Emergency medical material dynamic replenishment method and system

Abstract

The invention discloses an emergency medical material dynamic replenishment method and system, and relates to the technical field of medical material data management.The emergency medical material dynamic replenishment method comprises the steps that the actual consumption rate of medical materials of a medical institution at the current moment is obtained; acquiring an emergency response factor according to the risk level of the current emergency; obtaining an aging constraint factor according to the inventory condition of the medical materials of the medical institution at the current moment; obtaining a geographic optimization factor according to the distribution time efficiency of each medical material distribution warehouse; and according to the actual consumption rate, the emergency response factor, the aging constraint factor and the geographical optimization factor, the safe replenishment quantity of this time is calculated. According to the method, mathematical model innovation is combined with algorithm collaborative optimization, a medical supply chain management system with elastic response capability is constructed, and the method is remarkably superior to a traditional method in the aspects of emergency timeliness, resource utilization rate, data security and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of medical supplies data management, and in particular to a method and system for dynamic replenishment of emergency medical supplies. Background Art

[0002] Currently, most medical institutions in China automatically generate replenishment plans based on inventory. The basic principle behind this automatic replenishment plan is that the system automatically searches for inventory and determines that replenishment is necessary when inventory falls below a certain limit. In non-emergency situations, this model can generally meet the needs of medical institutions. However, in emergencies, this model fails to meet requirements and suffers from the following main drawbacks:

[0003] 1. Traditional replenishment strategies are triggered by fixed thresholds and cannot adapt to the surge in demand caused by sudden medical events;

[0004] 2. Failure to fully consider the unique expiration date constraints and category differences of medical consumables;

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

[0006] In response to the defects in the existing technology, the purpose of the present invention is to provide a dynamic replenishment method and system for emergency medical supplies. Through the innovation of mathematical models combined with algorithm collaborative optimization, a medical supply chain management system with flexible 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 present invention adopts a technical solution: a method for dynamic replenishment of emergency medical supplies, comprising:

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

[0009] Obtain emergency response factors based on the risk level of the current emergency situation;

[0010] Obtain the time constraint factor based on the current inventory status of medical supplies in the medical institution;

[0011] Obtain geographical optimization factors based on the delivery time of each medical supplies distribution warehouse;

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

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

[0014] E(t)=1+k·(1-e -t / d )

[0015] Among them, E(t) is the emergency response factor; k is the emergency gain coefficient, with a value range of 0.2 - 0.5, controlling the replenishment increment amplitude in the emergency state; d is the time decay constant, with a value range of ≤6 / ≤12 / ≤24 hours, representing the duration of the emergency gain effect; t is the duration of the ongoing emergency.

[0016] Based on the above technical solution, the calculation of the timeliness constraint factor is as follows:

[0017]

[0018] Among them, S(T) is the timeliness constraint factor; T c is the current average expiration date, representing the average remaining expiration date of the same type of materials in the inventory; T max is the standard expiration date threshold, representing the standard expiration date of the materials; g is the decay coefficient, controlling the suppression intensity of the expiration date on the replenishment quantity.

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

[0020]

[0021] Among them, L(G) is the geographical optimization factor; D k is the supply node distance, representing the physical distance from the target warehouse to the k-th supply node; V k is the real-time transportation speed, representing the average speed of the transportation route to the k-th supply node currently.

[0022] Based on the above technical solution, the calculation of the safe replenishment quantity this time is as follows:

[0023] Q(t) = D(t) × E(t) × S(T) × L(G)

[0024] Among them, Q(t) is the safe replenishment quantity this time; D(t) is the actual consumption rate of medical supplies; E(t) is the emergency response factor; S(T) is the timeliness constraint factor; L(G) is the geographical optimization factor.

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

[0026] For the first-level response, the time decay constant d ≤ 6h, the emergency gain coefficient k = 0.5, the decay coefficient g = 0.2, and the activation condition is a major public health event;

[0027] For the second-level response, the time decay constant d ≤ 12h, the emergency gain coefficient k = 0.35, the decay coefficient g = 0.1, and the activation condition is a regional epidemic / emergency surge;

[0028] The time attenuation constant of the third-level response is d≤24h, the emergency gain coefficient k=0.2, the attenuation coefficient g=0.05, and the activation condition is normal operation.

[0029] On the basis of the above technical solution, the possible replenishment quantity of this emergency event is calculated based on the actual consumption rate of medical supplies at the medical institution at the current moment and the duration.

[0030] The present invention also discloses a dynamic replenishment system for emergency medical supplies, which is used to implement the above-mentioned dynamic replenishment method for emergency medical supplies, including: an actual consumption rate acquisition module, which is used to obtain the actual consumption rate of medical supplies of the medical institution at the current moment; an emergency response factor calculation module, which is used to obtain the emergency response factor according to the risk level of the current emergency situation; a time constraint factor calculation module, which is used to obtain the time constraint factor according to the inventory situation of medical supplies of the medical institution at the current moment; a geographical optimization factor calculation module, which is used to obtain the geographical optimization factor according to the delivery time of each medical supply distribution warehouse; and a replenishment quantity calculation module, which is used to calculate the safe replenishment quantity for this time based on the above-mentioned actual consumption rate, emergency response factor, time constraint factor and geographical optimization factor.

[0031] Based on the above technical solution, it also includes a replenishment quantity estimation module, which is used to calculate the possible replenishment quantity of this emergency event based on the actual consumption rate of medical supplies at the medical institution at the current moment and the duration.

[0032] On the basis of the above technical solution, it also includes a risk level setting module for setting the risk level.

[0033] The beneficial effects of the present invention are:

[0034] This invention combines mathematical model innovation with algorithm collaborative optimization to construct a medical supply chain management system with flexible response capabilities, which is significantly superior to traditional methods in terms of emergency timeliness, resource utilization and data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the method for dynamic replenishment of emergency medical supplies in the present invention;

[0036] Figure 2 This is a principle block diagram of the dynamic replenishment system for emergency medical supplies in the present invention. DETAILED DESCRIPTION

[0037] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0038] Combined with the accompanying drawings of the specification, the specific embodiments of the present invention are further described below, making the technical solutions and their beneficial effects of the present invention clearer and more definite. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention, but should not be construed as limiting the present invention.

[0039] See Figure 1 As shown, an emergency medical supplies dynamic replenishment method provided by an embodiment of the present invention includes:

[0040] Step S1. Obtain the actual consumption rate of medical supplies at the current moment of the medical institution; specifically, based on the actual consumption rate of medical supplies at the current moment of the medical institution and the elapsed time, calculate the possible replenishment quantity for this emergency event.

[0041] According to the risk level of the current emergency situation, obtain the emergency response factor; specifically, the calculation of the emergency response factor is as follows:

[0042] E(t) = 1 + k·(1 - e -t / d )

[0043] Wherein, E(t) is the emergency response factor; k is the emergency gain coefficient, and its value range is 0.2 - 0.5, which controls the replenishment increment amplitude in the emergency state; d is the time decay constant, and its value range is ≤6 / ≤12 / ≤24 hours, indicating the duration of the emergency gain effect; t is the elapsed emergency time. According to the urgency of this event of the medical institution, determine the emergency gain coefficient and the time decay constant, quantify the continuous impact of the emergency event, and achieve dynamic gain control through the exponential function to avoid the replenishment quantity increasing infinitely with the emergency time. Clinical managers can directly judge which stage of the emergency the system is in through the value of t. The elapsed emergency time t is the key bridge connecting "event recognition - response decision". By quantifying the impact of the time dimension, the replenishment strategy can not only quickly respond to sudden demands but also avoid long-term resource misallocation. By adjusting the value of d, it is adapted to different levels of emergency events.

[0044] Analysis of the calculation advantages of the emergency response factor E(t)

[0045] Table 1. Comparison table of this solution and the traditional threshold method

[0046] Dimension This solution E(t) Traditional threshold method Response speed First-hour gain > 6% (configurable) All-or-nothing (0 / 1 trigger) Continuous management Automatically adapt to short / medium / long-term events Manual reset of status required Over-suppression Mathematical convergence guarantee Prone to inventory backlog Interpretability Parameters associated with actual business metrics Rely on empirical rules

[0047] As shown in Table 1, compared with the traditional threshold method, the solution of this application has:

[0048] 1. Quantification of the time accumulation effect

[0049] Advantages:

[0050] Progressive response: Through the exponential function 1 - e -t / dConvert the emergency duration t into a non - linear gain to achieve:

[0051] Short - term emergency (t < d): Rapidly increase the replenishment quantity (e.g., the gain reaches 63.2% in the first 6 hours)

[0052] Long - term emergency (t > 3d): Automatically converge to the steady state (prevent over - replenishment)

[0053] 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.

[0054] Comparison with the threshold method: The traditional fixed - threshold trigger has nothing to do with time and cannot distinguish and handle sudden large demands for medical supplies in emergency situations, resulting in either insufficient replenishment in the initial stage or a large backlog in the later stage.

[0055] 2. Multi - level response adaptability

[0056] Advantages:

[0057] Classification of the decay constant d:

[0058] Ordinary emergency (d ≤ 6h): Quick response, short - term saturation

[0059] Regional epidemic (d ≤ 12h): Medium - term stability

[0060] Major disaster (d ≤ 24h): Long - term persistence

[0061] The gain coefficient K is adjustable: Dynamically adjusted according to the event level (e.g., k = 0.3 for ordinary emergency, k = 0.6 for disaster events).

[0062] Example: A certain hospital during the epidemic outbreak period (d = 12h, k = 0.5):

[0063] When t = 12h, E(t)=1.316 (replenishment quantity + 31.6%)

[0064] When t = 24h, E(t)=1.393 (replenishment quantity + 39.3%)

[0065] 3. Anti - over - response mechanism

[0066] Advantages:

[0067] Mathematical convergence guarantee: When t → ∞, E(t) → 1 + k (when k = 0.4, the limit is 1.4), avoiding infinite growth of the replenishment quantity.

[0068] Automatic attenuation design: After the emergency ends (t = 0), the factor immediately resets to 1 to prevent interference from historical data.

[0069] Comparison with the linear model: Linear growth E(t)=1 + αt will lead to out - of - control long - term replenishment quantity.

[0070] 4. Computational Efficiency and Feasibility

[0071] Advantages:

[0072] Low computational complexity: Only the exponential function and one multiplication need to be calculated, and the single - run time is < 100ms.

[0073] Parameter interpretability: Both k and d can be calibrated through historical data (such as fitting the consumption curve in past epidemics).

[0074] Compared with machine - learning models: Black - box algorithms are difficult to verify and require a large amount of training data.

[0075] Summary

[0076] The emergency response factor E(t) realizes the following through time - cumulative quantization, multi - level decay control, and dynamic priority adjustment:

[0077] Precise progressive response: Avoid "under - reaction" or "excessive hoarding";

[0078] Resource elastic allocation: Adapt to emergency events of different levels;

[0079] System self - stability: Mathematical convergence ensures long - term reliability;

[0080] Clinical operability: Parameter settings are intuitive and convenient for manual intervention.

[0081] Step S2. Obtain the time - effect constraint factor according to the current inventory of medical supplies in the medical institution; specifically, the calculation of the time - effect constraint factor is as follows:

[0082]

[0083] where S(T) is the time - effect constraint factor; T c is the current average expiration date, representing the average remaining expiration date of the same type of supplies in the inventory; T max is the standard expiration - date threshold, representing the standard expiration date of the supplies (such as 2 years for masks); g is the decay coefficient, controlling the suppression intensity of the expiration date on the replenishment quantity. Check the expiration - date factor of the current inventory, accelerate the replenishment decay of near - expiration supplies through a quadratic function, control the replenishment quantity of near - expiration supplies, and control costs.

[0084] Analysis of the calculation advantages of the time - effect constraint factor S(T)

[0085] Table II. Comparison table between this solution and the traditional threshold method

[0086] Dimension This solution S(T) Traditional threshold method Control accuracy Nonlinear continuous decay Hard truncation (such as no replenishment prohibited if the expiration date < 30 days) Flexibility Parameters can be dynamically adjusted (g, Tmax) Fixed rules Synergy Intelligent interaction with emergency response factors Isolated judgment Expired loss Significantly reduce expired loss Benchmark value

[0087] As shown in Table II, compared with the traditional threshold method, the time - effect constraint factor of this application solution The core module for ensuring the expiration safety of materials in the material replenishment algorithm has significant advantages in the following aspects:

[0088] 1. Strict suppression of materials approaching expiration

[0089] Advantages:

[0090] Quadratic accelerated decay: Through the square term When the material approaches the expiration date (Tc → Tmax), the replenishment quantity will decrease non-linearly and suddenly.

[0091] Table III. Example comparison (assuming g = 0.1):

[0092]

[0093] Referring to the example comparison shown in Table III, it has:

[0094] Key difference: The suppression intensity of the quadratic model for materials approaching expiration (>70%) is significantly enhanced, reducing the expiration risk.

[0095] Business value: Compared with the linear model, quadratic decay can reduce expiration losses.

[0096] 2. Intelligent protection of the expiration safety zone

[0097] Advantages:

[0098] Three-stage dynamic control:

[0099] Safety zone (Tc < 0.3Tmax): S(T) ≈ 1S (no decay, normal replenishment)

[0100] Warning zone (0.3Tmax ≤ Tc < 0.7Tmax): The replenishment quantity gradually decays (e.g., decays by 4.9% at 70% expiration)

[0101] High-risk zone (Tc ≥ 0.7Tmax): The replenishment quantity drops rapidly (e.g., decays by 8.1% at 90% expiration)

[0102] 3. Parameters are flexibly configurable

[0103] Advantages:

[0104] Adjustability of the decay coefficient g:

[0105] Conventional consumables: g = 0.05 (loose control)

[0106] High-value consumables: g = 0.2 (strict control)

[0107] Category adaptation of the expiration threshold Tmax: Different standard expiration periods can be set for drugs, medical devices, and dressings.

[0108] Application scenario: A certain hospital sets up for heart stents (high unit price, sensitive expiration date):

[0109] When Tc = 360 days:

[0110]

[0111] Summary

[0112] The aging constraint factor S(T) is obtained through:

[0113] The secondary decay model - precisely suppressing the replenishment of near-expiration materials;

[0114] The three-stage zoning - intelligently distinguishing safe / warning / high-risk inventory;

[0115] Dynamic parameter configuration - adapting to the characteristics of different categories of consumables.

[0116] Step S3. According to the delivery timeliness of each medical material distribution warehouse, obtain the geographical optimization factor; specifically, the calculation of the geographical optimization factor is as follows:

[0117]

[0118] Among them, L(G) is the geographical optimization factor; D k is the distance to the supply node, representing the physical distance from the target warehouse to the kth supply node; V k is the real-time transportation speed, representing the average speed of the transportation route to the kth supply node at present (combined with real-time road condition data). Based on the real-time transportation speed V k and the distance D k Calculate the optimal supply node, use the integrated Dijkstra algorithm to optimize the multi-node path, calculate the delivery timeliness of multiple warehouses, and select the warehouse with the fastest timeliness.

[0119] For example: The replenishment decision of a certain tertiary hospital in rainy weather

[0120] Input data:

[0121] Node A: D1 = 40, V1 = 20 km / h (the vehicle speed drops due to heavy rain)

[0122] Node B: D2 = 80, V2 = 60 km / h (the highway is normal)

[0123] Calculation result:

[0124]

[0125] Conclusion:

[0126] The system automatically gives priority to selecting Node B (although the distance is farther, but the speed advantage is obvious)

[0127] The replenishment quantity is increased to approximately 7.1 times the baseline value (1 / 0.14) to compensate for the potential risks caused by transportation delays.

[0128] Analysis of the computational advantages of the geographical optimization factor L(G)

[0129] Table IV. Comparison table between the proposed solution and the traditional distance weighting method

[0130] Evaluation dimension Geographical optimization factor L(G) Traditional distance weight method Real-time performance Dynamically respond to road condition changes (updated at the minute level) Fixed quarterly update Computational complexity O(n) (n is the number of nodes) <![CDATA[O(n 2 )(cross-weight needs to be calculated)]]> Anti-interference ability Product form suppresses the influence of outliers Single-point failure causes overall failure Business adaptability Automatically match the aging requirements of medical supplies Manual adjustment of distance threshold required

[0131] As shown in Table IV, compared with the traditional distance weighting method, the proposed solution of this application has the following advantages:

[0132] 1. Dynamic response to real-time traffic conditions

[0133] Advantages:

[0134] Speed-sensitive compensation: When the transportation speed V k decreases (due to factors such as traffic congestion, weather conditions, traffic control, and the epidemic), the factor automatically decreases, amplifying the replenishment quantity in the reverse direction to compensate for the risk of transportation delays.

[0135] Example: If the V of a certain node k decreases from 60 km / h to 30 km / h, L(G) decreases from 0.85 to 0.67, and the replenishment quantity increases by approximately 27%.

[0136] Multi-node collaboration: Supports the simultaneous calculation of geographical constraints for multiple supply nodes and automatically selects the optimal path (the minimum D k / V k ).

[0137] Comparison with the traditional method: The static distance model cannot reflect real-time traffic condition changes and may lead to replenishment delays.

[0138] 2. Nonlinear attenuation is more in line with reality

[0139] Advantages:

[0140] High sensitivity of the distance-speed ratio: Using the reciprocal form makes the factor value decay rapidly when D k / V k increases (i.e., the transportation efficiency decreases).

[0141] Example:

[0142]

[0143] When the transportation efficiency decreases by 50%, the attenuation amplitude of the replenishment quantity reaches 34% (nonlinear response).

[0144] Comparison with the traditional linear model: If the traditional linear form (1 - αDk ), which cannot accurately reflect the increasing marginal cost effect of long-distance transportation.

[0145] 3. Multi-node scalability and flexibility

[0146] Advantages:

[0147] Product form is compatible with multiple nodes: By calculating the combined impact of multiple nodes, it naturally supports distributed warehousing networks.

[0148] Example: When there are 3 supply nodes:

[0149]

[0150] Automatic weight allocation: Nodes with low efficiency (D k / V k large) have a greater impact on the overall factor, and there is no need to manually set weights.

[0151] Comparison with the fixed weight method: Traditional weighted average requires presetting node priorities and it is difficult to dynamically adapt to network changes.

[0152] The geographical optimization factor L(G) realizes the following core values through dynamic road condition integration, non-linear attenuation design, and multi-node product form:

[0153] Precisely compensate for transportation risks: Real-time speed changes are directly reflected in the calculation of replenishment quantities;

[0154] Reduce expiration losses: Strictly limit the replenishment quantities for long-duration transportation;

[0155] Enhance system robustness: Naturally adapt to the addition or deletion of nodes or sudden interruptions;

[0156] Reduce manual intervention: There is no need to preset complex weight rules.

[0157] Step S4. Calculate the safe replenishment quantity for this time according to the above actual consumption rate, emergency response factor, time limit constraint factor, and geographical optimization factor. Specifically, the safe replenishment quantity for this time is calculated as follows:

[0158] Q(t) = D(t) × E(t) × S(T) × L(G)

[0159] Among them, Q(t) is the safe replenishment quantity for this time; D(t) is the actual consumption rate of medical supplies; E(t) is the emergency response factor; S(T) is the time limit constraint factor; L(G) is the geographical optimization factor.

[0160] Table Five. Three-level emergency response matrix

[0161] Risk level Time decay constant Emergency gain coefficient Decay coefficient Enable condition Level 1 response ≤6h 0.5 0.2 Major public health events Level 2 response ≤12h 0.35 0.1 Regional epidemic / emergency surge Level 3 response ≤24h 0.2 0.05 Regular operation

[0162] As shown in Table 5, the risk level is divided into three levels. Among them, for the first-level response, the time decay constant d ≤ 6h, the emergency gain coefficient k = 0.5, the decay coefficient g = 0.2, and the enabling condition is a major public health event; for the second-level response, the time decay constant d ≤ 12h, the emergency gain coefficient k = 0.35, the decay coefficient g = 0.1, and the enabling condition is a regional epidemic / emergency surge; for the third-level response, the time decay constant d ≤ 24h, the emergency gain coefficient k = 0.2, the decay coefficient g = 0.05, and the enabling condition is normal operation.

[0163] See Figure 2 As shown, the present invention also provides an emergency medical supplies dynamic replenishment system for implementing the above-mentioned emergency medical supplies dynamic replenishment method, including: an actual consumption rate acquisition module for acquiring the actual consumption rate of medical supplies in a medical institution at the current moment; an emergency response factor calculation module for obtaining an emergency response factor according to the risk level of the current emergency situation; a time limit constraint factor calculation module for obtaining a time limit constraint factor according to the inventory situation of medical supplies in the medical institution at the current moment; a geographical optimization factor calculation module for obtaining a geographical optimization factor according to the delivery timeliness of each medical supplies distribution warehouse; a replenishment quantity calculation module for calculating the safe replenishment quantity this time according to the above actual consumption rate, emergency response factor, time limit constraint factor, and geographical optimization factor. Specifically, the emergency medical supplies dynamic replenishment system further includes a replenishment quantity estimation module for calculating the possible replenishment quantity of this emergency event according to the actual consumption rate of medical supplies in the medical institution at the current moment and the elapsed time. The emergency medical supplies dynamic replenishment system also includes a risk level setting module for setting the risk level.

[0164] The present invention will be further described below through specific embodiments.

[0165] A certain hospital needs to replenish N95 masks during the flu season (regional epidemic state), with a second-level response

[0166] · D(t) = 30 units / h

[0167] · T c = 80 days, T max = 180 days, g = 0.1

[0168] · Supply nodes:

[0169] Node 1: D1 = 40km, V1 = 50km / h

[0170] Node 2: D2 = 100km, V2 = 80km / h

[0171] · Emergency parameters: t = 10h, k = 0.35, d = 12h

[0172] · Emergency response factor:

[0173] E(t) = 1 + k·(1 - e -t / d )

[0174] E(t) = 1 + 0.35·(1 - e -10 / 12 ) ≈ 1.198

[0175] · Aging constraint factor:

[0176]

[0177] S(T) = 1 - 0.1·(80 / 180) 2 ≈ 0.96

[0178] · Geographic optimization factor:

[0179]

[0180] · Final replenishment quantity:

[0181] Q(t) = D(t) × E(t) × S(T) × L(G)

[0182] Q(t) = 30 × 1.198 × 0.98 × 0.25 ≈ 8.67 units

[0183] Rounding according to the product specifications, that is, the safe replenishment quantity this time is 10 units, which can meet the needs of the hospital.

[0184] In the description of the specification, the description referring to terms such as "one embodiment", "preferably", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. The schematic expressions of the above terms in this specification do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0185] The present invention is not limited to the above embodiments. For those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements are also regarded as within the protection scope of the present invention. The content not described in detail in this specification belongs to the prior art well-known to those of ordinary skill in the art.

Claims

1. A dynamic replenishment method for emergency medical supplies, characterized in that Including: Obtain the actual consumption rate of medical supplies at the current moment in the medical institution; Obtain the emergency response factor according to the risk level of the current emergency situation; Obtain the time limit constraint factor according to the inventory situation of medical supplies at the current moment in the medical institution; Obtain the geographical optimization factor according to the delivery timeliness of each medical supply distribution warehouse; Calculate the safe replenishment quantity for this time based on the above actual consumption rate, emergency response factor, time limit constraint factor and geographical optimization factor.

2. The dynamic replenishment method of emergency medical supplies according to claim 1, characterized in that The calculation of the emergency response factor is as follows: E(t) = 1 + k·(1 - e -t / d ) Wherein, E(t) is the emergency response factor; k is the emergency gain coefficient, and the value range is 0.2 - 0.5, which controls the replenishment increment amplitude in the emergency state; d is the time decay constant, and the value range is ≤6 / ≤12 / ≤24 hours, indicating the duration of the emergency gain effect; t is the duration of the ongoing emergency.

3. The dynamic replenishment method of emergency medical supplies according to claim 1, characterized in that The calculation of the time limit constraint factor is as follows: Among them, S(T) is the aging constraint factor; T c is the current average expiration date, representing the average remaining expiration date of similar materials in the inventory; T max is the standard expiration date threshold, representing the standard expiration date of the materials; g is the attenuation coefficient, controlling the inhibition intensity of the expiration date on the replenishment quantity.

4. The dynamic replenishment method of emergency medical supplies according to claim 1, wherein The calculation of the geographical optimization factor is as follows: Among them, L(G) is the geographical optimization factor; D k is the distance of the supply node, representing the physical distance from the target warehouse to the k-th supply node; V k is the real-time transportation speed, representing the average speed of the transportation route to the k-th supply node currently.

5. The dynamic replenishment method for emergency medical supplies according to claim 1, wherein The calculation of the safe replenishment quantity for this time is as follows: Q(t) = D(t) × E(t) × S(T) × L(G) Wherein, Q(t) is the safe replenishment quantity for this time; D(t) is the actual consumption rate of medical supplies; E(t) is the emergency response factor; S(T) is the time limit constraint factor; L(G) is the geographical optimization factor.

6. The dynamic replenishment method of emergency medical supplies according to claim 1, wherein: The risk level is divided into three levels, among which For the first-level response, the time decay constant d ≤ 6h, the emergency gain coefficient k = 0.5, and the decay coefficient g = 0.

2. The enabling condition is a major public health event; For the second-level response, the time decay constant d ≤ 12h, the emergency gain coefficient k = 0.35, and the decay coefficient g = 0.

1. The enabling condition is a regional epidemic / emergency surge; For the third-level response, the time decay constant d ≤ 24h, the emergency gain coefficient k = 0.2, and the decay coefficient g = 0.

05. The enabling condition is normal operation.

7. The dynamic replenishment method of emergency medical supplies according to claim 1, wherein: Based on the actual consumption rate of medical supplies at the current moment in the medical institution and the elapsed time, calculate the possible replenishment quantity for this emergency event.

8. An emergency medical supplies dynamic replenishment system for implementing the emergency medical supplies dynamic replenishment method according to any one of claims 1-7, characterized in that, Including: An actual consumption rate acquisition module, which is used to obtain the actual consumption rate of medical supplies at the current moment in the medical institution; An emergency response factor calculation module, which is used to obtain the emergency response factor according to the risk level of the current emergency situation; A time limit constraint factor calculation module, which is used to obtain the time limit constraint factor according to the inventory situation of medical supplies at the current moment in the medical institution; A geographical optimization factor calculation module, which is used to obtain the geographical optimization factor according to the delivery timeliness of each medical supply distribution warehouse; A replenishment quantity calculation module, which is used to calculate the safe replenishment quantity for this time based on the above actual consumption rate, emergency response factor, time limit constraint factor and geographical optimization factor.

9. The emergency medical supplies dynamic replenishment system according to claim 8, characterized in that: It further includes a replenishment quantity estimation module, which is used to calculate the possible replenishment quantity for this emergency event based on the actual consumption rate of medical supplies at the current moment in the medical institution and the elapsed time.

10. The emergency medical supplies dynamic replenishment system according to claim 8, wherein: It further includes a risk level setting module, which is used to set the risk level.

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