Unmanned aerial vehicle distribution planning method, system, equipment and medium

Abstract

The invention discloses an unmanned aerial vehicle distribution planning method, system and device and a medium, and the method comprises the steps: firstly determining the types of distributed commodities according to the performance parameters of an unmanned aerial vehicle and distribution scene demands, and then calculating the logistics distribution generation amount according to the types of the commodities and the influence factors of the logistics distribution generation amount; wherein the influence factors comprise land use property, geographic location and supply and demand distance; the method comprises the steps of obtaining a customer group portrait, calculating an unmanned aerial vehicle distribution proportion according to the customer group portrait, staged distribution willingness data and an unmanned aerial vehicle facility coverage rate, then generating an unmanned aerial vehicle logistics distribution rate according to a logistics distribution generation amount and the unmanned aerial vehicle distribution proportion, and finally planning unmanned aerial vehicle distribution according to the unmanned aerial vehicle logistics distribution rate. According to the invention, the efficiency and precision of unmanned aerial vehicle distribution demand prediction can be improved, so that the requirement of unmanned aerial vehicle logistics distribution scientific planning is met.

Description

Technical Field

[0001] The present invention relates to the field of drone safety technology, and in particular to a drone delivery planning method, system, equipment and medium. Background Art

[0002] With the rapid development of the low-altitude economy, low-altitude cargo delivery technology has matured and has become a prime application scenario. As an innovative delivery method, urban end-point drone delivery can effectively improve delivery timeliness, easily and conveniently cross mountains, rivers, lakes, and congested roads, and can be used in rainy, snowy, and foggy weather. This, to a certain extent, optimizes resource allocation efficiency, alleviates ground traffic congestion, and complements the existing delivery rider model. Therefore, scientifically forecasting the total demand for drone delivery can facilitate the layout of low-altitude logistics takeoff and landing facilities and the development of low-altitude logistics operations.

[0003] However, as drone logistics and delivery is an emerging field, related demand forecasting methods are still relatively scarce. Patent publication number CN114066062A proposes a method for forecasting high-value, small-volume, and time-sensitive urban food delivery and express delivery using a four-stage traffic method. While this method plays an important role in forecasting urban low-altitude logistics and delivery volumes and can improve work efficiency to a certain extent, it has many issues with effectiveness, implementation difficulty, and rigor, resulting in low forecasting efficiency and accuracy, and is unable to meet the needs of scientific planning for urban terminal drone logistics and delivery. Summary of the Invention

[0004] The purpose of the present invention is to provide a drone delivery planning method, system, equipment and medium, which can improve the efficiency and accuracy of drone delivery demand forecasting, thereby meeting the needs of scientific planning of drone logistics delivery.

[0005] To achieve the above objectives, an embodiment of the present invention provides a drone delivery planning method, comprising:

[0006] Determine the types of goods to be delivered based on the drone's performance parameters and delivery scenario requirements;

[0007] Calculate the logistics distribution generation volume based on the commodity types and factors influencing the logistics distribution generation volume; wherein the influencing factors include land use nature, geographical location, and supply and demand distance;

[0008] Calculate the proportion of drone deliveries based on customer group profiles, phased delivery willingness data, and drone facility coverage;

[0009] Generate a drone logistics delivery rate based on the logistics delivery generation volume and the drone delivery ratio;

[0010] Drone delivery is planned based on the drone logistics delivery rate.

[0011] Optionally, the calculating of the logistics distribution generation volume according to the commodity types and factors influencing the logistics generation volume includes:

[0012] Linking land use and geographical location with the demand and supply sides of each commodity category to obtain the benchmark logistics distribution generation rate for each commodity category in different geographical locations under different land use characteristics;

[0013] The logistics distribution generation volume is calculated based on the benchmark logistics distribution generation rate and the correction coefficient of the supply-demand distance.

[0014] Optionally, the correction coefficient of the supply-demand distance is calculated by the following steps:

[0015] Focusing on the demand side, a buffer zone is created with the preset product delivery distance as the radius;

[0016] When the coverage rate of the supply-side building area is insufficient, the distribution demand probability under the supply-demand distance is reduced by the following formula:

[0017]

[0018] Among them, P d is the probability of distribution demand under the supply-demand distance d, P 标准 is the logistics delivery probability under standard conditions, S d S is the building area of the supply side within the coverage of the supply-demand distance d, 标准 Under standard conditions, the ratio of the supply-side building area to the total building area of the distribution area;

[0019] According to the delivery demand probability under the supply-demand distance, the correction coefficient μ of the supply-demand distance is calculated by the following formula:

[0020]

[0021] Among them, d max The longest distance that can be covered by logistics distribution.

[0022] Optionally, the logistics distribution generation volume is calculated using the following formula:

[0023] P=P b ×μ;

[0024] Among them, P is the amount of logistics distribution generated, P b It is the benchmark logistics distribution generation rate.

[0025] Optionally, the calculation of the drone delivery ratio based on customer group profiles, phased delivery willingness data, and drone facility coverage includes:

[0026] Divide customers into various customer group portraits and calculate the proportion of each group portrait in each type of land use;

[0027] Obtain the drone delivery willingness of various customer groups at different stages;

[0028] Obtain drone facility coverage for various geographical locations;

[0029] The drone delivery ratio is calculated based on the proportion, the drone delivery willingness and the drone facility coverage rate.

[0030] Optionally, planning drone delivery according to the drone logistics delivery rate includes:

[0031] Prioritize the delivery areas according to the drone logistics delivery rate, and allocate the number of drones according to the priority;

[0032] The frequency of drones in the delivery area is adjusted according to the drone logistics delivery rate.

[0033] Optionally, the drone performance parameters include:

[0034] The maximum take-off weight of the drone is less than or equal to 25 kg, the empty weight is less than or equal to 15 kg, the effective load is 3 kg to 10 kg, and the cruising range is 10 km to 20 km.

[0035] To achieve the above objectives, an embodiment of the present invention further provides a drone delivery planning system, comprising:

[0036] The product category selection module is used to determine the types of products to be delivered based on the drone's performance parameters and delivery scenario requirements;

[0037] A logistics distribution generation volume calculation module is used to calculate the logistics distribution generation volume based on the commodity types and factors affecting the logistics distribution generation volume; wherein the factors include land use nature, geographical location and supply and demand distance;

[0038] The drone delivery ratio calculation module is used to calculate the drone delivery ratio based on customer group profiles, phased delivery willingness data, and drone facility coverage;

[0039] A drone logistics delivery rate generation module, configured to generate a drone logistics delivery rate based on the logistics delivery generation volume and the drone delivery ratio;

[0040] The drone planning module is used to plan drone delivery according to the drone logistics delivery rate.

[0041] To achieve the above objectives, an embodiment of the present invention also provides a drone delivery planning device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the drone delivery planning method described in any one of the above items.

[0042] To achieve the above objectives, an embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the drone delivery planning methods described above.

[0043] Compared with the existing technology, the embodiments of the present invention provide a drone delivery planning method, system, equipment and medium. First, by subdividing the delivery scene and combining the drone performance parameters to determine the suitable commodity types, it can accurately match the drone performance with market demand and avoid resource waste; the logistics delivery generation volume is classified and calculated according to the land use nature, geographical location, and supply and demand distance, which can realize the scientific and precise measurement object and accurately grasp the demand scale of various commodities in different regions; and the drone delivery ratio is calculated based on user attributes, acceptance willingness, and facility coverage rate, which can deeply understand the needs of different user groups. At the same time, considering user willingness in stages, the delivery plan can be dynamically adjusted with market acceptance, which has strong feasibility; finally, the logistics delivery generation volume and the drone delivery ratio are used to generate the drone logistics delivery rate, and the drone delivery is planned accordingly, which can realize the scientific and refined delivery process. While rationally planning resources and improving efficiency, it can avoid excessive investment and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings used in the implementation methods. Obviously, the drawings described below are only some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 This is a flow chart of a drone delivery planning method provided by an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the relationship between logistics delivery probability and supply-demand distance under different commodity categories provided by an embodiment of the present invention;

[0047] Figure 3 This is another flowchart of a drone delivery planning method provided by an embodiment of the present invention;

[0048] Figure 4This is a structural block diagram of a drone delivery planning system provided by an embodiment of the present invention;

[0049] Figure 5 This is a structural block diagram of a drone delivery planning device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0051] See also Figure 1 , Figure 1 : is a flow chart of a drone delivery planning method provided by an embodiment of the present invention, wherein the drone delivery planning method comprises steps S1 to S5:

[0052] Step S1: Determine the types of goods to be delivered based on the drone's performance parameters and delivery scenario requirements; the types of goods include takeout delivery, fresh products, medical health, and documents.

[0053] Specifically, in step S1, the drone performance parameters include:

[0054] The maximum take-off weight of the drone is less than or equal to 25 kg, the empty weight is less than or equal to 15 kg, the effective load is 3 kg to 10 kg, and the cruising range is 10 km to 20 km.

[0055] For example, according to the current upper-level planning requirements, the requirements for multi-rotor unmanned aerial vehicles are:

[0056] (a) The maximum takeoff gross weight does not exceed 25 kg and the empty weight does not exceed 15 kg;

[0057] (b) The maximum characteristic dimension does not exceed 3 meters;

[0058] (c) A typical kinetic energy not exceeding 34 kJ.

[0059] Furthermore, statistics from the embodiments of the present invention show that the payload capacity of mainstream delivery aircraft on the market is within 3-10 kg, and the cruising range is generally 10 km to 20 km. Therefore, they are mainly used to deliver small items weighing 1 kg. Table 1 shows the payload and cruising range characteristics of mainstream terminal delivery aircraft on the market provided by the embodiments of the present invention.

[0060] Table 1 Load and endurance characteristics of mainstream terminal delivery aircraft on the market

[0061] model Configuration Power type Payload Range A1 multi-rotor electric 9.5kg 10km A2 multi-rotor electric 3kg 12km B1 multi-rotor electric 12kg 20km B2 multi-rotor electric 3kg 20km C multi-rotor electric 150kg 20km D multi-rotor electric 30kg 16km E1 multi-rotor electric 24.8kg 27km E2 multi-rotor electric 5kg 20km F multi-rotor electric 15kg 20km G multi-rotor electric 5kg 20km H multi-rotor electric 7kg 18km I multi-rotor electric 3kg 20km

[0062] It's worth noting that while there are numerous drone types on the market, the embodiments of the present invention, based on specific standards and performance parameters, can precisely identify the range of drones suitable for logistics and delivery. This is what distinguishes them from other common logistics and delivery solutions. While individual parameters are publicly available, the embodiments of the present invention form innovative combinations through cross-domain data correlation, combining drone performance with product type, weight, and delivery distance. Furthermore, they exclude inappropriate scenarios based on the parameter range (e.g., a D-type drone with a payload of 30kg may be suitable for freight, but may not meet the needs of urban terminal delivery).

[0063] In addition, the embodiments of the present invention found through statistics that the logistics and distribution scenarios are mainly divided into takeaway scenarios, supermarket scenarios and express delivery scenarios, as shown in Table 2.

[0064] Table 2 Common logistics and distribution scenarios

[0065]

[0066]

[0067] Therefore, based on factors such as the requirements of higher-level planning, current drone performance parameters, and delivery requirements in various scenarios, the embodiments of the present invention focus on drone logistics delivery in takeout scenarios.

[0068] Furthermore, considering that drone logistics delivery is more timely than manual delivery, but the cost is also relatively high, the embodiment of the present invention, based on a comprehensive consideration of cost and benefit, as well as an analysis of market demand and delivery characteristics, concludes that drones are mainly suitable for the following four types of delivery: takeout delivery, fresh products, medical health, and document information. These can complement existing takeout / flash delivery and platform delivery, as shown in Table 3.

[0069] Table 3 UAV delivery adaptation types

[0070]

[0071] Step S2: Calculate the logistics distribution generation volume based on the commodity types and the factors affecting the logistics distribution generation volume; wherein the influencing factors include the nature of land use, geographical location and supply-demand distance; the nature of land use includes residential land, public management and public service facility land and commercial service land, and the geographical location includes the central area and the peripheral area.

[0072] For example, the current land use characteristics of a particular urban area are used as the raw data for analysis. Logistics distribution generation refers to the total logistics distribution demand generated by various types of goods within a specific area and within a specific timeframe. This quantitatively reflects the scale of logistics distribution demand, taking into account factors such as land use characteristics, geographical location, and the distance between supply and demand.

[0073] It's important to note that the nature of land use determines an area's primary functions and activity types, thus influencing demand for end-of-line logistics and distribution. For example, residential land is a primary demand area for food delivery and fresh produce delivery. Geographic location influences the accessibility and cost of delivery. Compared to peripheral areas, city centers are densely populated and have a high demand for delivery. The distance between supply and demand directly impacts delivery costs and efficiency. Within cities, demand for food delivery and fresh produce delivery is high within a range of 500 to 1,200 meters. Less than 500 meters eliminates the need for logistics and distribution, while distances above this fixed distance exceed food delivery service capacity.

[0074] It should be noted that the nature of land use determines the types of goods that are distributed and the direction of the flow of goods. The correlation between various types of land use and land use is shown in Table 4 below.

[0075] Table 4 Relationship between land use nature and demand flow

[0076]

[0077] Specifically, according to the differences in residential area floor area ratios, residential land can be divided into: Class I residential areas, Class II residential areas, and Class III residential land. Among them, Class I residential land is usually low-density, high-quality residential areas. Residents have high requirements for the quality and privacy of delivery services. The delivery demand is relatively scattered but the single value is high. Class II residential land is mainly multi-story and high-rise residential buildings with high population density. The delivery demand is large and diversified, which is suitable for a centralized delivery model. Class III residential land is mainly urban villages, old communities, or simple residences with relatively poor facilities. The delivery demand is mainly based on cost-effectiveness, and the requirements for delivery timeliness are relatively low. The relationship between residential land and commodity delivery demand is shown in Table 5:

[0078] Table 5 Relationship between residential land use and commodity distribution demand

[0079]

[0080]

[0081] Specifically, according to the "Urban Land Classification and Planning and Construction Land Standards", public administration and public service facilities land mainly includes 9 categories, among which logistics and distribution demand is mainly concentrated in the four categories of education and scientific research land, administrative office land, cultural facilities land, and medical and health land. Among the education and scientific research land, logistics and distribution demand is mainly concentrated in the land of colleges and universities, secondary vocational schools, and scientific research land, while the logistics and distribution demand for primary and secondary school land and special education land is relatively low. Therefore, it is necessary to carry out classification and measurement. The classification of public administration and public service facilities land is shown in Table 6:

[0082] Table 6 Classification of land used for public administration and public service facilities and its relationship with commodity distribution demand

[0083]

[0084]

[0085] Specifically, according to the "Urban Land Classification and Planning and Construction Land Standards," commercial service land mainly includes four categories. Among them, logistics and distribution demand is mainly concentrated in three categories: commercial land, business land, and entertainment and recreation land. The logistics and distribution demand in commercial land is mainly concentrated in retail commercial land, catering land, and hotel land. The distribution direction of each type of land is different, so it is calculated by classification. The classification of commercial service facility land and its relationship with commodity distribution demand are shown in Table 7:

[0086] Table 7 Classification of land used for commercial service facilities and its relationship with commodity distribution demand

[0087]

[0088] It should be noted that geographical location determines economic income, and economic income affects the flow of goods. The embodiment of the present invention can divide the city into two circles: the central area and the peripheral area according to the speed and degree of urban development. Among them, the central area has a dense population and strong consumption capacity, and has high requirements for fresh food quality and delivery timeliness. Fresh food stores and delivery services are highly concentrated, and the delivery range is small but the density is high; the peripheral area has a low population density, relatively low consumption capacity, and high requirements for cost-effectiveness. Fresh food stores and delivery services are sparsely distributed, and the delivery range is large but the density is low. Therefore, the logistics and distribution demand of the population in each circle is determined by the economic level, and the number of young and middle-aged people determines the possible probability of receiving drone delivery. The relationship between geographical location and logistics and distribution demand is shown in Table 8:

[0089] Table 8 Relationship between geographical location and logistics distribution demand

[0090]

[0091]

[0092] It should be noted that the distance between the demand side and the supply side is an inverse function. This is because if it is a commercial complex, the demand for logistics distribution is reduced. If the distribution distance is too long, it will easily affect the taste of the food and increase the logistics cost. Therefore, in the embodiment of the present invention, the correlation model between the distance between the demand side and the supply side and the selection probability is constructed as follows:

[0093]

[0094] Among them, d ij is the distance from demand point i to supply point j, d u is the appropriate service distance, d v is the maximum service distance.

[0095] See also Figure 2 , Figure 2 This is a schematic diagram of the relationship between logistics delivery probability and supply-demand distance under different commodity categories provided by the embodiment of the present invention. Figure 2 As shown in the figure, a curve chart shows how the probability of drone delivery for different types of goods changes with distance. The horizontal axis represents distance in meters (m), ranging from 100m to 12000m, indicating the distance between the demand point and the supply point. The vertical axis represents probability, ranging from 0 to 1, reflecting the possibility of using drones to deliver the corresponding goods at the corresponding distance.

[0096] Specifically, the probability of use for takeout delivery rises rapidly at close distances (around 100-2000m), peaking at nearly 1 around 2000m. It then declines rapidly with increasing distance, reaching zero at around 6000m. This indicates that takeout is well-suited for drone delivery over short distances. Beyond a certain range, drone use is highly unlikely. The probability of use for fresh produce rises gradually from 100m, peaking at around 3000-4000m (near 1), then slowly declines, reaching zero at 12000m. This indicates that drone use is more likely for fresh produce within medium- and short-range delivery, but the acceptable delivery distance is slightly longer than that for takeout. The probability of use for pharmaceuticals follows a similar upward trend as for takeout, peaking at around 2000-3000m (near 1), then declining at a rate intermediate between takeout and fresh produce, reaching zero at around 9000m. This suggests that pharmaceuticals also have a high probability of being delivered by drone over medium- and short-range delivery, but the acceptable delivery distance is slightly shorter than that for fresh produce. The probability of document use rises relatively slowly, reaching a peak (close to 1) at around 4,000-5,000 meters, and then declines slowly, reaching 0 at 12,000 meters. This suggests that drone delivery of documents over relatively long distances is still possible, and the acceptable delivery distance is longer than for other commodities.

[0097] The probability of drone delivery varies across different products, with distance showing different trends. Generally speaking, the longer the distance, the lower the probability of drone delivery. Due to the varying characteristics of each product (such as timeliness and value), the distance ranges suitable for drone delivery vary.

[0098] In an optional embodiment, step S2 includes:

[0099] Step S201: Associating land use properties and geographical locations with the demand side and supply side of each commodity type to obtain the benchmark logistics distribution generation rate of each commodity type in different geographical locations under different land use properties;

[0100] Step S202: Calculate the logistics distribution generation volume based on the benchmark logistics distribution generation rate and the correction coefficient of the supply-demand distance.

[0101] It should be noted that, through the above analysis, the embodiments of the present invention have closely linked the nature of land use and geographical location with the demand and supply sides of various commodity categories. On the demand side, in the central area of residential land, residents may have a higher demand for immediate fresh food, because residents in the central area have a fast pace of life and prefer convenient fresh food delivery services; while in the peripheral areas of residential land, residents may pay more attention to the cost-effectiveness of fresh food, and the demand frequency may be relatively low. On the supply side, the central area has developed commerce, with dense distribution of fresh food supermarkets, convenience stores, etc., and strong supply capacity; the peripheral areas may have limited supply capacity due to relatively insufficient commercial facilities.

[0102] Furthermore, existing research indicates that when current logistics and delivery order data is lacking, traffic travel rates can be used to estimate cargo generation. Therefore, this embodiment of the present invention, based on the "Traffic Travel Rate Handbook," selects typical land use types and uses traffic travel rates and current social data to predict logistics and delivery demand for various commodities within each land use type, as shown in Table 9.

[0103] Table 9: Logistics distribution generation rate / attraction rate for various commodity categories in different geographical locations under different land use characteristics (unit: times / day)

[0104]

[0105]

[0106] Furthermore, by analyzing the logistics distribution generation rate / attraction rate of each commodity type in different geographical locations under different land use properties, the benchmark logistics distribution generation rate of each commodity type in different geographical locations under different land use properties can be obtained.

[0107] It should be noted that even with the same land use nature and the same geographical location, due to differences in the surrounding supporting environment, the logistics distribution demand, distribution frequency, etc. will be affected to a certain extent. Therefore, the embodiment of the present invention proposes a correction coefficient based on the supply and demand distance.

[0108] In an optional embodiment, in step S202, the correction coefficient of the supply-demand distance is calculated by the following steps:

[0109] Focusing on the demand side, a buffer zone is created with the preset product delivery distance as the radius;

[0110] When the coverage rate of the supply-side building area is insufficient, the distribution demand probability under the supply-demand distance is reduced by the following formula:

[0111]

[0112] Among them, P d is the probability of distribution demand under the supply-demand distance d, P 标准 is the logistics delivery probability under standard conditions, S d S is the building area of the supply side within the coverage of the supply-demand distance d, 标准 Under standard conditions, the proportion of the supply-side building area to the total building area of the distribution area.

[0113] According to the delivery demand probability under the supply-demand distance, the correction coefficient μ of the supply-demand distance is calculated by the following formula:

[0114]

[0115] Among them, d max The longest distance that can be covered by logistics distribution, such as Figure 2 As shown, the longest distance that can be covered by takeout logistics delivery is 6km, the longest distance that can be covered by fresh food logistics delivery is 9km, the longest distance that can be covered by medicine logistics delivery is 9km, and the longest distance that can be covered by document logistics delivery is 12km.

[0116] Furthermore, in step S202, the logistics distribution generated amount is calculated using the following formula:

[0117] P=P b ×μ;

[0118] Among them, P is the amount of logistics distribution generated, P b It is the benchmark logistics distribution generation rate.

[0119] S3. Calculate the drone delivery ratio based on customer group profiles, phased delivery willingness data, and drone facility coverage;

[0120] In an optional embodiment, step S3 includes:

[0121] Step S301: Divide customers into various customer group portraits, and calculate the proportion of each group portrait in each type of land use; wherein the various group portraits include college students, urban blue-collar workers, urban white-collar workers, busy parents, and mature elites;

[0122] Step S302: Obtain drone delivery willingness of various customer groups in various stages; wherein the stages include short-term, medium-term and long-term;

[0123] Step S303: Obtaining drone facility coverage rates for various geographical locations; wherein the various geographical locations include central areas and peripheral areas;

[0124] Step S304: Calculate the drone delivery ratio based on the proportion, the drone delivery willingness, and the drone facility coverage rate.

[0125] It should be noted that the drone delivery ratio refers to the proportion of planned or actual drone delivery in the total logistics delivery volume. It is used to reflect the application level and development trend of drones in the entire logistics distribution system, and to measure the penetration of drone delivery in the market.

[0126] For example, in a more specific embodiment, the drone delivery ratio is calculated based on customer group profiles, phased delivery willingness data, and drone facility coverage, specifically including the following steps:

[0127] Step 1: Identify the basic attributes of users who use urban terminal drone logistics delivery under the current situation, such as age, gender, education level, occupation, monthly income, etc., to create customer profiles.

[0128] Specifically, the urban end-to-end delivery groups can be divided into five main categories: college students, urban blue-collar workers, urban white-collar workers, busy parents, and mature elites. College students prioritize convenience, variety, and affordability, with demand concentrated during peak meal times and high requirements for eco-friendly packaging. Urban blue-collar workers prioritize price and convenience, with demand concentrated during weekday lunch and dinner. Urban white-collar workers pursue high-quality, healthy dining, demanding delivery efficiency and service quality, and are willing to pay a premium. Busy parents prioritize food safety and nutrition, with demand concentrated during family mealtimes, preferring healthy and nutritious meals. Mature elites prioritize quality, privacy, and personalized service, and are willing to pay a premium for high-quality services. Table 10 shows the logistics and delivery demand relationships for various customer group profiles provided by this embodiment of the present invention.

[0129] Table 10 Logistics and distribution demand relationships of each customer group portrait

[0130]

[0131]

[0132] Step 2: Calculate the proportion of each group portrait in each type of land use. The proportion of each group portrait in each type of land use is shown in Table 11.

[0133] Table 11 The proportion of each group portrait in each type of land use

[0134]

[0135]

[0136] Step 3: Calculate the willingness of various groups to use drone delivery. Based on the distribution considerations, the willingness to accept in different stages such as short-term, medium-term and long-term can be calculated.

[0137] For example, 200 online and offline questionnaires were distributed to various groups to determine their willingness to use drone logistics delivery. Among college students, the short-term, medium-term, and long-term willingness rates were 35%, 47%, and 69%, respectively; among urban blue-collar workers, the short-term, medium-term, and long-term willingness rates were 25%, 45%, and 62%, respectively; among urban white-collar workers, the short-term, medium-term, and long-term willingness rates were 27%, 47%, and 64%, respectively; among busy parents, the short-term, medium-term, and long-term willingness rates were 23%, 40%, and 55%, respectively; and among established elites, the short-term, medium-term, and long-term willingness rates were 29%, 39%, and 49%. Statistically, college students showed the highest willingness to use drone delivery, while established elites showed the lowest willingness.

[0138] Step 4: Obtain drone delivery coverage for various geographic locations, including coverage in the short, medium, and long term.

[0139] Specifically, in the near term (2027), demonstration operation will be carried out, and the coverage rate of drone logistics distribution needs in the central area will be up to 25%, and the coverage rate of drone logistics distribution needs in the peripheral areas will be up to 16%; in the medium term (2035), gradual promotion will be carried out, and the coverage rate of drone logistics distribution needs in the central area will be up to 35%, and the coverage rate of drone logistics distribution needs in the peripheral areas will be up to 30%; in the long term (2050), all projects should be built, and the coverage rate of drone logistics distribution needs in the central area will be up to 45%, and the coverage rate of drone logistics distribution needs in the peripheral areas will be up to 50%.

[0140] Step 5: Calculate the drone logistics delivery ratio:

[0141] The proportion of drone logistics delivery = the proportion of different groups in each type of land use in step 2 * the willingness of each group to use drone delivery in step 3 * the drone delivery coverage rate of each geographical location in step 4.

[0142] For example, the recent drone delivery ratio in a residential underground central area = recent coverage rate *

[0143] (Proportion of urban blue-collar workers in land use * Proportion of urban blue-collar workers who have recently received drone logistics delivery +

[0144] The proportion of urban white-collar workers in land use*The proportion of urban white-collar workers who have recently accepted drone logistics delivery+

[0145] The proportion of busy parents in land use*The proportion of busy parents who have recently accepted drone logistics delivery+

[0146] The proportion of mature elites in land use*The proportion of mature elites who have recently accepted drone logistics delivery)

[0147] =25%*(6%*25%+10%*27%+20%*24%+64*29%)=6.89%, take 7%.

[0148] S4. Generate a drone logistics delivery rate based on the logistics delivery generated volume and the drone delivery ratio;

[0149] It should be noted that the drone logistics delivery rate is calculated through the logistics delivery generation volume and the drone delivery ratio. It refers to the number of logistics deliveries carried out by drones per unit time and per unit area. It is a key indicator to measure the actual delivery capacity and efficiency of drones, and is used to guide the specific configuration of drone delivery resources in the future.

[0150] For example, the recent takeaway drone logistics delivery rate (times / day / 100m) in a residential underground central area 2 ) = the amount of takeout logistics distribution generated in the first-class residential underground central area * the recent drone logistics distribution ratio in the first-class residential underground central area = 0.02 * 7% = 0.14% times / day / 100m 2 After converting the units, it is 0.14 times / day / 10000m 2 .

[0151] S5. Plan drone delivery according to the drone logistics delivery rate.

[0152] Specifically, step S5 includes:

[0153] Step S501: Prioritize the delivery areas according to the drone logistics delivery rate, and allocate the number of drones according to the priority;

[0154] Step S502: Adjust the frequency of drones in the delivery area according to the drone logistics delivery rate.

[0155] For example, let's assume a city has three distinct delivery zones, A, B, and C. A represents the central commercial area, B is a type of residential land, and C is administrative office land. Based on the highest drone delivery rate, the delivery zones are prioritized as Zone A > Zone B > Zone C. For a total of 20 delivery drones, based on priority, 10 could be allocated to Zone A, as this area has the highest delivery demand; 7 could be allocated to Zone B to meet daily delivery needs in residential areas; and 3 could be allocated to Zone C to ensure basic delivery services for administrative offices.

[0156] Furthermore, the frequency of drone delivery within a delivery area is adjusted based on the drone delivery rate. Area A has a high delivery rate. To ensure timely delivery, the drone delivery frequency is set to three times per hour. During peak hours (e.g., 11:00-1:00 PM and 5:00-7:00 PM), the frequency is further increased to four times per hour to accommodate the high volume of orders. Area B has a moderate delivery rate. Under normal circumstances, the drone delivery frequency is set to two times per hour. During peak hours in the morning and evening when residents are returning home from get off work (e.g., 6:00 PM-8:00 PM), the frequency is adjusted to three times per hour to ensure residents receive fresh produce, takeout, and other items quickly. Area C has a relatively low delivery rate. Normally, the drone delivery frequency is set to once per hour. During administrative office areas with urgent material delivery needs or during specific work hours (e.g., concentrated document delivery at the beginning of each month), the frequency is temporarily increased to two times per hour. Through this approach, delivery areas are prioritized, drone numbers are allocated, and delivery frequencies are adjusted based on drone delivery rates, enabling rational planning of drone delivery, thereby improving logistics delivery efficiency and service quality.

[0157] In summary, see Figure 3 , Figure 3 This is another flow chart of a drone delivery planning method according to an embodiment of the present invention. Figure 3As shown, the embodiment of the present invention first subdivides the delivery scenarios, including takeout scenarios, express delivery scenarios, and supermarket scenarios. At the same time, combined with the upper-level planning requirements and the current status of aircraft and other delivery requirements, it clarifies the types of goods suitable for drone delivery, namely, takeout delivery, fresh products, medical health, and document information; then, considering the three influencing factors of land use nature, geographical location, and supply-demand distance, it comprehensively calculates the generation volume of various types of land and goods, thereby obtaining the logistics distribution generation volume, and considering user attributes (college students, urban blue-collar workers, urban white-collar workers, busy parents, mature elites), analyzes their acceptance willingness in different stages in the short, medium, and long term, and calculates the drone delivery ratio based on the location preference (first-class residential area, second-class residential area, commercial office buildings, colleges and universities, etc.) and facility coverage (central area, peripheral area). Based on the previously obtained logistics distribution generation volume and the calculated drone delivery ratio, the drone logistics distribution rate is calculated and generated. Finally, based on the obtained drone logistics distribution rate, drone delivery is planned.

[0158] By segmenting delivery scenarios and determining compatible products based on planning requirements and aircraft status, the present invention accurately matches drone performance with market demand, avoiding resource waste. Logistics delivery volume is categorized and calculated based on land use, geographic location, and supply-demand distance, achieving scientific and precise measurement targets and accurately determining the scale of demand for various commodities in different regions. Calculating drone delivery ratios based on user attributes, acceptance willingness, and facility coverage provides a deep understanding of the needs of different user groups. By factoring user preferences in stages, delivery planning can be dynamically adjusted based on market acceptance, demonstrating strong practical feasibility. Using logistics delivery volume and drone delivery ratios to generate drone logistics delivery rates, delivery planning can be tailored to specific areas, such as prioritizing regions and adjusting delivery frequency. This streamlines and refines the delivery process, while rationalizing resource allocation and improving efficiency, avoiding overinvestment and reducing operating costs.

[0159] In addition, the embodiment of the present invention realizes the modular operation of the entire process from the identification of the types of goods delivered by drones, the calculation of the traffic generation volume of various types of plots to the proportion of drone logistics delivery. It can quickly identify the urban terminal drone delivery volume in any area, greatly improve the prediction efficiency, and perform refined calculations for differentiated building land types, delivery acceptance by different groups, and take-off and landing facility coverage by time period, effectively avoiding the problem of insufficient accuracy caused by homogenization processing in traditional methods, and significantly improving the prediction accuracy. The embodiment of this case is both flexible and universal, and is suitable for logistics demand forecasting in large areas such as cities and districts. It can also perform refined forecasting for planning management units or single plots to meet the prediction needs in different scenarios.

[0160] See also Figure 4 , Figure 4: is a structural block diagram of a drone delivery planning system 200 provided in an embodiment of the present invention, wherein the drone delivery planning system 200 includes:

[0161] The commodity category selection module 21 is used to determine the type of goods to be delivered based on the performance parameters of the drone and the requirements of the delivery scenario;

[0162] The logistics distribution generation volume calculation module 22 is used to calculate the logistics distribution generation volume based on the commodity types and factors affecting the logistics distribution generation volume; wherein the factors affecting the logistics distribution generation volume include land use nature, geographical location and supply and demand distance;

[0163] The drone delivery ratio calculation module 23 is used to calculate the drone delivery ratio based on customer group profiles, phased delivery willingness data, and drone facility coverage;

[0164] a drone logistics delivery rate generating module 24, configured to generate a drone logistics delivery rate according to the logistics delivery generation volume and the drone delivery ratio;

[0165] The drone planning module 25 is used to plan drone delivery according to the drone logistics delivery rate.

[0166] In an optional embodiment, the logistics distribution generation volume calculation module 22 is specifically used to:

[0167] Linking land use and geographical location with the demand and supply sides of each commodity category to obtain the benchmark logistics distribution generation rate for each commodity category in different geographical locations under different land use characteristics;

[0168] The logistics distribution generation volume is calculated based on the benchmark logistics distribution generation rate and the correction coefficient of the supply-demand distance.

[0169] In an optional embodiment, the drone delivery ratio calculation module 23 is specifically used to:

[0170] Divide customers into various customer group portraits and calculate the proportion of each group portrait in each type of land use;

[0171] Obtain the drone delivery willingness of various customer groups at different stages;

[0172] Obtain drone facility coverage for various geographical locations;

[0173] The drone delivery ratio is calculated based on the proportion, the drone delivery willingness and the drone facility coverage rate.

[0174] In an optional embodiment, the drone planning module 25 is specifically configured to:

[0175] Prioritize the delivery areas according to the drone logistics delivery rate, and allocate the number of drones according to the priority;

[0176] The frequency of drones in the delivery area is adjusted according to the drone logistics delivery rate.

[0177] It should be noted that the drone delivery planning system provided in an embodiment of the present invention is used to execute all the process steps of a drone delivery planning method in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0178] See also Figure 5 , Figure 5 3 is a block diagram of a drone delivery planning device 300 provided in an embodiment of the present invention. The drone delivery planning device 300 includes a processor 31, a memory 32, and a computer program stored in the memory 32 and executable on the processor 31. When the processor 31 executes the computer program, the steps of each of the drone delivery planning method embodiments described above, such as steps S1 to S5, are implemented.

[0179] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 32 and executed by the processor 31 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the drone delivery planning device 300.

[0180] The drone delivery planning device 300 may include, but is not limited to, a processor 31 and a memory 32. Those skilled in the art will appreciate that the schematic diagram is merely an example of the drone delivery planning device 300 and does not limit the drone delivery planning device 300 . The drone delivery planning device 300 may include more or fewer components than shown, or may combine certain components or different components. For example, the drone delivery planning device 300 may also include input and output devices, network access devices, buses, and the like.

[0181] The processor 31 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor. The processor 31 is the control center of the drone delivery planning device 300, and utilizes various interfaces and lines to connect various parts of the entire drone delivery planning device 300.

[0182] The memory 32 can be used to store the computer programs and / or modules. The processor 31 implements the various functions of the drone delivery planning device 300 by running or executing the computer programs and / or modules stored in the memory 32 and accessing the data stored in the memory 32. The memory 32 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). Furthermore, the memory 32 may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0183] Among them, if the modules / units integrated in the drone delivery planning device 300 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor 31, it can implement the steps of the above-mentioned various method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0184] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A drone delivery planning method, characterized in that: include: Determine the types of goods to be delivered based on the drone's performance parameters and delivery scenario requirements; Calculate the logistics distribution generation volume based on the commodity types and factors influencing the logistics distribution generation volume; wherein the influencing factors include land use nature, geographical location, and supply and demand distance; Calculate the proportion of drone deliveries based on customer group profiles, phased delivery willingness data, and drone facility coverage; Generate a drone logistics delivery rate based on the logistics delivery generation volume and the drone delivery ratio; Drone delivery is planned based on the drone logistics delivery rate.

2. The drone delivery planning method according to claim 1, wherein: The calculating of the logistics distribution generation volume according to the commodity types and the factors influencing the logistics generation volume includes: Linking land use and geographical location with the demand and supply sides of each commodity category to obtain the benchmark logistics distribution generation rate for each commodity category in different geographical locations under different land use characteristics; The logistics distribution generation volume is calculated based on the benchmark logistics distribution generation rate and the correction coefficient of the supply-demand distance.

3. The drone delivery planning method according to claim 2, wherein: The correction coefficient of the supply-demand distance is calculated by the following steps: Centered on the demand side, a buffer zone is created with the preset product delivery distance as the radius; When the coverage rate of the supply-side building area is insufficient, the distribution demand probability under the supply-demand distance is reduced by the following formula: Among them, P d is the probability of distribution demand under the supply-demand distance d, P 标准 is the logistics delivery probability under standard conditions, S d S is the building area of the supply side within the coverage of the supply-demand distance d, 标准 Under standard conditions, the ratio of the supply-side building area to the total building area of the distribution area; According to the delivery demand probability under the supply-demand distance, the correction coefficient μ of the supply-demand distance is calculated by the following formula: Among them, d max The longest distance that can be covered by logistics distribution.

4. The drone delivery planning method according to claim 3, wherein: The logistics distribution generation volume is calculated using the following formula: P=P b ×μ; Among them, P is the amount of logistics distribution generated, P b It is the benchmark logistics distribution generation rate.

5. The drone delivery planning method according to claim 1, wherein: The calculation of drone delivery ratio based on customer group profiles, phased delivery willingness data and drone facility coverage includes: Divide customers into various customer group portraits and calculate the proportion of each group portrait in each type of land use; Obtain the drone delivery willingness of various customer groups at different stages; Obtain drone facility coverage for various geographical locations; The drone delivery ratio is calculated based on the proportion, the drone delivery willingness and the drone facility coverage rate.

6. The drone delivery planning method according to claim 1, wherein: The planning of drone delivery according to the drone logistics delivery rate includes: Prioritize the delivery areas according to the drone logistics delivery rate, and allocate the number of drones according to the priority; The frequency of drones in the delivery area is adjusted according to the drone logistics delivery rate.

7. The drone delivery planning method according to claim 1, wherein: The UAV performance parameters include: The maximum take-off weight of the drone is less than or equal to 25 kg, the empty weight is less than or equal to 15 kg, the effective load is 3 kg to 10 kg, and the cruising range is 10 km to 20 km.

8. A drone delivery planning system, characterized in that: include: The product category selection module is used to determine the types of products to be delivered based on the drone's performance parameters and delivery scenario requirements; A logistics distribution generation volume calculation module is used to calculate the logistics distribution generation volume based on the commodity types and factors affecting the logistics distribution generation volume; wherein the factors include land use nature, geographical location and supply and demand distance; The drone delivery ratio calculation module is used to calculate the drone delivery ratio based on customer group profiles, phased delivery willingness data, and drone facility coverage; A drone logistics delivery rate generation module, configured to generate a drone logistics delivery rate based on the logistics delivery generation volume and the drone delivery ratio; The drone planning module is used to plan drone delivery according to the drone logistics delivery rate.

9. A drone delivery planning device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the drone delivery planning method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the drone delivery planning method according to any one of claims 1 to 7.

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