A method and system for multi-dimensional dynamic pricing of unmanned aerial vehicle operations

Abstract

The application discloses a kind of unmanned aerial vehicle operation service multidimension dynamic pricing method and system.The method includes the following steps: receiving the service order request sent by user;According to service order request, obtain area coefficient, specification coefficient and meteorological coefficient;Get the information of forbidden flight area around service site and calculate airspace complexity coefficient;Obtain the digital elevation data and building density data of service site and calculate flight complexity coefficient;Area coefficient, specification coefficient, meteorological coefficient, airspace complexity coefficient and flight complexity coefficient are multiplied with the preset reference price to generate the final offer;The real-time value of all coefficients, calculation basis and original data source are serialized into structured data and simultaneously persisted with the final offer.The application can perceive the change of external environmental factors such as meteorological condition, airspace control state and terrain flight complexity in real time, realize the effective correspondence of price and actual operation risk cost.

Description

Technical Field

[0001] This invention relates to the field of drone service technology, and in particular to a multi-dimensional dynamic pricing method and system for drone operation services. Background Technology

[0002] With the rapid development of drone technology and the booming low-altitude economy, drone operation services have been widely applied in various fields such as agricultural plant protection, geographic surveying and mapping, logistics distribution, infrastructure inspection, and aerial filming. Drone service pricing, as a core component of the business model of drone service platforms, directly affects the platform's operational efficiency, pilots' economic benefits, and customers' purchasing decisions. Unlike traditional offline service industries (such as housekeeping and express delivery), drone operation services are highly sensitive to environmental conditions and safety constraints. Their service costs and operational risks are influenced by multiple external factors, including weather conditions, airspace control status, and terrain features. These factors can lead to significant differences in service costs and risk levels, sometimes by orders of magnitude, depending on the time, location, and task type.

[0003] Currently, most drone service platforms on the market use a two-dimensional fixed price list based on region and specifications for pricing services. This involves maintaining a static price matrix according to administrative divisions and drone models or job types, with customers directly querying the corresponding price when placing an order. While this static pricing scheme is simple and easy to understand, it has a fundamental flaw: it cannot perceive or respond to changes in the external physical environment upon which drone operations depend. The safety boundaries of drone flight operations are rigidly constrained by objective physical conditions. For example, when wind speeds exceed safety thresholds, flight missions must be refused. Fixed price lists cannot convey price signals corresponding to dynamic environmental risks to pilots, leading to pilots refusing orders under high-risk conditions or causing safety accidents. Furthermore, no-fly zones are dynamically adjusted according to major event security, disaster emergency response, etc. Dense urban building clusters significantly interfere with drone positioning signals, and mountainous and hilly areas require frequent adjustments to flight altitude to adapt to terrain changes. These factors all substantially impact the difficulty of drone operation, compliance costs, and safety risks, but fixed price lists completely fail to reflect these differences. More importantly, when seasonal supply and demand changes cause significant fluctuations in market demand, the response time for manually modifying price lists usually takes several days to several weeks, making it difficult to seize fleeting market opportunities.

[0004] Some platforms have introduced a manual surcharge mechanism on top of fixed prices, allowing operators to manually add additional fees such as severe weather surcharges and remote area surcharges based on actual conditions. However, this approach also has significant shortcomings. The collection of surcharges relies entirely on the operator's subjective judgment, lacking objective and unified calculation criteria and quantitative standards, resulting in low pricing transparency, which is often unacceptable to customers and prone to price disputes. Furthermore, manual judgment cannot provide real-time responses, lacking effective automated handling mechanisms for emergencies such as sudden thunderstorms and temporary airspace control. When multiple adverse factors occur simultaneously, such as strong winds combined with proximity to no-fly zones and complex terrain, the existing scheme lacks a unified mathematical model to support how to calculate or exclude various surcharge items, leading to inconsistent calculation results among different operators. In addition, the surcharge collection process lacks complete record archiving, making it impossible to retrospectively audit pricing decisions and posing significant regulatory compliance risks.

[0005] Some startups have attempted to adopt dynamic pricing models from ride-hailing or food delivery sectors, adjusting service prices based on real-time supply and demand ratios. While this approach can automatically adjust prices according to market supply and demand, it is fundamentally incompatible with the characteristics of drone operation services. Ride-hailing service supply is primarily influenced by traffic conditions and driver status, with flexible safety boundaries. In contrast, drone operation safety boundaries are rigid constraints; deteriorating weather conditions or airspace access restrictions have a veto power, and safety risks cannot be simply covered by adjusting prices. Furthermore, drone operations in specific airspaces must comply with the regulatory requirements of civil aviation authorities, and compliance costs must be clearly reflected in the price, not merely a passive reflection of market supply and demand. More importantly, ride-hailing pricing models fail to understand the impact of drone payload capacity, range, and altitude on operating costs, making it impossible to construct a pricing function that accurately reflects the true cost structure of drone services.

[0006] In summary, existing drone service pricing technologies have failed to construct a systematic and dynamic pricing model that simultaneously covers both market and environmental factors. Existing solutions either completely ignore the unique environmental sensitivity of drone operations, leading to a disconnect between pricing and actual risk costs; or they rely on subjective human judgment, resulting in a lack of transparency and auditability in the pricing process. These technical deficiencies severely restrict the price management capabilities and service quality improvement of drone service platforms. Therefore, the industry urgently needs a dynamic pricing method for drone operations that can perceive multi-dimensional environmental factors such as weather conditions, airspace control status, and terrain flight complexity in real time, systematically incorporate these factors into the pricing model, and ensure a transparent and traceable pricing process. This would achieve rationalization of drone service prices, proactive risk management, and compliant platform operations. Summary of the Invention

[0007] To address the technical problem that existing drone service pricing technologies have failed to construct a systematic dynamic pricing model that simultaneously covers market and environmental factors, this invention provides a multi-dimensional dynamic pricing method and system for drone operation services. This system can perceive changes in external environmental factors such as weather conditions, airspace control status, and terrain flight complexity in real time, thereby achieving an effective correspondence between price and actual operational risk costs.

[0008] This invention provides a multi-dimensional dynamic pricing method for drone operation services, comprising the following steps:

[0009] Receive a service order request sent by a user; the service order request includes the geographical coordinates of the service location, the service specification identifier, and the estimated operation time;

[0010] Based on the service order request, obtain the corresponding regional coefficient, specification coefficient, and meteorological coefficient respectively;

[0011] Obtain information on no-fly zones surrounding the service location, calculate the distance between the service location and the nearest no-fly zone boundary, and calculate the airspace complexity coefficient based on the distance between the service location and the nearest no-fly zone boundary;

[0012] Obtain digital elevation data and building density data of the service location, calculate the elevation standard deviation and building density index, and calculate the flight complexity coefficient based on the elevation standard deviation and building density index;

[0013] The final price is generated by multiplying the regional coefficient, the specification coefficient, the meteorological coefficient, the airspace complexity coefficient, and the flight complexity coefficient with a preset benchmark price.

[0014] All real-time values ​​of all coefficients, calculation basis, and original data sources are serialized into structured data and persistently stored together with the final quote.

[0015] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, a regional coefficient configuration table based on the national standard administrative division code is constructed; the geographical coordinates of the service location are reverse geocoded to obtain the national standard administrative division code corresponding to the service location; and the regional coefficient corresponding to the service location is obtained from the regional coefficient configuration table using the national standard administrative division code corresponding to the service location as an index.

[0016] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, the national standard administrative division code is accurate to the county level. If no regional coefficient configuration record that precisely matches the national standard administrative division code corresponding to the service location is found in the regional coefficient configuration table, the query will be performed at the next higher level of administrative division corresponding to the service location until a matching regional coefficient configuration record is found.

[0017] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, a specification coefficient configuration table based on drone operation parameters is constructed; the corresponding specification coefficient is queried from the specification coefficient configuration table using the service specification identifier as an index.

[0018] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, corresponding meteorological index data is obtained based on the expected operation time, and a safety assessment is performed on the meteorological index data according to a preset meteorological index grading rule; if the safety assessment indicates that the drone is not flyable, a quote is refused to be generated and a prompt message indicating that the meteorological conditions do not meet the safety standards is returned to the user; if the safety assessment indicates that the drone is flyable, a meteorological coefficient is calculated based on the meteorological index data.

[0019] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, the meteorological index data includes wind speed, precipitation, visibility, and thunderstorm warning level;

[0020] The step of conducting a security assessment of the meteorological indicator data according to preset meteorological indicator classification rules includes:

[0021] Set up independent meteorological indicator classification rules for each meteorological indicator;

[0022] According to the meteorological index classification rules corresponding to each meteorological index, a safety assessment is performed on each meteorological index data; if the safety assessment indicates that flight is not permitted, a quote is refused to be generated and a prompt message indicating that the meteorological conditions do not meet the safety standards is returned to the user; if the safety assessment indicates that flight is permitted, the corresponding meteorological sub-coefficient is calculated based on each meteorological index data.

[0023] The maximum value among all the meteorological sub-coefficients is taken as the meteorological coefficient.

[0024] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, the no-fly zone information includes airport airspace, military restricted areas, and government sensitive facility restricted areas designated by the Civil Aviation Administration; after obtaining the no-fly zone information, the distance between the service location and the nearest no-fly zone boundary is calculated, and the no-fly zone distance coefficient of the service location is obtained according to the preset no-fly zone distance classification rules;

[0025] The airspace complexity coefficient also includes a compliance cost coefficient superimposed based on the airspace type, which includes temporary restricted areas, low-altitude flight test areas, and urban core business areas; the airspace type of the service location is determined, and the final airspace complexity coefficient is calculated based on the preset compliance cost coefficient of the airspace type;

[0026] The final airspace complexity coefficient is obtained by multiplying the no-fly zone distance coefficient by the compliance cost coefficient.

[0027] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, the digital elevation data is a digital elevation model grid data within an appropriate radius centered on the service location; the elevation standard deviation is obtained by calculating the square root of the sum of squares of the deviations between the elevation values ​​of each point in the digital elevation model grid data and the average elevation value; and the terrain complexity coefficient is calculated using the elevation standard deviation.

[0028] The building density data is the building density index within an appropriate radius centered on the service location;

[0029] The flight complexity coefficient is the product of the terrain complexity coefficient and the building density index.

[0030] In a preferred embodiment of the multi-dimensional dynamic pricing method for drone operation services provided by the present invention, after generating the final quote, the method further includes:

[0031] Based on the operational configuration, a preset minimum price threshold and a maximum price threshold are obtained, and the final price is compared with these two boundary values. If the final price is lower than the minimum price threshold, the final price is automatically adjusted to the minimum price threshold; if the final price is higher than the maximum price threshold, the final price is automatically adjusted to the maximum price threshold.

[0032] This invention also provides a multi-dimensional dynamic pricing system for drone operation services, comprising:

[0033] The request receiving module is used to receive service order requests sent by users; the service order request includes the geographical coordinates of the service location, the service specification identifier, and the estimated operation time;

[0034] The regional coefficient calculation module is used to obtain the corresponding regional coefficient based on the geographical coordinates of the service location;

[0035] The specification coefficient calculation module is used to obtain the corresponding specification coefficient based on the service specification identifier;

[0036] The meteorological coefficient calculation module is used to obtain corresponding meteorological index data based on the expected operation time, conduct a safety assessment of the meteorological index data, and calculate the meteorological coefficient.

[0037] The airspace complexity calculation module is used to obtain information on no-fly zones around the service location and calculate the airspace complexity coefficient.

[0038] The flight complexity calculation module is used to acquire digital elevation data and building density data of the service location and calculate the flight complexity coefficient.

[0039] The quotation generation module is used to multiply all coefficients with the benchmark price to generate the final quotation;

[0040] The snapshot storage module is used to serialize all the real-time values ​​of all coefficients, the basis for calculation, and the original data sources into structured data and persist them to the final quote.

[0041] Compared with existing technologies, the multi-dimensional dynamic pricing method and system for drone operation services provided by this invention has the following beneficial effects:

[0042] 1. This invention constructs a five-dimensional product pricing model to achieve a dynamic correlation between the price of UAV operation services and multi-dimensional environmental factors. Compared with existing static pricing schemes, the pricing method of this invention can perceive changes in external environmental factors such as weather conditions, airspace control status, and terrain flight complexity in real time, and quantify these factors into corresponding coefficient values ​​and incorporate them into the pricing formula, thereby achieving an effective correspondence between price and actual operational risk costs.

[0043] 2. This invention embeds a safety boundary check mechanism directly into the pricing level. When weather conditions exceed the safety threshold or the service location is within the no-fly zone, the system automatically rejects the generation of a quote and returns a clear reason for rejection, thus blocking the generation of unsafe work orders from the source and effectively reducing the incidence of safety accidents.

[0044] 3. This invention uses a pricing snapshot structured evidence storage mechanism to persistently store the complete calculation process of each quote in structured data. The real-time values ​​of all coefficients, the basis for calculation, and the source of the original data can be traced and queried, meeting the compliance requirements of civil aviation regulations. At the same time, it provides customers with transparent quote details, enhancing customers' trust and acceptance of the pricing results.

[0045] 4. The multi-dimensional dynamic pricing method and system for drone operation services provided by this invention effectively solves the technical problems in the prior art, such as the disconnect between pricing and actual risk costs, and the lack of transparency and auditability in the pricing process. It realizes the rationalization of drone service prices, the pre-positioning of risk control, and the compliance of platform operation. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart of the multi-dimensional dynamic pricing method for drone operation services provided in this embodiment of the invention;

[0048] Figure 2 This is a structural block diagram of the multi-dimensional dynamic pricing system for drone operation services provided in this embodiment of the invention. Detailed Implementation

[0049] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0050] In embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as superior or more advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0051] Please see Figure 1 , Figure 1 This is a flowchart of a multi-dimensional dynamic pricing method for drone operation services provided in an embodiment of the present invention. The present invention provides a multi-dimensional dynamic pricing method for drone operation services, comprising the following steps:

[0052] S1. Receive service order requests sent by users; the service order request includes the geographical coordinates of the service location, service specification identifier, and estimated operation time;

[0053] S2. Obtain the corresponding regional coefficient, specification coefficient, and meteorological coefficient according to the service order request;

[0054] S3. Obtain information on no-fly zones around the service location, calculate the distance between the service location and the nearest no-fly zone boundary, and calculate the airspace complexity coefficient based on the distance between the service location and the nearest no-fly zone boundary.

[0055] S4. Obtain digital elevation data and building density data of the service location, calculate the elevation standard deviation and building density index, and calculate the flight complexity coefficient based on the elevation standard deviation and building density index.

[0056] S5. Multiply the regional coefficient, specification coefficient, meteorological coefficient, airspace complexity coefficient, and flight complexity coefficient with the preset benchmark price to generate the final price.

[0057] S6. Serialize all real-time values ​​of coefficients, calculation basis, and original data sources into structured data and persist them to the final quotation.

[0058] Please see Figure 2 , Figure 2 This is a structural block diagram of the multi-dimensional dynamic pricing system for drone operation services provided in an embodiment of the present invention. The present invention also provides a multi-dimensional dynamic pricing system for drone operation services, comprising:

[0059] The request receiving module is used to receive service order requests sent by users; the service order request includes the geographical coordinates of the service location, the service specification identifier, and the estimated operation time;

[0060] The regional coefficient calculation module is used to obtain the corresponding regional coefficient based on the geographical coordinates of the service location;

[0061] The specification coefficient calculation module is used to obtain the corresponding specification coefficient based on the service specification identifier;

[0062] The meteorological coefficient calculation module is used to obtain corresponding meteorological index data based on the expected operation time, conduct a safety assessment of the meteorological index data, and calculate the meteorological coefficient.

[0063] The airspace complexity calculation module is used to obtain information on no-fly zones around the service location and calculate the airspace complexity coefficient.

[0064] The flight complexity calculation module is used to acquire digital elevation data and building density data of the service location and calculate the flight complexity coefficient.

[0065] The quotation generation module is used to multiply all coefficients with the benchmark price to generate the final quotation;

[0066] The snapshot storage module is used to serialize all the real-time values ​​of all coefficients, the basis for calculation, and the original data sources into structured data and persist them to the final quote.

[0067] This invention employs eight functional modules: a request receiving module, a regional coefficient calculation module, a specification coefficient calculation module, a meteorological coefficient calculation module, an airspace complexity calculation module, a flight complexity calculation module, a price generation module, and a snapshot storage module. These modules communicate via message queues or remote procedure calls, achieving a decoupled design. The request receiving module provides a unified service access interface, supporting both HTTP and message queue access methods. The regional coefficient calculation, specification coefficient calculation, meteorological coefficient calculation, airspace complexity calculation, and flight complexity calculation modules each calculate their respective coefficients, utilizing parallel computing to improve processing efficiency. The price generation module, as the core computing node, aggregates the output results from each coefficient calculation module, performs the final product operation, and implements price constraints. The snapshot storage module uses asynchronous writing, simultaneously writing pricing snapshot data to the database and providing the price result to the user, ensuring that the user experience is unaffected by storage operations.

[0068] In one specific embodiment, the complete implementation process of this multi-dimensional dynamic pricing method for drone operation services is as follows: When a user initiates a service order request through a mobile terminal application or web page, the system first receives the service order request and parses its key parameters. The service order request includes at least three core fields: the geographic coordinates of the service location, the service specification identifier, and the estimated operation time. The geographic coordinates of the service location adopt the internationally recognized WGS84 coordinate system format, with longitude values ​​ranging from -180 to 180 degrees and latitude values ​​ranging from -90 to 90 degrees, and the coordinate accuracy requirement is no less than six decimal places. The service specification identifier adopts a predefined classification coding system; for example, the identifier for plant protection operations is AG-001, for aerial surveying and mapping services is SA-002, and for logistics and delivery services is LG-003. Different service specifications correspond to different base prices and operation requirements. The estimated operation time adopts the ISO8601 standard time format, accurate to the minute.

[0069] Upon receiving a service order request, the system enters the regional coefficient calculation phase. The regional coefficient calculation module first calls the reverse geocoding interface to convert the latitude and longitude coordinates of the service location into the national standard administrative division code. The reverse geocoding interface uses high-precision address resolution services provided by mainstream domestic geographic information service providers, with conversion accuracy required to reach the district / county level. That is, the returned administrative division code should be a six-digit code, where the first two digits represent the provincial-level administrative region, the middle two digits represent the prefecture-level administrative region, and the last two digits represent the county-level administrative region. For example, the administrative division code for Chaoyang District in Beijing is 110105, and the administrative division code for Pudong New Area in Shanghai is 310115.

[0070] After obtaining the region code, the system uses that region code as an index to query the region coefficient configuration table. The region coefficient configuration table is a structured configuration table stored in the database, containing an administrative division code field and a corresponding region coefficient field. The region coefficient reflects the differences in operating costs and market supply and demand in different regions, and its value range can be set between 0.8 and 2.0. In a preferred embodiment, some records in the region coefficient configuration table are as follows: the region coefficient for the main urban area of ​​Beijing is 1.5, the region coefficient for the main urban area of ​​Shanghai is 1.6, the region coefficient for the Pearl River Delta region of Guangdong Province is 1.4, and the region coefficient for remote areas of Xinjiang Uygur Autonomous Region is 0.9.

[0071] Furthermore, to enhance the system's fault tolerance and adaptability, this invention also includes a hierarchical fallback mechanism. This mechanism involves automatically falling back to the next higher administrative level when the system fails to return a matching record in the regional coefficient configuration table using a precise administrative division code. For example, a newly established economic and technological development zone may not yet have an independent configuration record in the regional coefficient configuration table. In this case, the system will automatically fall back to its corresponding administrative division. If a configuration record exists in that higher-level administrative division, the corresponding regional coefficient will be returned. If no matching record is found up to the provincial administrative level, the preset global default regional coefficient of 1.0 will be used. This hierarchical fallback mechanism ensures that the system can still operate normally when facing emerging regions or special administrative division adjustments, avoiding interruptions to the pricing process due to missing configurations.

[0072] After the regional coefficient calculation is completed, the system enters the specification coefficient calculation stage. The specification coefficient calculation module queries the specification coefficient configuration table using the service specification identifier as an index. The specification coefficient configuration table is also stored in the database and contains UAV operation parameter fields and corresponding specification coefficient fields. The specification coefficient reflects the cost structure differences of different types of UAV operation services, which are formed by a combination of factors such as equipment depreciation costs, operator labor costs, operation timeliness requirements, and operation efficiency. In a specific embodiment, the specification coefficient for plant protection operation services is 1.0, the specification coefficient for aerial surveying and mapping services is 1.3, the specification coefficient for logistics and distribution services is 1.5, and the specification coefficient for emergency rescue services is 2.0.

[0073] After obtaining the regional and specification coefficients, the system enters the meteorological coefficient calculation stage. The meteorological coefficient calculation module first calls the meteorological data interface to obtain meteorological indicator data corresponding to the expected operation time. The meteorological data interface connects to the data platform of the National Meteorological Administration or a third-party meteorological service provider, providing meteorological indicator data for specified latitude and longitude coordinates and a specified time range. The meteorological indicator data includes at least four core parameters: wind speed, precipitation, visibility, and thunderstorm warning level.

[0074] After acquiring meteorological data, the system conducts a safety assessment process. This process follows strict meteorological condition grading rules, with each meteorological indicator assessed independently. In a preferred embodiment, the wind speed assessment first determines if the wind speed exceeds the safe threshold of 10 m / s. If the wind speed exceeds this threshold, the system determines that the current weather conditions are unsuitable for flight, directly rejects the quotation generation, returns a message to the user indicating that the weather conditions do not meet safety standards, and terminates the current pricing process. If the wind speed is within the safe threshold range, the system determines the corresponding meteorological sub-coefficient based on the specific wind speed value: a sub-coefficient of 1.0 indicates good weather conditions with no additional risk; a sub-coefficient of 1.15 indicates a slight weather risk requiring increased operational caution; and a sub-coefficient of 1.35 indicates a significant weather risk requiring increased operational costs.

[0075] The safety assessment of precipitation indicators also follows a grading system. A meteorological sub-coefficient of 1.0 corresponds to 0 mm of precipitation; a sub-coefficient of 1.1 corresponds to 0.1-10 mm of precipitation, indicating slight precipitation that may affect operational efficiency; a sub-coefficient of 1.3 corresponds to 10-25 mm of precipitation, indicating a significant impact of precipitation on operations; and a sub-coefficient of 1.6 corresponds to more than 25 mm of precipitation, indicating severe precipitation conditions that would significantly increase operational costs.

[0076] The safety assessment grading rules for visibility indicators are as follows: When visibility is greater than 10 kilometers, the corresponding meteorological sub-coefficient is 1.0, indicating good visibility; when visibility is between 5 and 10 kilometers, the corresponding meteorological sub-coefficient is 1.1, indicating the presence of light fog or haze; when visibility is between 2 and 5 kilometers, the corresponding meteorological sub-coefficient is 1.4, indicating poor visibility requiring increased navigation difficulty; when visibility is less than 2 kilometers, the corresponding meteorological sub-coefficient is 1.8, indicating extremely poor visibility unsuitable for drone operations.

[0077] The safety assessment grading rules for thunderstorm warning level indicators adopt the thunderstorm warning signal standards issued by the meteorological department: when no thunderstorm warning is issued, the corresponding meteorological sub-coefficient is 1.0; when a yellow warning is issued, the corresponding meteorological sub-coefficient is 1.3; when an orange warning is issued, the corresponding meteorological sub-coefficient is 1.6; when a red warning is issued, the system directly determines that flight is not allowed and refuses to generate a quote.

[0078] After completing the tiered assessment of each meteorological indicator, the system takes the maximum value of all meteorological sub-coefficients as the final meteorological coefficient. The design logic of taking the maximum value is that the safety of drone operations depends on the most unfavorable environmental conditions, so the final meteorological coefficient should reflect the impact of the most unfavorable factors on the operating cost.

[0079] After the meteorological coefficient calculation is completed, the system proceeds to the airspace complexity coefficient calculation stage. The airspace complexity calculation module first queries no-fly zone information around the service location. No-fly zone information sources include airport airspace protection zone data published on the official website of the Civil Aviation Administration of China, military control zone information, and government-designated sensitive facility protection zone data. The system maintains a real-time updated no-fly zone database, which records the boundary coordinates and control requirements of various no-fly zones.

[0080] After obtaining no-fly zone information, the system calculates the distance between the service location and the nearest no-fly zone boundary. This distance calculation uses a point-to-polygon shortest distance algorithm. First, it determines whether the service location is within the no-fly zone. If it is, the system directly rejects the generation of a quote and returns a message indicating that the service location does not meet airspace control requirements. If the service location is outside the no-fly zone, it calculates the shortest horizontal distance from the service location to the no-fly zone boundary line. This shortest horizontal distance is the distance between the service location and the nearest no-fly zone boundary.

[0081] Based on the calculated distance between the service location and the nearest no-fly zone boundary, the system determines the no-fly zone distance coefficient of the service location according to the preset no-fly zone distance classification rules: when the distance between the service location and the nearest no-fly zone boundary exceeds 5,000 meters, the no-fly zone distance coefficient is 1.0, indicating good airspace conditions with no additional control restrictions; when the distance is between 1,000 and 5,000 meters, the no-fly zone distance coefficient is 1.20, indicating that entering the edge area of ​​the airport's airspace protection zone requires additional pre-flight declaration and flight monitoring costs; when the distance is between 500 and 1,000 meters, the no-fly zone distance coefficient is 1.50, indicating that being in a highly sensitive airspace control area requires additional compliance costs; when the distance is less than 500 meters, the system refuses to generate a quote, indicating that the service location does not meet the drone flight safety requirements.

[0082] Furthermore, this invention also includes a compliance cost coefficient superimposed based on airspace type. Airspace types are divided into three categories: temporary restricted areas, low-altitude flight test areas, and urban core business districts. After determining the airspace type of the service location, an additional compliance cost coefficient is superimposed on the no-fly zone distance coefficient. Temporary restricted areas refer to air traffic control areas established for temporary reasons such as major event support or emergency rescue. Operations within this type of airspace require additional costs for temporary airspace application and approval, with a compliance cost coefficient of 1.3. Low-altitude flight test areas refer to approved low-altitude flight test bases. Operations within this type of airspace have relatively mature supporting conditions, with a compliance cost coefficient of 0.9. Urban core business districts refer to densely populated and densely built-up urban central business areas. Operations within this type of airspace require more stringent flight safety measures, with a superimposed compliance cost coefficient of 1.1. The final airspace complexity coefficient is obtained by multiplying the no-fly zone distance coefficient by the compliance cost coefficient.

[0083] After the airspace complexity coefficient is calculated, the system proceeds to the flight complexity coefficient calculation stage. The flight complexity calculation module acquires digital elevation data and building density data for the service location. The digital elevation data is selected from DEM (Digital Elevation Model) data acquired by aerospace remote sensing satellites, with a resolution of 30 meters. The system extracts DEM grid data with a radius of 2 kilometers centered on the service location, forming a dataset containing several elevation points.

[0084] After acquiring the digital elevation model (DEM) grid data, the system calculates the elevation standard deviation. The elevation standard deviation is obtained by calculating the square root of the sum of squared deviations of the elevation values ​​of each point in the DEM grid data from the average elevation value. The elevation standard deviation reflects the degree of terrain undulation at the service location; a larger standard deviation indicates more complex terrain. Based on the elevation standard deviation value, the system applies a preset terrain complexity coefficient classification rule: when the elevation standard deviation is less than 20 meters, the terrain complexity coefficient is 1.0, indicating flat terrain; when the elevation standard deviation is between 20 and 50 meters, the terrain complexity coefficient is 1.1, indicating some terrain undulation; when the elevation standard deviation is greater than 50 meters, the terrain complexity coefficient is 1.25, indicating that the complex terrain has a significant impact on flight safety.

[0085] Building density data is obtained by extracting vector data of buildings around the service location from a geographic information system. The system counts the number of buildings within a 500-meter radius of the service location and calculates the building density index, expressed as the number of buildings per square kilometer. Based on the building density index, the system applies preset building density index classification rules: a density index of less than 100 buildings / square kilometer is 1.0, indicating sparse buildings; a density index between 100 and 200 buildings / square kilometer is 1.08, indicating moderate building density; and a density index greater than 200 buildings / square kilometer is 1.18, indicating dense buildings and a high risk to UAV flight safety.

[0086] Ultimately, the flight complexity coefficient is obtained by multiplying the terrain complexity coefficient by the building density index. This product calculation method reflects the combined effect of terrain and building factors on flight complexity; a higher complexity in either factor will significantly increase the overall flight risk cost.

[0087] After calculating all coefficients, the price generation module performs the final price generation calculation. The system pre-queries the benchmark price corresponding to the service specification from the database. The benchmark price is the basic value of the pricing model, reflecting the basic cost under standard operating conditions. Taking plant protection services as an example, the benchmark price can be set at 20 RMB per acre.

[0088] The final price is calculated using the following formula: Final Price = Benchmark Price · Regional Coefficient · Specification Coefficient · Weather Coefficient · Airspace Complexity Coefficient · Flight Complexity Coefficient. This product pricing model implements a linkage adjustment of the final price based on five dimensions of coefficients; when any one of these coefficients changes, the final price will be adjusted accordingly.

[0089] Furthermore, the price generation module also includes a price upper and lower limit constraint mechanism. The system obtains preset minimum and maximum price thresholds based on operational configurations and compares the final price with these two boundary values. If the final price is lower than the minimum price threshold, the system automatically adjusts it to the minimum price threshold; if the final price is higher than the maximum price threshold, the system automatically adjusts it to the maximum price threshold. This price upper and lower limit constraint mechanism effectively prevents abnormal prices from arising from extreme coefficient combinations, ensuring the rationality and stability of the platform's pricing.

[0090] Finally, the snapshot storage module persistently stores complete information about the pricing process. Snapshot data includes real-time values ​​and calculation basis of the regional coefficient, real-time values ​​and query sources of the specification coefficient, real-time values ​​and raw meteorological data of the meteorological coefficient, real-time values ​​of the airspace complexity coefficient and calculation results of no-fly zone distances, real-time values ​​of the flight complexity coefficient and terrain and building density data, the final price, and the generation timestamp. All this data is serialized into structured data in JSON format and written to the pricing snapshot table in the database, establishing a relationship with the corresponding order number. This snapshot storage mechanism ensures the post-event traceability and auditability of pricing decisions.

[0091] In one embodiment, the present invention further includes pricing optimization processing for bulk orders. This pricing optimization processing for bulk orders includes: when a user submits multiple service order requests at once, performing data deduplication and cluster analysis on the multiple service order requests based on the geographical coordinates of the service locations; merging service order requests with similar geographical coordinates; then calculating all coefficients for each merged service order request in parallel; and finally generating the final price for each merged service order request. This pricing optimization processing for bulk orders effectively improves order processing efficiency.

[0092] In one embodiment, at the system architecture level, this invention adopts a configuration-driven parameter management architecture. All threshold ranges, premium increments, and weights involved in coefficient calculations are managed through database configuration tables, supporting hot updates in the background. This design allows operators to flexibly adjust pricing parameters without modifying the code, shortening the response cycle from days in existing solutions to minutes, effectively improving the platform's operational efficiency and price management capabilities. Furthermore, this invention supports operations teams in adjusting parameters based on region and service type, enabling real-time responses to market changes and regulatory policy adjustments without engineering deployments, significantly enhancing the flexibility of the pricing system. Specifically, the system's configuration-driven architecture supports three levels of configuration parameter management: a global default configuration applicable to all regions and all service specifications, serving as the system's baseline parameter set; a region-coverage configuration allowing specific administrative divisions to cover default parameter values, achieving differentiated pricing by region; and a service type-coverage configuration allowing the setting of differentiated parameters for specific job types to meet the cost characteristics of different service specifications. Configuration parameters support hot updates in the background; after operators modify configuration parameters in the management backend, the system takes effect within minutes, without requiring service restarts or application redeployment. Furthermore, the priority of the regional coverage configuration is higher than that of the global default configuration, and the priority of the service type coverage configuration is higher than that of the regional coverage configuration and the global default configuration. When multiple configurations have different values ​​for the same parameter, the parameter value configured with the highest priority is adopted.

[0093] In one embodiment, the present invention further includes a confidence assessment of meteorological indicator data. This confidence assessment includes: when the estimated operation time submitted by the user is more than 72 hours from the current time, the system simultaneously acquires meteorological indicator data for the estimated operation time and historical meteorological statistics for the same period at the service location, and assesses the confidence of the meteorological indicator data for the estimated operation time using the historical meteorological statistics for the same period at the service location; if the confidence of the meteorological indicator data is low, the weighted average of the meteorological indicator data and the historical statistics is used as the basis for calculating the meteorological coefficient, wherein the weight of the meteorological indicator data can be configured to 0.6, and the weight of the historical meteorological statistics can be configured to 0.4.

[0094] In one embodiment, the present invention further includes encrypting the data in the snapshot storage module. This encryption protection includes: the snapshot storage module encrypts the geographic location coordinates and meteorological index data using an encryption algorithm (such as AES256); the system has a dedicated security module for unified management of the keys; and the system strictly controls access permissions to the snapshot storage module. Only administrators with auditing privileges and key access can query historical pricing records; ordinary users are not authorized to access the data in the snapshot storage module, thus improving data security.

[0095] In one embodiment, the present invention further includes a data degradation strategy. This data degradation strategy includes: when the meteorological data interface is unavailable, the system automatically switches to a backup meteorological data source to obtain meteorological indicator data corresponding to the expected operation time; when the backup meteorological data source is also unavailable, the system uses historical average meteorological data as the meteorological indicator data corresponding to the expected operation time to calculate the meteorological coefficient, and marks the data source in the pricing result; when no-fly zone information data is not updated in a timely manner, the system adopts a conservative strategy to calculate the airspace complexity coefficient based on the most recently updated no-fly zone information data, ensuring the security of the pricing result.

[0096] In one embodiment, the present invention further includes caching meteorological data. This caching includes: in high-concurrency scenarios, the system subscribes to a meteorological push service by region, caches the meteorological data sent by the service in Redis (TTL = 15 minutes), and directly reads the cached meteorological data value from Redis as the meteorological indicator data corresponding to the estimated operation time during pricing. This solution triggers the meteorological data interface call only when the meteorological data cache expires, which can significantly reduce the frequency and latency of meteorological data interface calls while ensuring data timeliness, avoiding the performance bottleneck problem that may be caused by real-time calls to the meteorological data interface for each dynamic pricing in high-concurrency scenarios.

[0097] The following example illustrates the pricing strategy. Suppose a user initiates a plant protection service order at a location in Pudong New Area, Shanghai, with coordinates 121.6348° longitude and 31.2246° latitude. The estimated operation time is 2 PM the following day. The system will calculate the price according to the following process:

[0098] First, the regional coefficient calculation module calls the reverse geocoding interface to convert the coordinates into the administrative division code 310115, which corresponds to Pudong New Area, Shanghai. The regional coefficient is 1.6, obtained by querying the regional coefficient configuration table.

[0099] Subsequently, the specification coefficient calculation module queries the specification coefficient configuration table based on the service specification identifier AG-001 to obtain a specification coefficient of 1.0.

[0100] The meteorological coefficient calculation module calls the meteorological data interface to obtain the meteorological data corresponding to the operation time: wind speed 6 m / s, precipitation 2 mm, visibility 12 km, no thunderstorm warning. According to the meteorological index classification rules, the wind speed sub-coefficient is 1.15, the precipitation sub-coefficient is 1.1, the visibility sub-coefficient is 1.0, and the thunderstorm sub-coefficient is 1.0. The maximum value of 1.15 is taken as the meteorological coefficient.

[0101] The airspace complexity calculation module queries no-fly zone information and calculates that the service location is approximately 3,500 meters from the nearest airport's airspace boundary, corresponding to a no-fly zone distance coefficient of 1.20. Since the service location is located on the edge of a city's core business district, a compliance cost coefficient of 1.1 is added, resulting in a final airspace complexity coefficient of 1.32 (no-fly zone distance coefficient · compliance cost coefficient).

[0102] The flight complexity calculation module acquires digital elevation model data with a radius of 2 kilometers, calculates the elevation standard deviation as 8 meters, and corresponds to a terrain complexity coefficient of 1.1; it acquires building density data with a radius of 500 meters, with a density index of 320 buildings / square kilometer, corresponding to a building density index of 1.18; the final flight complexity coefficient is 1.298 (terrain complexity coefficient · building density index).

[0103] After obtaining the base price of 20 yuan for plant protection services, the final quote is calculated as 20 multiplied by 1.6 multiplied by 1.0 multiplied by 1.15 multiplied by 1.32 multiplied by 1.298, resulting in approximately 63.05 yuan per mu. The system checks that this quote is within the preset upper and lower price limits and outputs it directly as the final quote.

[0104] Table 1 and Table 1 (continued) show the detailed values ​​and calculation process of each coefficient in this embodiment:

[0105] Table 1. Detailed values ​​and calculation process of each coefficient in the embodiment.

[0106]

[0107] Table 1 (Continued) shows the detailed values ​​and calculation process of each coefficient in the examples.

[0108]

[0109] The comparative scenario is set as follows: The multi-dimensional dynamic pricing method described in this invention is compared and analyzed with the static pricing method in the prior art. The static pricing method refers to charging a fixed unit price regardless of changes in weather conditions, airspace status, and terrain complexity at the service location. Assuming that under the same service location and operating time conditions, the unit price under the static pricing method is 25 RMB per mu (approximately 0.16 acres) of operating area.

[0110] In the above-described operational scenario, the final price calculated by the method of this invention is RMB 63.05 per mu, which is significantly different from the static pricing method. This difference mainly stems from the following aspects: First, the service location is in Pudong New Area, Shanghai, an economically developed region with high operational costs and market premiums; the regional coefficient of 1.6 directly leads to a 60% price increase. Second, the weather conditions corresponding to the operational time are a wind speed of 6 m / s, which, although within the flight-safe range, presents a slight risk; the weather coefficient of 1.15 leads to a 15% price increase. Third, the service location is 3500 meters from the airport's airspace boundary and located in the city's core business district; the airspace complexity coefficient of 1.32 leads to a 32% price increase. Fourth, the building density in the operational area reaches 320 buildings per square kilometer, a relatively high density area; the flight complexity coefficient of 1.298 leads to a 29.8% price increase. Considering the adjusting effects of various coefficients, the final price obtained by this invention more accurately reflects the actual operational risk costs and safety management costs.

[0111] From a platform operation perspective, the pricing results of this invention offer greater transparency and interpretability. The value of each coefficient can be traced back to a clear calculation basis and data source, allowing users to clearly understand the price composition when accepting a quote. When weather conditions worsen or airspace control is tightened, the system automatically adjusts the quote to match the price with risk costs, enabling dynamic pricing without manual intervention from the operator.

[0112] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A multi-dimensional dynamic pricing method for drone operation services, characterized in that, Includes the following steps: Receive a service order request sent by a user; the service order request includes the geographical coordinates of the service location, the service specification identifier, and the estimated operation time; Based on the service order request, obtain the corresponding regional coefficient, specification coefficient, and meteorological coefficient respectively; Obtain information on no-fly zones surrounding the service location, calculate the distance between the service location and the nearest no-fly zone boundary, and calculate the airspace complexity coefficient based on the distance between the service location and the nearest no-fly zone boundary; Obtain digital elevation data and building density data of the service location, calculate the elevation standard deviation and building density index, and calculate the flight complexity coefficient based on the elevation standard deviation and building density index; The final price is generated by multiplying the regional coefficient, the specification coefficient, the meteorological coefficient, the airspace complexity coefficient, and the flight complexity coefficient with a preset benchmark price. All real-time values ​​of all coefficients, calculation basis, and original data sources are serialized into structured data and persistently stored together with the final quote.

2. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, Construct a regional coefficient configuration table based on the national standard administrative division code; perform reverse geocoding on the geographic coordinates of the service location to obtain the national standard administrative division code corresponding to the service location; Using the national standard administrative division code corresponding to the service location as an index, the regional coefficient corresponding to the service location is obtained from the regional coefficient configuration table.

3. The multi-dimensional dynamic pricing method for drone operation services according to claim 2, characterized in that, The national standard administrative division code is accurate to the district / county level. If no regional coefficient configuration record that precisely matches the national standard administrative division code corresponding to the service location is found in the regional coefficient configuration table, the query will be performed at the next higher level of administrative division corresponding to the service location until a matching regional coefficient configuration record is found.

4. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, Construct a specification coefficient configuration table based on UAV operation parameters; use the service specification identifier as an index to query the corresponding specification coefficient from the specification coefficient configuration table.

5. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, Based on the estimated operation time, obtain the corresponding meteorological index data, and perform a safety assessment on the meteorological index data according to the preset meteorological index classification rules; if the safety assessment indicates that the flight is not permitted, refuse to generate a quote and return a prompt message to the user that the meteorological conditions do not meet the safety standards; if the safety assessment indicates that the flight is permitted, calculate the meteorological coefficient based on the meteorological index data.

6. The multi-dimensional dynamic pricing method for drone operation services according to claim 5, characterized in that, The meteorological data include wind speed, precipitation, visibility, and thunderstorm warning level; The step of conducting a security assessment of the meteorological indicator data according to preset meteorological indicator classification rules includes: Set up independent meteorological indicator classification rules for each meteorological indicator; According to the meteorological index classification rules corresponding to each meteorological index, a safety assessment is performed on each meteorological index data; if the safety assessment indicates that flight is not permitted, a quote is refused to be generated and a prompt message indicating that the meteorological conditions do not meet the safety standards is returned to the user; if the safety assessment indicates that flight is permitted, the corresponding meteorological sub-coefficient is calculated based on each meteorological index data. The maximum value among all the meteorological sub-coefficients is taken as the meteorological coefficient.

7. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, The no-fly zone information includes airport airspace, military restricted areas, and government sensitive facility restricted areas designated by the Civil Aviation Administration; after obtaining the no-fly zone information, the distance between the service location and the nearest no-fly zone boundary is calculated, and the no-fly zone distance coefficient of the service location is obtained according to the preset no-fly zone distance classification rules; The airspace complexity coefficient also includes a compliance cost coefficient superimposed based on the airspace type, which includes temporary restricted areas, low-altitude flight test areas, and urban core business areas; the airspace type of the service location is determined, and the final airspace complexity coefficient is calculated based on the preset compliance cost coefficient of the airspace type; The final airspace complexity coefficient is obtained by multiplying the no-fly zone distance coefficient by the compliance cost coefficient.

8. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, The digital elevation data is a digital elevation model grid data within an appropriate radius centered on the service location; the elevation standard deviation is obtained by calculating the square root of the sum of squares of the deviations between the elevation values ​​of each point in the digital elevation model grid data and the average elevation value; the terrain complexity coefficient is calculated using the elevation standard deviation. The building density data is the building density index within an appropriate radius centered on the service location; The flight complexity coefficient is the product of the terrain complexity coefficient and the building density index.

9. The multi-dimensional dynamic pricing method for drone operation services according to claim 1, characterized in that, After generating the final quote, the process also includes: Based on the operational configuration, a preset minimum price threshold and a maximum price threshold are obtained, and the final price is compared with these two boundary values. If the final price is lower than the minimum price threshold, the final price is automatically adjusted to the minimum price threshold; if the final price is higher than the maximum price threshold, the final price is automatically adjusted to the maximum price threshold.

10. A multi-dimensional dynamic pricing system for drone operation services, characterized in that, include: The request receiving module is used to receive service order requests sent by users. The service order request includes the geographical coordinates of the service location, the service specification identifier, and the estimated operation time; The regional coefficient calculation module is used to obtain the corresponding regional coefficient based on the geographical coordinates of the service location; The specification coefficient calculation module is used to obtain the corresponding specification coefficient based on the service specification identifier; The meteorological coefficient calculation module is used to obtain corresponding meteorological index data based on the expected operation time, conduct a safety assessment of the meteorological index data, and calculate the meteorological coefficient. The airspace complexity calculation module is used to obtain information on no-fly zones around the service location and calculate the airspace complexity coefficient. The flight complexity calculation module is used to acquire digital elevation data and building density data of the service location and calculate the flight complexity coefficient. The quotation generation module is used to multiply all coefficients with the benchmark price to generate the final quotation; The snapshot storage module is used to serialize all the real-time values ​​of all coefficients, the basis for calculation, and the original data sources into structured data and persist them to the final quote.

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