A method and device for determining a medical unmanned aerial vehicle material transportation path and a medium

Abstract

The application provides a medical unmanned aerial vehicle material transportation path determination method, device and medium, relates to the medical unmanned aerial vehicle material transportation path determination technical field, and the method comprises the following steps: acquiring the transportation risk and the transportation cost corresponding to each air route to obtain a transportation risk list FA and a transportation cost list SA, and acquiring the population exposure risk corresponding to each air route to obtain a population exposure risk list RA; then, according to the FA, the SA and the RA, the priority corresponding to each air route is determined to obtain a priority list δ, and the air route with the highest priority is determined as a target air route, so that the purpose of determining the more preferred air route from several air routes is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical unmanned aerial vehicle (UAV) material transportation path determination, and in particular relates to a medical UAV material transportation path determination method, device and medium. BACKGROUND

[0002] With the start of the low-altitude economy development year, medical material transportation has become a main demonstration scenario of urban UAV application. Generally, medical materials include specific medical supplies with hazardous characteristics, such as targeted drugs, virus sampling, blood and organs, etc. Generally, there are several medical UAV flight routes between the supply point and the demand point of medical materials. The environmental factors, transportation risks and transportation costs corresponding to each route are different. Therefore, how to determine a more preferred route under the consideration of all factors becomes a technical problem to be solved. SUMMARY

[0003] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0004] According to a first aspect of the present application, a medical UAV material transportation path determination method is provided, which comprises the following steps:

[0005] Q100, obtaining each route between the preset supply point and demand point to obtain a route list HA=(HA1, HA2,..., HA α ,..., HA β ), a=1, 2,..., β; wherein HA α is the a-th route between the supply point and the demand point, and β is the number of routes between the supply point and the demand point.

[0006] Q200, obtaining the transportation risk and transportation cost corresponding to each route to obtain a transportation risk list FA=(FA1, FA2,..., FA α ,..., FA β ) and a transportation cost list SA=(SA1, SA2,..., SA α ,..., SA β ); wherein FA α is the transportation risk corresponding to HA α ; SA α is the transportation cost corresponding to HA α .

[0007] Q300, obtaining the population exposure risk corresponding to each route to obtain a population exposure risk list RA=(RA1, RA2,..., RA α ,..., RA β ); wherein RA α is the population exposure risk corresponding to HA αThe corresponding population exposure risk.

[0008] Q400, determining the priority of each route according to the FA, SA and RA, to obtain a priority list δ=(δ1, δ2, …, δn); wherein δi is the priority of the i th route. α , …, δ β ); wherein δ α is the priority of the HA. α

[0009] Q500, determining the target priority δ'=MAX(δ) according to the δ; wherein MAX() is a preset maximum value function;

[0010] Q600, determining the route corresponding to the δ' as the target route.

[0011] According to another aspect of the present application, a non-transitory computer readable storage medium is also provided, the storage medium storing at least one instruction or at least one program, the at least one instruction or at least one program being loaded and executed by a processor to implement the above-mentioned method for determining a medical unmanned aerial vehicle material transportation route.

[0012] According to another aspect of the present application, an electronic device is also provided, comprising a processor and the above-mentioned non-transitory computer readable storage medium.

[0013] The present application has at least the following beneficial effects:

[0014] The method for determining a medical unmanned aerial vehicle material transportation route of the present application obtains the transportation risk and transportation cost corresponding to each route to obtain a transportation risk list FA and a transportation cost list SA, and obtains the population exposure risk corresponding to each route to obtain a population exposure risk list RA; then, the priority of each route is determined according to the FA, SA and RA 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.

[0015] Further, when determining the priority of the route, the transportation risk, transportation cost and population exposure risk of the route are comprehensively considered, so that the priority of the determined route is more reasonable and accurate. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 ​A flow chart of a method for determining a medical unmanned aerial vehicle (UAV) material transportation path is provided in the embodiments of the present application.

[0018] Figure 2 A medical UAV route schematic diagram is provided in the embodiments of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0020] It should be noted that, based on the present disclosure, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, an apparatus and / or a method can be implemented using any number of the aspects set forth herein. In addition, this apparatus and / or method can be implemented using other structures and / or functionality in addition to or other than one or more of the aspects set forth herein.

[0021] Embodiment One:

[0022] In this embodiment, generally, the medical UAV transported materials include virus samples, blood bags, transplantable human organs, targeted drugs, AED voltage devices, and the like, which are suitable for urban medical needs. These specific types of medical materials contain infectious, toxic, and radioactive substances. If a leakage accident occurs during transportation, the harmful substances contained in the specific medical materials will be diffused in a three-dimensional manner in the low-altitude environment, causing harmful effects on personnel, property, and the environment in the urban low-altitude airspace. Therefore, based on the modeling concept of the conventional risk model, the medical UAV material transportation risk in the urban low-altitude environment is defined as: under a certain urban low-altitude operation height, the harmful effects caused by the three-dimensional diffusion of harmful substances in the medical UAV in the urban low-altitude environment due to a sudden material leakage accident of the medical UAV.

[0023] Obviously, the range of the harmful effects can be described as a three-dimensional risk field, and the diffusion radius and diffusion concentration of the harmful substances in the risk field determine the extent and intensity of the harmful effects.

[0024] In the real low-altitude airspace, the types of medical supplies transported by unmanned aerial vehicles in a certain area are different, the hazards they carry are different, and the radius of the influence range is also different. Specifically, this paper sets the specific medical supplies transported by unmanned aerial vehicles as biological samples with harmful viruses. When the unmanned aerial vehicle has a leakage accident in the urban low-altitude airspace, the harmful viruses carried by the biological samples will form an aerosol with the surrounding air with the leakage source as the center, conduct three-dimensional diffusion, and cause harmful effects in a certain three-dimensional space. The harmful effect range can be described as a risk field of an approximate sphere in three-dimensional space.

[0025] In addition, the medical unmanned aerial vehicle material transportation in this paper is operated in the urban low-altitude environment. Usually, the unmanned aerial vehicle carrying medical supplies will run in a certain formation on the corresponding air route of the low-altitude airspace. Therefore, the medical supplies with special hazards have a risk potential at any node of the moving unmanned aerial vehicle route, and the risk value is closely related to the corresponding load of the unmanned aerial vehicle. At the same time, the leakage risk of virus samples is transmitted through the atmospheric environment, and the transmission range will be affected by the wind direction and wind speed of the low-altitude airspace. In view of this, this embodiment adopts the traditional risk measurement form, that is, the probability of an accident multiplied by the consequences of an accident. According to the characteristics of unmanned aerial vehicle low-altitude operation and the hazard analysis of the medical supplies carried, this paper assumes that any leakage source point is a single primitive route, and the virus samples carried by the medical unmanned aerial vehicle are regarded as a potential risk source. The risk of medical unmanned aerial vehicle material transportation in urban low-altitude environment can be defined as: in the process of medical unmanned aerial vehicle material transportation in urban low-altitude environment, due to the leakage accident of virus samples, the harmful influence range of virus diffusion under the influence of atmospheric wind speed and direction.

[0026] In this embodiment, the risk of medical unmanned aerial vehicle material transportation can be determined by the following steps:

[0027] S100, obtaining the number N of medical unmanned aerial vehicles on the target medical unmanned aerial vehicle route; wherein the target medical unmanned aerial vehicle route is any medical unmanned aerial vehicle route.

[0028] In this embodiment, the unmanned aerial vehicle carrying medical supplies will run in a certain formation on the corresponding air route of the low-altitude airspace. Therefore, the number N of medical unmanned aerial vehicles on the target medical unmanned aerial vehicle route can be obtained.

[0029] S200, determining the probability P of leakage accident of the target medical unmanned aerial vehicle route according to N and the preset probability P of leakage accident of medical unmanned aerial vehicle n . n

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

[0031] ​​S210, acquire the number NUM1 of the leakage accidents of the historical medical unmanned vehicles.

[0032] S220, determine P according to NUM1 and the total number NUM2 of the medical unmanned vehicles for material transportation. n = NUM1 / NUM2.

[0033] In this embodiment, the number NUM1 of the leakage accidents of the historical medical unmanned vehicles and the total number NUM2 of the medical unmanned vehicles for material transportation can be acquired, and then P n .

[0034] S300, determine the harmful substance component concentration of the specified point in the potential pollution area of the downwind direction corresponding to the leakage source wherein 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 speed 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 respectively; and the leakage source is any medical unmanned vehicle with leakage accident.

[0035] In this embodiment, the height of each medical unmanned vehicle on the target medical unmanned vehicle route can be acquired, and the average of the heights of all the medical unmanned vehicles is determined as H.

[0036] It should be noted that in this embodiment, the accident consequence of the unmanned vehicle transportation risk in the urban low-altitude environment is the harmful influence range V of the virus sample leakage; wherein the harmful influence range of the virus sample leakage is based on the commonly used hemispherical box model for simulating and calculating the atmospheric environmental pollution of harmful substances in road transportation of dangerous goods, and at the same time, considering that the leakage accident occurs in the air, the virus will spread uniformly to all directions, and the leakage influence radius of the virus sample is set as the maximum diffusion distance of the leakage source in this area, so the risk influence range caused by the leakage source in the low-altitude area can be described as a complete spherical volume. Specifically, it is expressed as:

[0037]

[0038] R is the leakage influence radius of the virus sample; unlike the influence radius considered by the conventional box model on the ground, since the medical unmanned aerial vehicle material transportation scenario is carried out in the urban low-altitude environment, the spread of the virus depends on the atmosphere as the transmission medium, and the low-altitude environment is relatively complex and often affected by atmospheric wind flow. Therefore, in order to determine the virus sample leakage influence radius that is more in line with the actual low-altitude environment, in this embodiment, the Gaussian plume model for simulating the diffusion of harmful components in the atmosphere is used for risk measurement, the diffusion process of harmful components in the Gaussian plume model is subject to Gaussian distribution, and the concentration of harmful components at any position in the potential pollution area is calculated according to the differences in wind speed, wind direction, properties of the leakage source and leakage speed. The standard Gaussian plume model is as follows:

[0039]

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

[0041] σ y =ax c ;

[0042] σ z =bx d ;

[0043] Wherein, 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 determine a, b, c and d using the existing method for determining the stability parameter of the atmospheric environment according to actual needs, which will not be described here.

[0044] Since the medical unmanned aerial vehicle transportation environment is a certain height urban low-altitude environment, in the event of a leakage accident during material transportation, the leakage source has a certain instantaneous height, therefore, this paper uses the Gaussian plume model to deduce the harmful substance diffusion concentration in the downwind environment, specifically, the standardized Gaussian plume model can be transformed into the following form:

[0045]

[0046] S400, according to C(x, y, z) and the limit boundary value C lv of the harmful substance corresponding to the leakage source, determine the harmful influence radius R of the leakage source Wherein, Q capt represents the maximum payload of the medical drone, and t represents 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, i.e., 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 represented by the limiting boundary value C... lv At that time, based on C(x, y, z), the harmful influence radius R of the leakage source can be derived as:

[0048]

[0049] Typically, the leakage rate of a leak source is related to the leakage amount and leakage time. In this embodiment, the maximum carrying capacity of the medical drone is considered as the potential maximum leakage amount, and the formula for calculating the leakage rate is set as follows:

[0050]

[0051] Among them, Q cap t represents the maximum payload of the medical drone, and t represents the leakage time.

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

[0053] S410, obtain the leakage time of each target medical drone within 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 Let be the leakage time of the i-th target medical drone within the historical time period, and n be the number of target medical drones within the historical time period; the target medical drone is one with a carrying capacity of Q. cap And the medical drone that was leaked.

[0054] S420, Based on T, determine the leakage time volatility 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 the embodiment, if β < β', it indicates that the difference of the leakage time in T is small, which can represent a normal leakage time, and thus the average of the leakage time in T can be determined as t.

[0057] Further, after step S430, the method can further include the following steps:

[0058] S440, if β ≥ β', the leakage time in T is clustered into several clusters using a preset clustering algorithm to obtain a cluster list B = (B1, B2, …, Bm), j = 1, 2, …, m; wherein Bjis 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. j m j

[0059] S450, the number of leakage time in each cluster in B is obtained to obtain a number list C = (C1, C2, …, Cm); wherein Cjis the number of leakage time in B. j m j j

[0060] S460, the two largest numbers QC1 and QC2 in C are obtained; wherein QC1 > QC2.

[0061] In the embodiment, under normal circumstances, the number of abnormal leakage time is small, and most of the leakage time will be concentrated in a time range, and thus theoretically, only one cluster in the cluster obtained by clustering will have a large number of leakage time, and the difference between the number of leakage time in the cluster and the second largest cluster will be large.

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

[0063] In the embodiment, if β ≥ β', it indicates that the difference of the leakage time in T is large, and there is an abnormal leakage time, which cannot accurately represent the leakage time when most of the medical unmanned vehicles have a leakage accident, and thus the large and small leakage time in T needs to be filtered out, and t is determined by the filtered leakage time; so that the accuracy and rationality of the determined t is higher, and the accuracy of the transportation risk determination is further improved.

[0064] ​​​​​​​In this embodiment, if the medical unmanned vehicle has a leakage accident during transportation, the harmful substances of the special medical supplies carried by the medical unmanned vehicle will spread in three-dimensional mode, and finally form a three-dimensional risk field. Generally, the diffusion radius of the harmful substances is affected by the wind speed, so the distance of the harmful substances diffusing along the wind direction within a certain time is counted as the diffusion radius of the three-dimensional risk field. Therefore, the volume of the medical unmanned vehicle transportation risk field can be calculated as:

[0065]

[0066] S500, determining the material transportation risk corresponding to the target medical unmanned vehicle route according to R and P

[0067] Further, after step S500, the method can further include the following steps:

[0068] S600, if Risk>Risk', generating a preset transportation risk prompt; wherein Risk' is a preset transportation risk threshold.

[0069] In this embodiment, the risk threshold can be obtained according to the experience of historical transportation. If Risk>Risk', a preset transportation risk prompt is generated to prompt the user that the transportation risk of the target medical unmanned vehicle route is large, and other medical unmanned vehicle transportation routes with smaller risk can be selected.

[0070] In this embodiment, the number N of medical unmanned vehicles on the target medical unmanned vehicle route and the probability P of the medical unmanned vehicle having a leakage accident are obtained n , so that the probability P of the target medical unmanned vehicle route having a leakage accident can be obtained; and the harmful substance component concentration C(x, y, z) of the specified point in the potential pollution area corresponding to the downwind direction of the leakage source is determined according to C(x, y, z) and the limit boundary value C lv of the harmful substance corresponding to the leakage source. The harmful influence radius R of the leakage source is determined according to R and P, and the material transportation risk corresponding to the target medical unmanned vehicle route is determined; so that the medical unmanned vehicle material transportation risk in low-altitude environment can be reasonably analyzed and scientifically measured.

[0071] Further, in this embodiment, when determining the material transportation risk corresponding to the target medical unmanned vehicle route, the number of medical unmanned vehicles on the target medical unmanned vehicle route, the probability of the leakage accident, and the stability parameters of the harmful substance corresponding to the leakage source and the atmospheric environment are combined, so that the determined material transportation risk corresponding to the target medical unmanned vehicle route is more accurate and reasonable.

[0072] Embodiment two:

[0073] Based on the method for determining the risk of medical unmanned aerial vehicle material transportation in Embodiment One, a method for determining the path of medical unmanned aerial vehicle material transportation will be introduced below with reference to the flowchart of the method for determining the path of medical unmanned aerial vehicle material transportation shown in FIG. 3. Figure 1

[0074] The method for determining the path of medical unmanned aerial vehicle material transportation can include the following steps:

[0075] Q100, obtain each flight route between the preset supply point and demand point to obtain a flight route list HA=(HA1, HA2, …, HA α , …, HA β ), α=1, 2, …, β; wherein HA α is the αth flight route between the supply point and the demand point, and β is the number of flight routes between the supply point and the demand point.

[0076] In this embodiment, there are multiple flight routes between the supply point and the demand point, and each flight route can transport medical materials from the supply point to the demand point. For example, as shown in FIG. 4, the supply point is O, the demand point is D, and 1-6 are intermediate nodes. There are three flight routes from the supply point to the demand point. Figure 2

[0077] Q200, obtain the transportation risk and transportation cost corresponding to each flight route to obtain a transportation risk list FA=(FA1, FA2, …, FA α , …, FA β ) and a transportation cost list SA=(SA1, SA2, …, SA α , …, SA β ); wherein FA α is the transportation risk corresponding to HA α , and SA α is the transportation cost corresponding to HA α .

[0078] Further, HA α =(HA α，1 , HA α，2 , …, HA α，γ , …, HA α，f(γ) ); wherein HA α，γ is the γth flight segment of the αth flight route, and f(γ) is the number of flight segments corresponding to the αth flight route.

[0079] In this embodiment, each flight route corresponds to multiple flight segments, as shown in FIG. 5, for example: O-1 is a flight segment, and 1-2 is a flight segment. Each flight segment corresponding to each flight route can be obtained. Figure 2

[0080] FA α ​​​The following steps are taken:

[0081] Q210, obtaining the HA α corresponding to each leg, to obtain the HA α corresponding to each leg, to obtain the HA α = (FB α，1 , FB α，2 , …, FB α，γ , …, FB α，f(γ) ); wherein FB α，γ is the transportation risk corresponding to each leg. α，γ

[0082] Q220, determining FA α according to FB α =∑ f(γ) γ＝1 FB α，γ .

[0083] In this embodiment, the transportation risk corresponding to each leg is calculated, and the transportation risks of each leg corresponding to each route are added to obtain the transportation risk of the corresponding route.

[0084] Further, FA α is determined according to the number of medical unmanned vehicles corresponding to each leg in HA α , the probability of a medical unmanned vehicle having a leakage accident, the attributes of the medical supplies carried by the medical unmanned vehicle, and the corresponding environmental stability parameters; or the transportation risk corresponding to each leg is determined using the leg risk determination method in the prior art.

[0085] Further, the transportation risk of each leg can also be determined by the transportation risk determination method of embodiment one, which is not described here.

[0086] In addition, a and c are empirical values, and are usually 0.02 and 0.89, respectively; b and d can be determined according to the height layer, and the flight height of the medical unmanned vehicle can be obtained in real time; the average wind speed of the low-altitude airspace environment corresponding to the height layer can be obtained by averaging the wind speed monitoring.

[0087] Further, SA α is obtained by the following steps:

[0088] Q230, obtaining the distance of each leg in HA α , to obtain the JL α corresponding to each leg. α = (JL α，1 , JL α，2 , …, JL α，γ , …, JL α，f(γ) ); wherein JL​α，γ for HA α，γ corresponding leg distance.

[0089] Q230, according to the preset unit transportation cost DZ and JL α , determine SA α =DZx∑ f(γ) γ＝1 JL α，γ .

[0090] In this embodiment, the transportation cost can be understood as the transportation cost, and the unit transportation cost of the medical unmanned plane is known, for example: the unit cost of the medical unmanned plane transporting medical supplies is 20 yuan / km; at the same time, the distance of each leg corresponding to each air route can be obtained, so that the transportation cost corresponding to each air route can be obtained.

[0091] Q300, obtaining the population exposure risk corresponding to each air route to obtain a population exposure risk list RA=(RA1, RA2, …, RA α , …, RA β ); wherein, RA α is the population exposure risk corresponding to HA α .

[0092] In this embodiment, when the medical unmanned plane flies on the corresponding air route, the medical unmanned plane will pass through the population exposed area, which also has a certain risk.

[0093] Further, RA α is determined by the following steps:

[0094] Q310, obtaining the population density of each node corresponding to HA α to obtain a population density list MD α =(MD α，1 , MD α，2 , …, MD α，λ , …, MD α，f(γ)+1 ); wherein, MD α，λ is the population density corresponding to the λth node of the αth air route.

[0095] Q320, according to MD α , determine RA α =(1 / (f(γ)+1))∑ f(γ)+1 λ＝1 MD α，λ .

[0096] In this embodiment, each air route corresponds to a plurality of nodes, and the population density in the preset area corresponding to each node can be obtained, and then RA αPopulation density is used as a measure of population exposure risk; that is, the higher the population density, the higher the population exposure risk.

[0097] 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 For HA α The corresponding priority.

[0098] Furthermore, δ α This can be determined through the following steps:

[0099] Q410, obtain the target transportation risk FA' = MAX(FA), the target transportation cost SA' = MAX(SA), and the target population exposure risk RA' = MAX(RA); where MAX() is a preset function to find the maximum value.

[0100] 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.

[0101] In this embodiment, K1+k2+K3=1; K1, k2 and K3 can be fine-tuned according to actual needs. For example, in scenarios where transportation risks are more important, K1 can be increased; through the formula corresponding to the priority, the priority of each route can be limited to the range of 0-1. For any route, the higher the priority of the route, the better the comprehensive indicators of the route, that is, the transportation risk, transportation cost and population exposure risk are more balanced and lower.

[0102] Q500, based on δ, determine the target priority δ' = MAX(δ); where MAX() is the preset maximum value function.

[0103] Q600, the route corresponding to δ' is determined as the target route.

[0104] In the embodiment, the integrated index corresponding to the route with the highest priority is the best, the transportation risk, transportation cost and population exposure risk are balanced and low, and therefore, the route corresponding to δ' is determined as the target route; under the premise of ensuring low transportation risk and population exposure risk, the transportation cost is also low.

[0105] Further, using the method in the embodiment, the transportation risk of each node on each route can also be obtained. When determining the transportation risk of each node, the number of medical unmanned aerial vehicles flying on the corresponding node and the stability parameters of the corresponding atmospheric environment need to be obtained.

[0106] In the embodiment, in order to further verify the sensitivity of the calculation results of the new model to the parameter changes, the maximum carrying capacity, the median of the flight height interval and the median of the wind speed interval in the example are respectively taken as the basic control parameters, and the parameter sensitivity of the analysis model is tested and analyzed by setting different scenarios. Among them, scenario 1: reduce the maximum carrying capacity of the unmanned aerial vehicle by 50%; scenario 2: the median of the flight height interval is doubled, i.e. changed to 340m; scenario 3: the median of the wind speed interval is doubled to 10.6m / s.

[0107] The parameter sensitivity analysis results show that: under the condition that other parameters remain unchanged, in scenario 1, when the maximum carrying capacity is reduced by half, the total risk is reduced by more than half of the basic example; in scenario 2, when the median of the flight height interval is doubled, the total risk is reduced by more than 1 / 3 of the basic example; in scenario 3, when the median of the wind speed interval is doubled, the total risk is also reduced by more than 1 / 3 of the basic example.

[0108] The method for determining the medical unmanned aerial vehicle material transportation path in the embodiment obtains the transportation risk and transportation cost corresponding to each route to obtain the transportation risk list FA and the transportation cost list SA, and the population exposure risk corresponding to each route to obtain the population exposure risk list RA; then, according to FA, SA and RA, the priority corresponding to each route is determined to obtain the 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.

[0109] Further, when determining the priority of the route, the transportation risk, transportation cost and population exposure risk of the route are comprehensively considered, so that the priority of the determined route is more reasonable and accurate.

[0110] Moreover, although individual steps of the methods in the present disclosure are described in a particular order in the figures, this is not required or implied as to the order of execution of the steps, nor is it required that all of the steps be executed for the desired results. Additionally or alternatively, certain steps can be omitted, combined into a single step, broken into multiple steps, and / or executed in a different order than shown.

[0111] Embodiments of the present application also provide a non-transitory computer readable storage medium, which can be arranged in an electronic device to save at least one instruction or at least one program related to a method in the method embodiments, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method provided by the above-mentioned embodiments.

[0112] The program product can employ any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium, for example, can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0113] The computer readable signal medium can include a data signal propagated in baseband or propagated as a carrier wave in a propagated data signal, in which the readable program code is embodied. Such propagated data signal can take a variety of forms, including but not limited to electro-magnetic, optical or any suitable combination thereof. The readable signal medium can also be any readable medium that is not a readable storage medium and that can communicate, propagate or transport program for use by or in connection with an instruction execution system, apparatus or device.

[0114] The program code contained on the readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, and the like, or any suitable combination of the above.

[0115] The program code can be executed by one or more programmable processors, digital signal processors, ASICs, FPGAs, or microprocessors or microcontrollers or other similar consumer electronics devices. The program code can be downloaded to the user's computing device from a network or can be downloaded to the user's computing device from a removable storage device or other computer readable medium. The program code can be executed by one or more programmable processors, digital signal processors, ASICs, FPGAs, or microprocessors or microcontrollers or other similar consumer electronics devices. The program code can be downloaded to the user's computing device from a network or can be downloaded to the user's computing device from a removable storage device or other computer readable medium. The program code can be executed by one or more programmable processors, digital signal processors, ASICs, FPGAs, or microprocessors or microcontrollers or other similar consumer electronics devices. The program code can be downloaded to the user's computing device from a network or can be downloaded to the user's computing device from a removable storage device or other computer readable medium.

[0116] Embodiments of the present application also provide an electronic device including a processor and the aforementioned non-transitory computer readable storage medium.

[0117] Electronic device is merely an example, and should not bring any limitation to the function and use range of embodiments of the present application.

[0118] The electronic device is in the form of a general computing device. Components of the electronic device can include, but are not limited to, the aforementioned at least one processor, the aforementioned at least one memory, a bus connecting different system components, including the memory and the processor.

[0119] The memory stores program code, which can be executed by the processor, so that the processor performs the steps in the various embodiments described in the specification.

[0120] The memory can include a readable medium in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and can further include read only memory (ROM).

[0121] The memory can also include program / utility programs with a set of (at least one) 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 of which can include implementation of a network environment.

[0122] The bus can be one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures.

[0123] The electronic device can also communicate with one or more external devices such as a keyboard or a pointing device, through an I / O interface. The electronic device can communicate with one or more devices that enable a user to interact with it through a communication interface. The electronic device can also communicate with one or more devices or networks (e.g., LANs, WANs, and / or the Internet) through a network adapter. The network adapter can communicate with the other modules of the electronic device through the bus. It should be appreciated that although not shown in the figure, other hardware and / or software modules could be used in conjunction with the electronic device. These include, but are not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0124] From the above description of the 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 software in combination 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 disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the methods according to the embodiments of the present disclosure.

[0125] The embodiments of the present disclosure also provide a computer program product, which includes program codes for causing an electronic device to perform the steps in the methods according to various example embodiments of the present disclosure described above in the specification when the program product is run on the electronic device.

[0126] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be appreciated that the above examples are only for illustration and not intended to limit the scope of the present disclosure. It should also be appreciated by those skilled in the art that various modifications can be made to the embodiments without departing from the scope and spirit of the present disclosure.

Claims

1. A method for determining a medical drone material transport path, characterized by, The method comprises the following steps: 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; wherein, 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; Q200, obtain the transportation risk and transportation cost corresponding to each route to obtain the transportation risk list FA = (FA1, FA2, ..., FA2). α , ..., FA β ) and the transportation cost list SA = (SA1, SA2, ..., SA α SA β ); where FA α For HA α Corresponding transportation risks; SA α For HA α The corresponding transportation costs; Q300, obtaining the population exposure risk corresponding to each route to obtain a population exposure risk list RA=(RA1, RA2, …, RA α N β ); wherein RA α is the population exposure risk corresponding to HA α . Q400, according to FA, SA and RA, determine the priority corresponding to each route to obtain a priority list δ = (δ1, δ2, …, δn) ; wherein δi is the priority corresponding to HAi. α , …, δ β ) α is the priority corresponding to HA α . Q500, determining a target priority δ' = MAX(δ) according to δ; wherein MAX() is a preset maximum function; Q600, determining a target route corresponding to δ' as the target route; The transportation risk corresponding to each route is determined by the following steps: S100, obtaining the number N of medical unmanned aerial vehicles on the target medical unmanned aerial vehicle route; wherein the target medical unmanned aerial vehicle route is any medical unmanned aerial vehicle route; S200, determine the probability P of the occurrence of the leakage accident corresponding to the target medical unmanned aerial vehicle route according to N and the preset probability P of the occurrence of the leakage accident of the medical unmanned aerial vehicle n . n ; S300, determine the concentration of harmful substance components of the specified point in the potential pollution area of the downwind direction corresponding to the leakage source ; wherein 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 speed 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 stability parameters of the atmospheric environment respectively; the leakage source is any medical unmanned aerial vehicle that has a leakage accident; S400, according to C(x, y, z) and the limit boundary value of the harmful substance corresponding to the leakage source C lv , determine the harmful influence radius of the leakage source ; wherein, ; Q cap is the maximum carrying capacity of the medical unmanned aerial vehicle, and t is the leakage time; S500, determining the material transportation risk corresponding to the target medical unmanned aerial vehicle route according to R and P .

2. The method of claim 1, wherein, HA α = (HA α，1 , HA α，2 , …, HA α，γ , …, HA α，f（γ） ); wherein, HA α，γ is the γth leg of the αth air route, and f(γ) is the number of legs corresponding to the αth air route; FA α is obtained by the following steps: Q210, obtaining the HA α corresponding to each voyage section, to obtain the HA α corresponding to each voyage section, to obtain the HA α = (FB α，1 , FB α，2 , …, FB α，γ , …, FB α，f（γ） ); wherein FB α，γ is the transportation risk corresponding to each voyage section; and α，γ is the HA Q220, according to FB α , determine FA α =∑ f（γ） γ=1 FB α，γ .

3. The method of claim 2, wherein, SA α By the following steps: Q230, get HA α the distance of each leg to get HA α the corresponding leg distance list JL α = (JL α，1 , JL α，2 , …, JL α，γ , …, JL α，f（γ） ); wherein JL α，γ is HA α，γ corresponding leg distance; Q230, determines SA according to preset unit transportation cost DZ and JL α , determines SA α =DZ x ∑ f（γ） γ=1 JL α，γ .

4. The method of claim 2, wherein, RA α was determined by the following steps: Q310, obtaining the HA α a population density of each node to obtain a population density list MD α = (MD α，1 , MD α，2 , …, MD α，λ , …, MD α，f（γ）+1 ); wherein MD α，λ is a population density corresponding to the λth node of the αth route; Q320, according to MD α , determine RA α = (1 / (f(y) + 1))∑ f（γ）+1 λ=1 MD α，λ .

5. The method of claim 1, wherein, delta α was determined by the following steps: Q410, obtaining a target transportation risk FA' = MAX(FA), a target transportation cost SA' = MAX(SA), and a target population exposure risk RA' = MAX(RA); wherein MAX() is a preset maximum function; 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.

6. The method of claim 5, wherein, K1+k2+K3=1. 7.A non-transitory computer-readable storage medium having stored therein at least one instruction or at least one piece of program, characterized in that, The at least one instruction or the at least one program is loaded and executed by the processor to realize the medical unmanned aerial vehicle material transportation path determination method according to any one of claims 1-6.

8. An electronic device, comprising: The non-transitory computer readable storage medium of claim 7 comprises a processor.

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