risk levels.

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