A method, device and medium for determining the risk of medical drone material transportation

Abstract

The present invention provides a method, device and medium for determining the transportation route of medical drone materials, relating to the technical field of medical drone material transportation route determination. The method comprises: obtaining the transportation risk and transportation cost corresponding to each route to obtain a transportation risk list FA and a transportation cost list SA, and the population exposure risk corresponding to each route to obtain a population exposure risk list RA; then, based on FA, SA and RA, determining the priority corresponding to each route to obtain a priority list δ, and determining the route with the highest priority as the target route, thereby achieving the purpose of determining a more preferred route from multiple routes.

Description

Technical Field

[0001] The present invention relates to the technical field of medical drone material transportation risk determination, and in particular to a method, device, and medium for determining medical drone material transportation risk. Background Art

[0002] With the start of the first year of low-altitude economic development, the transportation of medical supplies has become the main demonstration scenario for urban drone applications. Usually, medical supplies include specific medical products with hazardous characteristics, such as targeted drugs, virus samples, blood and organs. If a leakage accident occurs during transportation, it is very easy to cause environmental pollution, property loss and casualties, seriously endangering the safe and healthy development of the urban low-altitude environment. Therefore, how to reasonably analyze and scientifically measure the risks of medical drone material transportation in low-altitude environments has become a technical problem that needs to be solved urgently. Summary of the Invention

[0003] In view of the above technical problems, the technical solution adopted by the present invention is:

[0004] According to a first aspect of the present application, a method for determining the risk of transporting medical supplies by drone is provided, the method comprising the following steps:

[0005] S100: Obtain the number N of medical drones on a target medical drone route; wherein the target medical drone route is any medical drone route.

[0006] S200, based on N and the preset probability P of a medical drone leaking accident n , determine the probability of leakage accident corresponding to the target medical drone route P = P n ×N.

[0007] S300: Determine the concentration of harmful substances at a designated point in the potential contaminated area downwind from the leakage source. Where x is the longitudinal distance between the specified point and the leakage source, y is the lateral distance between the specified point and the leakage source, and z is the vertical distance between the specified point and the leakage source; v is the leakage velocity of the leakage source; u is the average wind speed; H is the effective height of the leakage source; σ y is the horizontal diffusion coefficient, σ z is the vertical diffusion coefficient; a, b, c, and d are the stability parameters of the atmospheric environment; the leakage source is any medical drone that has a leakage accident.

[0008] S400, based on C(x, y, z) and the limit boundary value C of the harmful substance corresponding to the leakage source lv , determine the harmful impact radius of the leakage source in, Q capis the maximum carrying capacity of the medical drone, and t is the leakage time.

[0009] S500, based on R and P, determines the material transportation risk corresponding to the target medical drone route

[0010] According to another aspect of the present application, a non-transitory computer-readable storage medium is also provided, in which at least one instruction or at least one program is stored. The at least one instruction or at least one program is loaded and executed by a processor to implement the above-mentioned method for determining the risk of medical drone material transportation.

[0011] According to another aspect of the present application, an electronic device is provided, including a processor and the above-mentioned non-transitory computer-readable storage medium.

[0012] The present invention has at least the following beneficial effects:

[0013] The method for determining the risk of medical drone material transportation of the present invention obtains the number N of medical drones on the target medical drone route and the probability P of a preset medical drone leakage accident. n , thus the probability P of a leakage accident corresponding to the target medical drone route can be obtained; then the concentration C(x, y, z) of the hazardous substance components at a specified point in the potential contaminated area in the downwind direction corresponding to the leakage source can be determined. According to C(x, y, z) and the limit boundary value C of the hazardous substance corresponding to the leakage source, lv , determine the harmful impact radius R of the leakage source, and based on R and P, determine the material transportation risk corresponding to the target medical drone route; thereby rationally analyzing and scientifically measuring the material transportation risk of medical drones in low-altitude environments.

[0014] Furthermore, when determining the material transportation risk corresponding to the target medical drone route, the present invention combines the number of medical drones on the target medical drone route, the probability of leakage accidents, and the stability parameters of the harmful substances and atmospheric environment corresponding to the leakage source, so that the determined material transportation risk corresponding to the target medical drone route is more accurate and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1A flowchart of a method for determining the risk of medical drone material transportation provided by an embodiment of the present invention;

[0017] Figure 2 A schematic diagram of the medical drone route provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be noted that, based on this disclosure, those skilled in the art will appreciate that an aspect described herein can be implemented independently of any other aspect, and that two or more of these aspects can be combined in various ways. For example, any number of the aspects described herein can be used to implement an apparatus and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein can be used to implement such an apparatus and / or practice such a method.

[0020] Example 1:

[0021] In this embodiment, medical drones typically transport supplies such as virus samples, blood transfusion bags, transplantable human organs, targeted drugs, and AED voltage regulators, suitable for urban medical needs. These specific types of medical supplies often contain infectious, toxic, and radioactive substances. If a sudden leak occurs during transportation, the hazardous substances contained in these specific medical supplies will diffuse three-dimensionally in the low-altitude environment, causing harmful effects on people, property, and the environment within the urban low-altitude airspace. Therefore, based on the modeling concept of traditional risk models, this embodiment defines the risk of medical drone material transportation in the urban low-altitude environment as follows: At a certain urban low-altitude operating altitude, if a medical drone suddenly leaks materials, the harmful effects caused by the three-dimensional diffusion of hazardous substances within the materials in the urban low-altitude environment.

[0022] Obviously, the scope of the harmful impact can be described as a three-dimensional risk field, and the diffusion radius and diffusion concentration of harmful substances in the risk field determine the breadth and intensity of the harmful impact.

[0023] In real-world low-altitude airspace, drones transporting medical supplies within a given area vary in type, carrying varying degrees of hazard, and affecting varying radii. Specifically, this paper considers the specific supplies transported by medical drones as biological samples containing hazardous viruses. If a drone were to leak within an urban low-altitude airspace, the hazardous viruses carried by the biological samples would mix with the surrounding air, forming aerosols and spreading in three dimensions, centered around the leak source. This aerosol would then cause harmful effects within a defined three-dimensional spatial range. This range of harmful effects can be described in three-dimensional space as a risk field approximately spherical.

[0024] Furthermore, the medical drone transport scenario described in this paper operates in a low-altitude urban environment. Typically, drones carrying medical supplies operate in formations along designated routes within low-altitude airspace. Therefore, the risk of particularly hazardous medical supplies lurks at any point along the drone's route, and the risk is closely related to the drone's payload capacity. Furthermore, the risk of viral sample leakage is transmitted through the atmosphere, and its spread is affected by wind direction and speed in low-altitude airspace. Given this, this embodiment employs a traditional risk metric: probability of accident × consequences of the accident. Based on the characteristics of drone low-altitude operations and the hazard analysis of the medical supplies they carry, this paper assumes that any leakage source is a single primitive route, treating the viral samples carried by the medical drone as a potential risk source. The risk of medical drone transport in low-altitude urban environments can be defined as: the range of harmful effects of viral spread due to a viral sample leakage accident during medical drone transport in low-altitude urban environments, influenced by atmospheric wind speed and direction.

[0025] The following will refer to Figure 1 The flowchart of the method for determining the risk of medical drone material transportation is shown, which introduces a method for determining the risk of medical drone material transportation.

[0026] The method for determining the risk of medical drone material transportation may include the following steps:

[0027] S100: Obtain the number N of medical drones on a target medical drone route; wherein the target medical drone route is any medical drone route.

[0028] In this embodiment, on the corresponding route designated in the low-altitude airspace, drones carrying medical supplies will operate in a certain formation; therefore, the number N of medical drones on the target medical drone route can be obtained.

[0029] S200, based on N and the preset probability P of a medical drone leaking accident n , determine the probability of leakage accident corresponding to the target medical drone route P = P n×N.

[0030] Furthermore, P n This can be determined by the following steps:

[0031] S210, obtain the number NUM1 of leakage accidents that occurred during the historical medical drone transportation of materials.

[0032] S220, based on NUM1 and the total number of medical drone material transportation times NUM2, determine P n =NUM1 / NUM2.

[0033] In this embodiment, during several medical drone transportation processes in the past, there may be medical drones that have leakage accidents. The number of leakage accidents NUM1 and the total number of historical medical drone material transportation times NUM2 can be obtained, and then P is obtained. n .

[0034] S300: Determine the concentration of harmful substances at a designated point in the potential contaminated area downwind from the leakage source. Where x is the longitudinal distance between the specified point and the leakage source, y is the lateral distance between the specified point and the leakage source, and z is the vertical distance between the specified point and the leakage source; v is the leakage velocity of the leakage source; u is the average wind speed; H is the effective height of the leakage source; σ y is the horizontal diffusion coefficient, σ z is the vertical diffusion coefficient; a, b, c, and d are the stability parameters of the atmospheric environment; the leakage source is any medical drone that has a leakage accident.

[0035] In this embodiment, the height of each medical drone on the route of the target medical drone can be obtained, and the average value of the heights of all medical drones can be determined as H.

[0036] It should be noted that in this embodiment, the accident consequences of drone transportation risks in urban low-altitude environments are calculated as the harmful impact range V of the virus sample leakage; wherein, the harmful impact range of the virus sample leakage draws on the hemispherical box model commonly used in simulating and measuring the atmospheric pollution of hazardous substances when transporting dangerous goods by road. At the same time, considering that the leakage accident occurs in the air, the virus will spread evenly in all directions. The impact radius of the virus sample leakage is set as the maximum diffusion distance of the leakage source in the area. Therefore, the risk impact range caused by the leakage source in the low-altitude area can be vividly described as a complete spherical volume. Specifically expressed as:

[0037]

[0038] Among them, R is the impact radius of the leakage of the virus sample; different from the impact radius considered by the conventional box model on the ground, since the scene of medical drone material transportation is carried out in the low-altitude environment of the city, the spread of the virus depends on the atmosphere as a transmission medium, and the low-altitude environment is relatively complex and is often affected by atmospheric wind flow. Therefore, in order to determine the impact radius of the leakage of virus samples that is more in line with the actual low-altitude environment, in this embodiment, the Gaussian plume model that simulates the diffusion of harmful components in the atmosphere is used for risk measurement. The Gaussian plume model sets the diffusion process of harmful components to obey the Gaussian distribution, and calculates the concentration of harmful components at any location in the potential contaminated area based on factors such as wind speed, wind direction, the nature of the leakage source, and the leakage speed. The standard Gaussian plume model is as follows:

[0039]

[0040] Among them, σy and σz represent the horizontal diffusion coefficient and the vertical diffusion coefficient, respectively. It should be noted that due to the unstable airflow in the low-altitude airspace environment, the horizontal and vertical diffusion coefficients are affected by the combined influence of the stability parameters of the atmospheric environment and the downwind distance, which are specifically expressed as follows:

[0041] σ y =ax c ;

[0042] σ z =bx d ;

[0043] Among them, a is the stability parameter of the atmospheric environment related to the vertical temperature gradient; b is the stability parameter of the atmospheric environment related to the wind speed; c is the stability parameter of the atmospheric environment related to the humidity; d is the stability parameter of the atmospheric environment related to the terrain; it should be noted that those skilled in the art can use the existing method for determining the stability parameters of the atmospheric environment to determine a, b, c and d according to actual needs, which will not be elaborated here.

[0044] Since the medical drone transportation environment is a low-altitude urban environment with a certain altitude, if a leakage accident occurs during the transportation of materials, the leakage source has a certain instantaneous altitude. Therefore, this paper uses the Gaussian plume model to derive the diffusion concentration of harmful substances in the downwind environment. Specifically, the standardized Gaussian plume model can be converted into the following form:

[0045]

[0046] S400, based on C(x, y, z) and the limit boundary value C of the harmful substance corresponding to the leakage source lv , determine the harmful impact radius of the leakage source in, Q capis the maximum carrying capacity of the medical drone, and t is the leakage time.

[0047] In this embodiment, it should be noted that this embodiment mainly considers the diffusion of harmful substances in the downwind direction, that is, y=0, and the diffusion direction of harmful substances is in all directions perpendicular to the z-axis. Obviously, when the concentration of a certain harmful substance is the limit boundary value C lv When C(x, y, z) is used, the harmful impact radius R of the leakage source can be derived as follows:

[0048]

[0049] Generally, the leakage rate of a leakage source is related to the leakage volume and leakage time. In this embodiment, the maximum carrying capacity of the medical drone is regarded as the potential maximum leakage volume, and the calculation formula of the leakage rate is set as follows:

[0050]

[0051] Among them, Q cap is the maximum carrying capacity of the medical drone, and t is the leakage time.

[0052] Furthermore, t can be determined by the following steps:

[0053] S410, obtaining the leakage time of each target medical drone during the historical time period to obtain a historical leakage time list T = (T1, T2, ..., T i ,…,T n ), i = 1, 2, ..., n; where T i is the leakage time of the i-th target medical drone in the historical time period, n is the number of target medical drones in the historical time period; the target medical drone has a carrying capacity of Q cap And a medical drone that leaked.

[0054] S420, based on T, determine the leakage time fluctuation rate β corresponding to T = ∑ n i＝1 (T i -(1 / n)×∑ n i＝1 T i ) 2 / n.

[0055] S430, if β<β', then determine t=(1 / n)×∑ n i＝1 T i .

[0056] In this embodiment, if β<β', it means that the difference of the leakage time in T is small, which can represent a relatively normal leakage time. Therefore, the mean value of the leakage time in T can be determined as t.

[0057] Furthermore, after step S430, the method may further include the following steps:

[0058] S440: If β≥β', cluster the leakage time in T into several clusters using a preset clustering algorithm to obtain a cluster list B = (B1, B2, ..., B j ,…,B m ), j = 1, 2, ..., m; where B j is the jth cluster obtained by clustering the leakage time in T, and m is the number of clusters obtained by clustering the leakage time in T.

[0059] S450, obtaining the number of leakage times in each cluster in B to obtain a number list C = (C1, C2, ..., C j ,…,C m ); among them, C j B j The number of internal leak events.

[0060] S460, obtaining the two largest quantities QC1 and QC2 in C; wherein QC1>QC2.

[0061] In this embodiment, under normal circumstances, the number of abnormal leakage times is small, and most of the leakage times are concentrated in one time range. Therefore, among the clusters obtained by clustering, theoretically only one cluster has a large number of leakage times, and the difference between the number of leakage times and the cluster with the second largest number of leakage times is large.

[0062] S470, if QC1-QC2>NU, then the average time of the leakage time in the cluster corresponding to QC1 is determined as t; where NU is the number difference threshold of the leakage time; NU=γ×m; γ is a preset number weight, 0.7<γ<1.

[0063] In this embodiment, if β ≥ β', it means that the leakage time in T varies greatly, and there are relatively abnormal leakage times. Abnormal leakage times cannot accurately represent the leakage time when most medical drones have leakage accidents. Therefore, it is necessary to filter out the larger and smaller leakage times in T and determine t based on the filtered leakage times; thereby making the determined t more accurate and reasonable, and further improving the accuracy of transportation risk determination.

[0064] In this embodiment, if a medical drone leaks during transportation, the hazardous substances contained in the special medical supplies will diffuse in all directions, ultimately forming a three-dimensional risk field. Generally, the diffusion radius of hazardous substances is affected by wind speed. Therefore, in the cross-section of the risk field, the wind direction and wind speed are relatively stable over a certain period of time. The distance that the hazardous substances diffuse along the downwind direction is calculated as the diffusion radius of the three-dimensional risk field. Therefore, the volume of the medical drone transport risk field can be calculated as:

[0065]

[0066] S500, based on R and P, determines the material transportation risk corresponding to the target medical drone route

[0067] Furthermore, after step S500, the method may further include the following steps:

[0068] S600: If Risk>Risk', a preset transportation risk prompt is generated; wherein Risk' is a preset transportation risk threshold.

[0069] In this embodiment, a risk threshold can be obtained based on historical transportation experience. If Risk>Risk', a preset transportation risk prompt is generated to remind the user that the transportation risk of the target medical drone transportation route is relatively high, and other medical drone transportation routes with lower risks can be selected.

[0070] The method for determining the risk of medical drone material transportation implemented in this paper obtains the number N of medical drones on the target medical drone route and the probability P of a preset medical drone leakage accident. n , thus the probability P of a leakage accident corresponding to the target medical drone route can be obtained; then the concentration C(x, y, z) of the hazardous substance components at a specified point in the potential contaminated area in the downwind direction corresponding to the leakage source can be determined. According to C(x, y, z) and the limit boundary value C of the hazardous substance corresponding to the leakage source, lv , determine the harmful impact radius R of the leakage source, and based on R and P, determine the material transportation risk corresponding to the target medical drone route; thereby rationally analyzing and scientifically measuring the material transportation risk of medical drones in low-altitude environments.

[0071] Furthermore, when determining the material transportation risk corresponding to the target medical drone route, the present invention combines the number of medical drones on the target medical drone route, the probability of leakage accidents, and the stability parameters of the harmful substances and atmospheric environment corresponding to the leakage source, so that the determined material transportation risk corresponding to the target medical drone route is more accurate and reasonable.

[0072] Example 2:

[0073] In the case where there are multiple routes, the optimal route can be selected based on the method for determining the risk of transporting medical supplies by drones in Example 1, which specifically includes the following steps:

[0074] Q100, obtain each route between the preset supply point and demand point to obtain a route list HA = (HA1, HA2, ..., HA α ,…,HA β ), α=1, 2,...,β; among them, HA α is the αth route between the supply point and the demand point, and β is the number of routes between the supply point and the demand point.

[0075] In this embodiment, there are multiple routes between the supply point and the demand point, and medical supplies can be transported from the supply point to the demand point through each route; for example, Figure 2 As shown, the supply point is O, the demand point is D, and 1-6 are intermediate nodes; there are three routes from the supply point to the demand point.

[0076] Q200, obtain the transportation risk and transportation cost corresponding to each route to obtain the transportation risk list FA=(FA1, FA2, ..., FA α ,…,FA β ) and the transport cost list SA=(SA1,SA2,…,SA α ,…,SA β );Among them, FA α HA α Corresponding transportation risks; SA α HA α The corresponding transportation cost.

[0077] Furthermore, HA α =(HA α，1 , HA α，2 ,…,HA α，γ ,…,HA α，f(γ) ), where HA α，γ is the γth segment of the αth route, and f(γ) is the number of segments corresponding to the αth route.

[0078] In this embodiment, each route corresponds to multiple flight segments, such as Figure 2 As shown, for example: O-1 is a flight segment, 1-2 is a flight segment; each flight segment corresponding to each route can be obtained.

[0079] FA α Obtained through the following steps:

[0080] Q210, Get HA αThe transportation risk corresponding to each flight segment in the HA α Corresponding flight segment transportation risk list FB α =(FB α，1 , FB α，2 ,…,FB α，γ ,…,FB α，f(γ) ); Among them, FB α，γ HA α，γ Corresponding transportation risks.

[0081] Q220, according to FB α , determine FA α =∑ f(γ) γ＝1 Facebook α，γ .

[0082] In this embodiment, it is necessary to calculate the transportation risk corresponding to each flight segment, and then add up the transportation risk of each flight segment corresponding to each route to obtain the transportation risk of the corresponding route;

[0083] Furthermore, FA α According to HA α The number of medical drones corresponding to the above, the probability of leakage accidents occurring in medical drones, the properties of medical supplies carried by medical drones, and the corresponding environmental stability parameters are used to determine the transportation risk corresponding to each flight segment; or the flight segment risk determination method in the existing technology is used to determine the transportation risk corresponding to each flight segment.

[0084] Furthermore, the transportation risk of each flight segment can also be determined by the transportation risk determination method of embodiment 1, which will not be described in detail here.

[0085] In addition, a and c are empirical values, usually 0.02 and 0.89 respectively; b and d can be determined according to the altitude layer, and the flight altitude of the medical drone can be obtained in real time; the average wind speed of the low-altitude airspace environment at the corresponding altitude layer can be obtained by calculating the average value through wind speed monitoring.

[0086] Furthermore, SA α Obtained through the following steps:

[0087] Q230, Get HA α The distance of each segment in the flight is used to obtain HA α Corresponding flight distance list JL α =(JL α，1 , JL α，2 ,…,JL α，γ ,…,JL α，f(γ) ); among them, JL α，γ HA α，γ The corresponding flight distance.

[0088] Q230, according to the preset unit transport cost DZ and JL α , determine SA α =DZ×∑ f(γ) γ＝1 JL α，γ .

[0089] In this embodiment, the transportation cost can be understood as the transportation cost. The unit transportation cost of the medical drone is known. For example, the unit cost of transporting medical supplies by a medical drone is 20 yuan / km. At the same time, the distance of each flight segment corresponding to each route can be obtained, so that the transportation cost corresponding to each route can be obtained.

[0090] Q300, obtain the population exposure risk corresponding to each route to obtain a population exposure risk list RA = (RA1, RA2, ..., RA α ,…,RA β ); among them, RA α HA α The corresponding population exposure risk.

[0091] In this embodiment, when the medical drone flies on the corresponding route, it will pass through areas where the population is exposed, which also poses certain risks.

[0092] Furthermore, RA α Determine this by following these steps:

[0093] Q310, Get HA α The population density of each node corresponds to the population density list MD α =(MD α，1 , MD α，2 ,…,MD α，λ ,…,MD α，f(γ)+1 ), among which, MD α，λ is the population density corresponding to the λth node of the αth route.

[0094] Q320, according to MD α , determine RA α =(1 / (f(γ)+1))∑ f(γ)+1 λ＝1 MD α，λ .

[0095] In this embodiment, each route corresponds to multiple nodes, and the population density in the preset area corresponding to each node can be obtained, thereby obtaining the RA α , using population density as the population exposure risk, that is, the greater the population density, the higher the corresponding population exposure risk.

[0096] Q400, based on FA, SA and RA, determine the priority corresponding to each route to obtain a priority list δ = (δ1, δ2, ..., δ α ,…,δ β ); where δ α HA α The corresponding priority.

[0097] Furthermore, δ α This can be determined by the following steps:

[0098] Q410, obtain target transportation risk FA'=MAX(FA), target transportation cost SA'=MAX(SA) and target population exposure risk RA'=MAX(RA); where MAX() is a preset maximum value function.

[0099] Q420, according to FA', SA', RA', FA α 、SA α and RA α , determine δ α =1-(1 / 3)×(k1×(FA α / FA')+k2×(SA α / SA')+k3×(RA α / RA')); where K1 is the preset transportation risk weight, k2 is the preset transportation cost weight, and K3 is the preset population exposure risk weight.

[0100] In this embodiment, K1+k2+K3=1; K1, k2 and K3 can be fine-tuned according to actual needs. For example, in a scenario where transportation risk is more important, K1 can be increased. Through the formula corresponding to priority, the priority corresponding to each route can be limited to the range of 0-1. For any route, the greater the priority of the route, the better the comprehensive index of the route, that is, the transportation risk, transportation cost and population exposure risk are more balanced and lower.

[0101] Q500, according to δ, determine the target priority δ'=MAX(δ); wherein MAX() is a preset maximum value function.

[0102] Q600, determine the route corresponding to δ' as the target route.

[0103] In this embodiment, the route with the highest priority corresponds to the best comprehensive index, and the transportation risk, transportation cost, and population exposure risk are relatively balanced and low. Therefore, the route corresponding to δ' is determined as the target route; while ensuring that the transportation risk and population exposure risk are low, the transportation cost is also low.

[0104] Furthermore, using the method in this embodiment, the transportation risk of each node on each route can also be obtained. When determining the transportation risk corresponding to each node, it is necessary to obtain the number of medical drones flying at the corresponding node and the stability parameters of the corresponding atmospheric environment.

[0105] In this example, to further verify the sensitivity of the new model's calculation results to parameter changes, the maximum payload, the median of the flight altitude range, and the median of the wind speed range used in the calculation example were used as basic control parameters. The parameter sensitivity of the analysis model was tested by setting different scenarios. Scenario 1: Reduce the maximum payload of the drone by 50%; Scenario 2: Double the median of the flight altitude range to 340 meters; Scenario 3: Double the median of the wind speed range to 10.6 meters per second.

[0106] The results of parameter sensitivity analysis show that, with other parameters unchanged, in scenario 1, when the maximum load capacity is halved, the total risk is reduced by more than half of that in the basic case; in scenario 2, when the median of the flight altitude range is doubled, the total risk is reduced by more than 1 / 3 of that in the basic case; in scenario 3, when the median of the wind speed range is doubled, the total risk is also reduced by more than 1 / 3 of that in the basic case.

[0107] In this embodiment, the transportation risk and transportation cost corresponding to each route are obtained to obtain a transportation risk list FA and a transportation cost list SA, and the population exposure risk corresponding to each route is obtained to obtain a population exposure risk list RA; then, based on FA, SA and RA, the priority corresponding to each route is determined to obtain a priority list δ, and the route with the highest priority is determined as the target route, thereby achieving the purpose of determining a more preferred route from several routes.

[0108] Furthermore, when determining the priority of the routes, the transport risk, transport cost and population exposure risk of the routes are comprehensively considered, making the determined route priority more reasonable and accurate.

[0109] Furthermore, although the steps of the method of the present disclosure are described in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in this particular order, or that all steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0110] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store at least one instruction or at least one program related to implementing a method in a method embodiment. The at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0111] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0112] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0113] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0114] The program code for performing the operations of the present application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] An embodiment of the present invention further provides an electronic device including a processor and the aforementioned non-transitory computer-readable storage medium.

[0116] The electronic device is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0117] The electronic device is implemented as a general-purpose computing device. Components of the electronic device may include, but are not limited to, the aforementioned at least one processor, the aforementioned at least one memory, and a bus connecting different system components (including the memory and the processor).

[0118] The memory stores program codes, which can be executed by the processor, so that the processor performs the steps of various embodiments described in this specification.

[0119] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0120] The memory may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0121] The bus may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0122] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. The network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0123] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0124] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0125] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A method for determining the risk of medical drone material transportation, characterized in that: The method comprises the following steps: S100, obtaining the number N of medical drones on a target medical drone route; wherein the target medical drone route is any medical drone route; S200, based on N and the preset probability P of a medical drone leaking accident n , determine the probability of leakage accident corresponding to the target medical drone route P = P n ×N; S300: Determine the concentration of harmful substances at a designated point in the potential contaminated area downwind from the leakage source. Where x is the longitudinal distance between the specified point and the leakage source, y is the lateral distance between the specified point and the leakage source, and z is the vertical distance between the specified point and the leakage source; v is the leakage velocity of the leakage source; u is the average wind speed; H is the effective height of the leakage source; σ y is the horizontal diffusion coefficient, σ z is the vertical diffusion coefficient; a, b, c, and d are the stability parameters of the atmospheric environment; the leakage source is any medical drone that has a leakage accident; S400, based on C(x, y, z) and the limit boundary value C of the harmful substance corresponding to the leakage source lv , determine the harmful impact radius of the leakage source in, Qcap is the maximum carrying capacity of the medical drone, and t is the leakage time; S500, based on R and P, determines the material transportation risk corresponding to the target medical drone route 2. The method for determining the risk of medical drone material transportation according to claim 1, characterized in that: P n Determine this by following these steps: S210, obtain the number of leakage accidents NUM1 that occurred during the historical medical drone transportation of materials; S220, based on NUM1 and the total number of medical drone material transportation times NUM2, determine P n =NUM1 / NUM2.

3. The method for determining the risk of medical drone material transportation according to claim 1, characterized in that: t is determined by the following steps: S410, obtaining the leakage time of each target medical drone during the historical time period to obtain a historical leakage time list T = (T1, T2, ..., T i ,…,T n ), i = 1, 2, ..., n; where T i is the leakage time of the i-th target medical drone in the historical time period, n is the number of target medical drones in the historical time period; the target medical drone has a carrying capacity of Q cap and a medical drone that leaked; S420, based on T, determine the leakage time fluctuation rate β corresponding to T = ∑ n i＝1 (T i -(1 / n)×∑ n i＝1 T i ) 2 / n; S430, if β<β', then determine t=(1 / n)×∑ n i＝1 T i .

4. The method for determining the risk of medical drone material transportation according to claim 3, characterized in that: After step S430, the method further includes the following steps: S440: If β≥β', cluster the leakage time in T into several clusters using a preset clustering algorithm to obtain a cluster list B = (B1, B2, ..., B j ,…,B m ), j = 1, 2, ..., m; where B j is the jth cluster obtained by clustering the leakage time in T, and m is the number of clusters obtained by clustering the leakage time in T; S450, obtaining the number of leakage times in each cluster in B to obtain a number list C = (C1, C2, ..., C j ,…,C m ); among them, C j B j the number of internal leakage events; S460, obtaining the two largest quantities QC1 and QC2 in C; wherein QC1>QC2; S470, if QC1-QC2>NU, then the average time of the leakage time in the cluster corresponding to QC1 is determined as t; where NU is the number difference threshold of the leakage time; NU=γ×m; γ is a preset number weight, 0.7<γ<1.

5. The method for determining the risk of medical drone material transportation according to claim 1, characterized in that: s y =ax c ;s z =bx d 。 6. The method for determining the risk of medical drone material transportation according to claim 1, characterized in that: After step S500, the method further includes the following steps: S600: If Risk>Risk', a preset transportation risk prompt is generated; wherein Risk' is a preset transportation risk threshold.

7. The method for determining the risk of medical drone material transportation according to claim 1, characterized in that: a is the stability parameter of the atmospheric environment related to the vertical temperature gradient; b is the stability parameter of the atmospheric environment related to wind speed; c is the stability parameter of the atmospheric environment related to humidity; d is the stability parameter of the atmospheric environment related to terrain.

8. A non-transitory computer-readable storage medium, wherein at least one instruction or at least one program is stored in the storage medium, characterized in that: The at least one instruction or the at least one program is loaded and executed by the processor to implement the method for determining the risk of medical drone material transportation as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: The method comprises a processor and the non-transitory computer-readable storage medium of claim 8.

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