A low-altitude unmanned aerial vehicle operation safety risk quantitative evaluation method and system

Abstract

The application provides a kind of low altitude unmanned aerial vehicle operation safety risk quantitative evaluation method and system, it is related to unmanned aerial vehicle safety evaluation technical field, the method includes: collection operation basic data, including unmanned aerial vehicle model parameter according to model parameter and flight task parameter, determine the compliance of operation subject, obtain compliance determination result;According to compliance determination result, in combination with airspace environment data and meteorological data, assess airspace level and meteorological flyability, obtain airspace risk level;According to airspace risk level, in combination with communication and identification ability parameter, energy system parameter and emergency landing ability parameter, obtain technical dimension risk score.The application achieves the objective and efficient technical effect of low altitude unmanned aerial vehicle operation safety risk quantification through full-chain automatic evaluation mechanism.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) safety assessment technology, and in particular to a method and system for quantitatively assessing the safety risks of low-altitude UAV operations. Background Technology

[0002] With the rapid development of the civilian unmanned aerial vehicle industry, its application in logistics distribution, power line inspection, surveying and aerial photography and other fields is becoming increasingly widespread. In order to ensure the safety of low-altitude flight, regulatory agencies in various countries have successively introduced strict operation and management regulations. Flight activities in controlled airspace must submit detailed flight plan applications to the airspace authorities and civil aviation management departments, and can only be carried out after strict approval.

[0003] The preparation of flight application documents and risk assessments rely heavily on human experience. For example, a logistics company plans to use a small drone with a maximum takeoff weight of 25 kg to perform a 15-kilometer medical supply delivery mission in a city in Guangdong Province. This route requires crossing a controlled airspace at an altitude of approximately 150 meters and traversing an urban expressway and an old residential area. According to current regulations and operating manuals, applicants need to meticulously check multiple dimensions, including aircraft technical parameters, communication links, weather conditions, emergency landing points, and ground population density. Applicants must also prepare flight plans and risk assessment reports based on a series of scattered qualitative checklists and subjective experience, and submit them to the airspace management department and the civil aviation operation management platform. This over-reliance on the applicant's individual professional expertise means that different assessors may arrive at vastly different risk conclusions for the same flight mission, leading to highly subjective and inconsistent assessment results. This is particularly problematic for flights crossing old residential areas. In the scenario of old residential areas, one assessor may subjectively perceive a high risk, while another may underestimate the risk due to a lack of historical population density data for the area. This directly affects the efficiency and accuracy of airspace authorities and civil aviation approval agencies in judging the overall safety of a mission. Scattered inspection items cannot reflect the coupling and weighting relationships between various risk factors. A mission with a barely adequate aircraft redundancy ratio but a very complete emergency landing system may have significantly different overall safety risks compared to a mission with a high redundancy ratio but poor emergency landing capabilities. However, existing qualitative assessment methods are unable to accurately distinguish the risk levels of the two, making it impossible for approval agencies to conduct refined and quantitative risk ranking and decision-making for applications. When multiple applications are submitted simultaneously, air traffic control departments lack a unified risk value as a priority ranking basis and can only rely on manual review one by one, resulting in low approval efficiency and the possibility of high-risk missions being misjudged as low-risk due to better-packaged materials.

[0004] In existing technologies, some studies have attempted to quantify the operational risks of unmanned aerial vehicles (UAVs) using the Analytic Hierarchy Process (AHP) or fuzzy comprehensive evaluation methods. However, these methods have the following limitations: First, the assessment dimensions are singular, considering only one aspect of aircraft parameters or ground risks, lacking a comprehensive integration across the entire chain from compliance determination, airspace assessment, and technical capabilities to ground risks. Second, the ground risk modeling is coarse, failing to accurately characterize the impact of multi-level buffer zones on the exposure of sensitive targets. Third, the coverage of failure scenarios is insufficient, lacking a systematic deduction and quantitative ranking mechanism for multiple typical failure scenarios. Therefore, there is an urgent need for a fully automated, multi-dimensional, and finely quantifiable method for assessing the operational safety risks of low-altitude UAVs. Summary of the Invention

[0005] This invention provides a method and system for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations. By collecting multi-source data and performing compliance judgment, airspace meteorological assessment, technical dimension quantification, polygon nested hierarchical analysis, and fault inference at each level, a fully automated assessment mechanism is formed, achieving objective, precise, and efficient technical results in quantifying the safety risks of low-altitude UAV operations.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations, the method comprising: Step 1: Collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. Step 2: Based on the aircraft type parameters and flight mission parameters, determine the compliance of the operating entity and obtain the compliance determination result; Step 3: Based on the compliance assessment results, combined with airspace environmental data and meteorological data, assess the airspace level and weather flyability to obtain the airspace risk level. Step 4: Based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters, obtain the technical dimension risk score; Step 5: Based on the risk score of the technical dimension, perform polygon nested hierarchical analysis on the distribution data of sensitive targets, extract the area ratio and overlap depth of each layer, calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. Step 6: Based on the comprehensive ground risk level, simulate five preset failure scenarios in sequence: communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure. Calculate the occurrence frequency and impact level of each scenario to obtain the quantitative value of operational safety risk. Match mitigation measures according to the quantitative value of operational safety risk, generate a risk control plan, and complete the assessment of the quantitative safety risk.

[0007] Secondly, the low-altitude unmanned aerial vehicle (UAV) operation safety risk quantitative assessment system includes: The data acquisition module is used to collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. The judgment module is used to determine the compliance of the operating entity based on aircraft type parameters and flight mission parameters, and obtain the compliance judgment result; based on the compliance judgment result, combined with airspace environment data and meteorological data, the airspace level and weather flyability are assessed to obtain the airspace risk level; The scoring module is used to obtain a technical risk score based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters. The analysis module is used to perform polygon nested hierarchical analysis on the distribution data of sensitive targets based on the risk score of the technical dimension, extract the area ratio and overlap depth of each layer and calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. The processing module is used to sequentially deduce five preset failure scenarios—communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure—based on the comprehensive ground risk level, calculate the occurrence frequency and impact level of each type, and obtain the quantitative value of operational safety risk; match mitigation measures according to the quantitative value of operational safety risk, generate risk control plan, and complete the assessment of the quantitative safety risk.

[0008] The above-described solution of the present invention has at least the following beneficial effects: This invention employs multi-source data acquisition and automatic compliance assessment, overcoming the technical problems of strong subjectivity and inconsistent verification standards in manual assessment, achieving objective and consistent compliance assessment and comprehensive and traceable data acquisition. By adopting a quantitative mechanism that progressively transmits risk scores based on airspace risk levels and technical dimensions, it overcomes the technical problems of isolated analysis of risk factors and lack of coupled weights, thus achieving progressive risk quantification and reproducible and comparable scoring. By using polygonal nested hierarchical analysis to geometrically superimpose risks on the distribution of sensitive targets and correct ground weights, it overcomes the technical problems of qualitative description of ground risks and the inability to quantify the impact of multi-level buffer zones, achieving refined ground risk modeling and accurate quantification of exposure levels. By employing five types of fault scenario simulations and risk matrix fusion with automatic matching of mitigation measures, it overcomes the technical problems of unsystematic emergency analysis and lack of unified sorting criteria for approval, achieving full coverage of fault scenarios and improved approval efficiency and security. Furthermore, it forms a fully automated assessment system, realizing objective and efficient quantification of safety risks in low-altitude UAV operations.

[0009] This invention employs the Cuckoo Search algorithm to optimize risk level matching in mixed airspace scenarios, overcoming the technical problem of insufficient accuracy of the single lookup table method when multiple types of airspace overlap, and achieving global optimal matching of airspace risk levels. It also employs the Rotating Caliper Algorithm to accurately calculate the actual length of curved routes, overcoming the technical problem of deviation in alternate landing point density caused by straight-line distance estimation, and improving the accuracy of risk scoring in technical dimensions. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles (UAVs) according to an embodiment of the present invention.

[0011] Figure 2 This is a schematic diagram of a low-altitude unmanned aerial vehicle (UAV) operation safety risk quantification assessment system provided by an embodiment of the present invention. Detailed Implementation

[0012] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0013] like Figure 1 As shown in the figure, an embodiment of the present invention proposes a method for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations. The method includes the following steps: Step 1: Collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. Step 2: Based on the aircraft type parameters and flight mission parameters, determine the compliance of the operating entity and obtain the compliance determination result; Step 3: Based on the compliance assessment results, combined with airspace environmental data and meteorological data, assess the airspace level and weather flyability to obtain the airspace risk level. Step 4: Based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters, obtain the technical dimension risk score; Step 5: Based on the risk score of the technical dimension, perform polygon nested hierarchical analysis on the distribution data of sensitive targets, extract the area ratio and overlap depth of each layer, calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. Step 6: Based on the comprehensive ground risk level, simulate five preset failure scenarios in sequence: communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure. Calculate the occurrence frequency and impact level of each scenario to obtain the quantitative value of operational safety risk. Match mitigation measures according to the quantitative value of operational safety risk, generate a risk control plan, and complete the assessment of the quantitative safety risk.

[0014] In a preferred embodiment of the present invention, step 1 above includes: Collect flight area coordinates from flight mission parameters to obtain airspace environment data, specifically including: The data acquisition module is activated to read the pre-defined coordinate information of the flight area from the flight mission parameters. This coordinate information is in latitude and longitude coordinate format and includes the coordinates of the flight mission's starting point, ending point, key nodes along the route, and the boundary coordinates of the flight area, forming a complete set of flight area coordinates. This coordinate set is then imported into the airspace information query system. Through the system's built-in airspace database, the airspace range corresponding to the coordinate set is matched, and the corresponding airspace management category is queried, including controlled airspace, monitored airspace, reporting airspace, and visual flight airspace. Simultaneously, information such as the airspace's boundary range, altitude restrictions, usage rights, and access conditions is obtained. This information is then integrated to form complete airspace environment data.

[0015] Based on the airspace category in the airspace environment data, the meteorological collection range is determined. Wind speed, visibility, and precipitation data for the flight area are obtained from the meteorological service interface to acquire meteorological data. Specifically, after acquiring the airspace environment data, the corresponding meteorological collection range is determined according to the airspace category specified in the airspace environment data. The meteorological collection range is centered on the flight area coordinate set and extends outwards by a preset distance. The extension distance is adjusted according to different airspace categories. Specifically, the meteorological collection range extension distance for controlled airspace is no less than 5 kilometers, and the meteorological collection range extension distance for monitored airspace, reporting airspace, and visual flight airspace is no less than 3 kilometers. The above extension distance parameters are based on the "Rules for the Safety Management of Civil Unmanned Aerial Vehicle Operations" (CCAR-92) and related regulations. Based on comprehensive practical experience in human-machine meteorological support engineering, and verified through multi-scenario flight experiments, the system ensures that the collected meteorological data can fully cover the entire flight area and surrounding areas affecting flight safety. After determining the meteorological collection range, a meteorological data acquisition request is sent to the meteorological data service platform by calling an authoritative meteorological service interface. The request carries the latitude and longitude boundary information of the meteorological collection range, the collection time range, and the required meteorological parameter types. According to the request parameters, the meteorological service platform returns real-time meteorological data and forecast meteorological data within the corresponding collection range and time range. The core meteorological data includes wind speed, visibility, and precipitation data. These data are filtered, deduplicated, and verified. After removing abnormal data, complete meteorological data is formed.

[0016] Based on the flight time and wind resistance rating in the aircraft model parameters and the wind speed in the meteorological data, the range energy redundancy ratio is calculated. The communication link configuration and battery capacity are read to obtain communication and identification capability parameters and energy system parameters. Specifically, this includes: based on the previously completed flight area coordinate acquisition, airspace environment data acquisition, and meteorological data acquisition, combining the collected UAV model parameters and acquired meteorological data, and accurately extracting the nominal range and wind resistance rating from the verified UAV model parameters, and acquiring meteorological data to extract the average wind speed and instantaneous maximum wind speed of the flight area. After completing the extraction of the above core parameters, the specific calculation process of the range energy redundancy ratio begins, as follows: Calculate the required mission range using the formula: ;in, Indicates the required flight distance for the mission. Indicates the straight-line distance of the flight path. This represents the route correction factor, with values ​​determined based on the route type: short city routes. Regular cross-regional routes Cross-sea / airport detour / complex airspace The formula for calculating available range is: ; in, Indicates available range. This indicates the nominal range of the drone. This represents the reduction factor, which is determined based on the flight conditions as "good conditions". Conventional engineering is conservative Crossing the sea / strong winds / heavy load The formula for calculating the range energy redundancy ratio is as follows: ; in, This represents the energy redundancy ratio of the flight range, which is generally required for most routes. ≥1.3, long-haul sea routes must meet this requirement. ≥1.5; if the calculation result >1.5 or If the value is greater than 1.3, then the upper limit of the corresponding standard will be used. If < 0, then take =0. While calculating the range-to-energy redundancy ratio, the UAV's communication link configuration information is read, including the type, transmission rate, and communication distance of the primary communication link; the number, type, and switching response time of backup communication links; and the type, range, and accuracy of the identification system. This information is then integrated to form complete communication and identification capability parameters. Relevant parameters of the UAV's energy system are also read, including battery capacity, nominal battery voltage, remaining battery power, charging efficiency, and energy consumption rate. The calculation process for the actual usable battery power follows the original standard formula: Calculate the actual usable battery power using the formula... ;in, Indicates the actual usable battery capacity. Indicates battery capacity, Indicates the battery's nominal voltage. This indicates the remaining battery capacity; for land-based missions, the battery reserve must be ≥30%, and for sea-based missions, it must be ≥50%. These redundancy ratio minimums and battery reserve requirements are determined comprehensively based on the Civil Aviation Administration of China's "Safety Requirements for Civil Unmanned Aerial Vehicle Systems" and statistical data from UAV industry engineering practices, i.e., calculated. The corresponding reserve requirements must be met; otherwise, the energy system parameters are deemed to be substandard. These parameters, calculation results, and reserve verification results are then compiled to form complete energy system parameters.

[0017] Based on the flight route coordinate range in the flight mission parameters, population density, transportation network, and sensitive facility distribution data are extracted from the preset buffer zones on both sides of the flight route. This yields population density distribution data, transportation infrastructure data, and sensitive target distribution data. Specifically, this involves: extracting the coordinate range of the flight route from the flight mission parameters, including the latitude and longitude coordinates of the route's start, end, and intermediate nodes, forming a complete flight route coordinate sequence; setting buffer zones on both sides of the flight route, with the buffer zone width determined based on the flight mission altitude and UAV type (500 meters for small UAVs, 1000 meters for medium and larger UAVs); and symmetrically expanding the buffer zone width to both sides of the flight route coordinate sequence as the center line, forming a complete flight route buffer zone latitude and longitude boundary. After determining the flight route buffer zone range, a geographic information database interface is called to extract the population density distribution data within the buffer zone. Specifically, the flight route buffer zone boundary range is imported into the geographic information database, and basic population density data for different areas within the buffer zone is directly queried. Accurate conversion is then performed based on the buffer zone area. The population density calculation process is as follows: The formula for calculating the regional population density is... ;in, Indicates the population density of the area. This represents the total population of the area within the buffer zone. This section represents the area of ​​the region; it integrates population density data from various areas within the buffer zone to clarify the population density of different areas, forming population density distribution data below the flight path; it extracts transportation infrastructure data within the flight path buffer zone by querying transportation network information within the buffer zone through a geographic information database, including road type, road width, number of lanes, traffic flow, and the location, scale, and usage status of transportation infrastructure such as bridges, tunnels, and stations; it categorizes and organizes this information according to transportation facility type to form complete transportation infrastructure data; it extracts sensitive target distribution data within the flight path buffer zone by querying sensitive facilities within the buffer zone through a geographic information database and a sensitive target-specific database, including schools, hospitals, residential areas, government agencies, military facilities, flammable and explosive sites, cultural relics protection units, etc., to obtain information such as the boundary latitude and longitude coordinates, land area, usage nature, and protection level of each sensitive target; and it integrates this information to form complete sensitive target distribution data.

[0018] In this embodiment of the invention, by adopting an adaptive expansion of the meteorological collection range based on airspace category, quantifying the range energy redundancy ratio based on nominal range and correction coefficient, setting buffer zones according to aircraft type and extracting population density and sensitive target distribution, the technical problems of isolated airspace meteorological data, lack of quantitative basis for endurance and communication energy, and coarse collection of ground risk factors are overcome, and the technical effects of accurate matching of airspace meteorology, accurate quantification of redundancy ratio, and fine spatial extraction of ground risks are achieved.

[0019] In a preferred embodiment of the present invention, step 2 above includes: Based on the flight area coordinates in the flight mission parameters, the airspace management category and corresponding operational access conditions of the area are determined, resulting in airspace access constraints. Specifically, this involves retrieving previously collected flight mission parameters and extracting complete coordinate information of the flight area. This coordinate information strictly adheres to the WGS-84 coordinate system, covering the start point, end point, key nodes along the route, and boundary closure coordinates of the flight area, forming a complete set of flight area coordinates. This coordinate set is then imported into the airspace information management system. Through the system's built-in national airspace database and in conjunction with established airspace classification standards, the airspace management category corresponding to this coordinate set is accurately matched, specifically including controlled airspace. The airspace is categorized into controlled airspace, monitored airspace, reporting airspace, and visual flight airspace. Controlled airspace is further subdivided into categories A, B, C, and D. After matching, all operational access conditions corresponding to the airspace management category are extracted simultaneously. Combining airspace management regulations and operational safety management requirements, airspace access constraints are formulated, which specifically include the airspace's weight limit, meteorological limits, altitude restrictions, usage rights, access qualification requirements, and flight time restrictions. The weight limit is determined based on the airspace category and UAV classification standards. Meteorological limits mainly include the allowable range of core meteorological parameters such as wind speed, visibility, and precipitation. Altitude restrictions specify the minimum and maximum flight altitudes for the airspace.

[0020] Based on the maximum takeoff weight and wind resistance rating in the aircraft model parameters, a comparison is made item by item with the weight limit and meteorological limit in the airspace access constraints to determine whether the physical capabilities of the aircraft model meet the access requirements, thus obtaining a compliance judgment. Specifically, this includes: retrieving the previously collected and verified UAV model parameters, extracting the two key indicators of maximum takeoff weight and wind resistance rating, and simultaneously extracting the corresponding weight limit and meteorological limit from the established airspace access constraints. The two sets of parameters are then compared item by item. For the maximum takeoff weight comparison, the UAV's maximum takeoff weight is directly compared with the weight limit stipulated in the airspace access constraints. If the maximum takeoff weight is less than or equal to the weight limit, the weight requirement is met; otherwise, it is not. Next, a wind resistance comparison is performed, first converting the wind resistance rating according to industry-standard conversion. The wind speed values ​​can be directly compared. The converted wind resistance speed is then compared with the allowable wind speed in the airspace meteorological limits and the actual maximum wind speed in the flight area. If the wind resistance speed is greater than or equal to both the allowable wind speed and the actual maximum wind speed, the wind resistance condition meets the requirements; otherwise, it does not. After completing the above two comparisons, further calculations of range energy redundancy are carried out to improve the aircraft's capability assessment. The required range, available range, and range energy redundancy ratio are obtained from the above formulas. According to operational safety standards, the redundancy ratio for regular routes is not less than 1.3, and the redundancy ratio for cross-sea routes is not less than 1.5. Considering the combined results of weight conditions, wind resistance conditions, and range energy redundancy ratio, if all three conditions are met, the aircraft's physical capability is deemed to meet the access requirements, and the aircraft's compliance is deemed qualified. If any one condition is not met, the aircraft is deemed unqualified.

[0021] Based on the flight route coordinate range in the flight mission parameters, information on special airspaces or no-fly zones traversed by the route is extracted. Combined with airspace access constraints, it is determined whether the route violates airspace usage regulations, resulting in a flight track compliance judgment. Specifically, this includes: retrieving the flight route coordinate range from the flight mission parameters. This range is based on the WGS-84 coordinate system and includes the coordinates of all nodes along the route and the coordinates of preset buffer zones on both sides of the route. The buffer zone width is uniformly set according to the UAV type. The route coordinate range is imported into the geographic information and airspace management system. Information on the distribution of special airspaces, no-fly zones, and sensitive targets traversed by the route is extracted. In combination with airspace access constraints, compliance judgments are carried out sequentially to determine whether the route crosses a no-fly zone. The route coordinates are superimposed and compared with the no-fly zone coordinates. If there is an overlap, the flight track is directly determined to be in violation. To determine compliance with special airspace usage regulations, if a flight path enters controlled airspace or other specially managed airspace, the corresponding approval documents are checked. If no valid approval documents are available, a violation is deemed. To determine compliance with sensitive target avoidance regulations, the minimum horizontal distance between the flight path and the sensitive target is calculated and compared to the legally mandated avoidance distance. If the distance is less than the required distance, a violation is deemed. To determine compliance with flight altitude, the actual true altitude of each node on the flight path is calculated. The true altitude is obtained by subtracting the corresponding ground altitude from the altitude of the flight path node. The actual true altitude is compared to the minimum and maximum altitudes specified in the airspace access constraints. If the altitude exceeds the specified range, a violation is deemed. Considering all the results, if all items comply with the regulations, the flight path is deemed to have not violated airspace usage regulations, and the flight path compliance is deemed satisfactory. If any violation exists, the flight path is deemed unsatisfactory.

[0022] Based on the compliance assessments of aircraft type and flight path, a comprehensive judgment is made to determine whether the operating entity meets all compliance requirements, resulting in a compliance assessment result. Specifically, this includes retrieving completed aircraft type and flight path compliance assessment results, and simultaneously conducting a comprehensive assessment of the operating entity in conjunction with the full-process compliance management requirements for drone operations. The assessment covers all compliance points, including aircraft physical conditions, flight path usage conditions, drone registration, operator qualifications, operating entity permits, legality of take-off and landing sites, and third-party liability insurance, ensuring that all requirements are verified. If the aircraft type compliance assessment is qualified, the flight path compliance assessment is qualified, and the operating entity possesses complete and valid real-name registration information, compliant operator qualifications, necessary operating permits, legally valid site usage documents, and sufficient third-party liability insurance, thus fully meeting the legal requirements for operational safety and airspace management, then the operating entity is deemed to meet all compliance requirements, and the final compliance assessment result is qualified. If the aircraft type or flight path is not compliant, or if there is any situation such as missing legal qualifications, invalid documents, or unmet conditions, the operating entity will be deemed not to meet all compliance requirements, and the final compliance judgment result will be unqualified. The unqualified items and rectification directions will be clearly marked. After all issues are rectified, a compliance judgment can be carried out again until all operational compliance requirements are met.

[0023] In this embodiment of the invention, by employing airspace database matching to extract operational access constraints, and combining them with the maximum takeoff weight of the aircraft type, wind resistance level conversion, and range energy redundancy ratio for item-by-item quantitative comparison, the technical problems of inaccurate matching between aircraft capabilities and airspace requirements and lack of quantitative standards for range redundancy are overcome. This achieves the technical effect of objective quantification of aircraft compliance and closed-loop verification of triple indicators. By using spatial overlay analysis of route coordinates with no-fly zones and sensitive targets, and combining true altitude calculation to verify altitude compliance, the technical problems of relying on experience-based judgment for track violations and coarse altitude calculation are overcome. This achieves the technical effect of automatic detection of track violations and accurate verification of avoidance distances. By comprehensively judging aircraft compliance, track compliance, and legal requirements such as real-name registration, operating qualifications, and operating permits, the technical problems of scattered and easily overlooked compliance check items are overcome. This achieves the technical effect of comprehensive closed-loop judgment of operational entity compliance and automatic marking of non-compliant items.

[0024] In a preferred embodiment of the present invention, step 3 above includes: Based on the aircraft type compliance assessment and flight path compliance assessment in the compliance determination results, a subset of airspace environment data that meets the access conditions is selected to obtain valid airspace data. Specifically, this includes retrieving the complete compliance determination results, which contain aircraft type compliance assessment, flight path compliance assessment, and detailed descriptions of each compliance check. The system automatically and accurately extracts the two core conclusions: aircraft type compliance assessment and flight path compliance assessment. The system will only automatically initiate the valid airspace data filtering process if both the aircraft type compliance assessment result and the flight path compliance assessment result are satisfactory. If the judgment result is unqualified, the screening of valid airspace data will be terminated directly, and a screening termination prompt will be output with the reason for termination. The screening process will be restarted after the aircraft type or flight track compliance is rectified and qualified. After the screening process is restarted, the system will retrieve all collected airspace environment data, which includes all relevant information such as airspace management category, boundary range, altitude restriction, usage permission, and access conditions of the flight area. According to the preset three-level rejection rules, all airspace environment data will be screened one by one, and invalid data will be removed. All airspace information related to unqualified aircraft types will be removed, including those exceeding the maximum limits for the aircraft type. Airspace information that exceeds takeoff weight limits, does not meet aircraft wind resistance requirements, or is incompatible with aircraft range; all airspace information related to illegal flight paths, including flight paths crossing no-fly zones, failure to avoid sensitive targets, and exceeding airspace altitude limits; all airspace information related to exceeding parameters, including exceeding airspace usage time limits and not meeting airspace access qualification requirements. After invalid data removal, the system only retains airspace information that simultaneously meets the following four conditions: fully complies with all requirements of the airspace access constraints determined in step 2 above, including weight limits, The system automatically integrates all the airspace information that has been filtered and retained, according to the system's preset format. The system aligns and calibrates the airspace boundary coordinates and synchronizes the time dimension of the airspace information, ultimately forming complete, continuous, and uninterrupted valid airspace data. The airspace information is subject to the following criteria: meteorological limits, altitude restrictions, etc.; performance parameters that are fully matched with those of qualified aircraft models; spatial range that is completely consistent with the compliant flight path and does not exceed the preset buffer zone on both sides of the flight path; and airspace safety thresholds, including airspace traffic restrictions and usage time restrictions, all of which comply with relevant regulations.

[0025] Based on the airspace categories in the valid airspace data, and referring to the preset airspace risk classification table, the basic airspace risk level is determined, and an initial airspace level value is obtained. Specifically, based on the filtered valid airspace data, the system automatically initiates the airspace category extraction process. Through the data parsing module, airspace category information is accurately extracted from the valid airspace data to clarify which category the airspace involved in the flight mission belongs to. The airspace categories mainly include four types: controlled airspace, monitored airspace, reporting airspace, and visual flight airspace. If the flight mission involves multiple different categories of airspace, the category information of each airspace is extracted separately, and subsequent risk level matching is carried out one by one. After the airspace category information is extracted, the system automatically calls the built-in, preset, and unmodifiable airspace risk classification table. This classification table is based on national airspace management regulations and unmanned aerial vehicle (UAV) regulations. The Aircraft Operation Safety Management Rules, taking into account factors such as the strictness of airspace control, flight restrictions, and the degree of safety risk, have been developed into a standardized risk classification table after multiple rounds of verification. The classification table clearly defines the basic risk level corresponding to each type of airspace. The specific classification standards are as follows: Controlled airspace is classified as high-risk due to the strictest control, the most flight restrictions, and the highest safety risk; Surveilled airspace has the next level of control, requiring flights to be monitored, and has a relatively high safety risk, classified as medium-high-risk; Reporting airspace has a moderate level of control, requiring flights to submit reports as required, and has a medium safety risk, classified as medium-risk; Visual Flight airspace has the most lenient control, requiring flights only to be observed visually, and has the lowest safety risk, classified as low-risk. When a flight mission involves a single standard airspace category, the basic risk level can be directly determined based on the airspace risk classification table. However, in actual low-altitude flight missions, there are often mixed airspace scenarios where flight paths cross multiple airspace categories. For example, the flight path may simultaneously pass through the overlapping areas of controlled airspace edges and monitored airspace, or there may be temporary restricted areas and regular reporting airspaces within the flight area. In such cases, a single lookup table method cannot accurately reflect the comprehensive risk characteristics brought about by the overlap of airspace categories. To address this, the Cuckoo Search algorithm iteratively optimizes the matching results between airspace categories and risk levels. It globally searches among multiple candidate solutions for the optimal matching result that combines the strictness of control and flight restrictions, thereby overcoming the problem of inaccurate lookup table results in mixed airspace scenarios. The specific implementation process and formula are as follows: The core parameters of the cuckoo search algorithm are set, including population size, maximum number of iterations, discovery probability, and step size factor. The population size N is set to 20, and the maximum number of iterations is [not specified]. T Set to 50, probability of detection p Set the step size factor to 0.25. α Set to 0.1, using the basic risk level corresponding to the airspace risk classification table as the initial candidate solution, each candidate solution corresponds to a matching relationship between airspace category and risk level, using... X The set of candidate solutions is represented by the formula: ,in,X This represents the set of candidate solutions for the cuckoo search algorithm, containing... One candidate matching result; The population size is represented by the number of candidate solutions, which is set to 20 here; the fitness function is constructed as follows: ,in, Indicates the first j The fitness values ​​of the candidate solutions; ω1 represents the weight of the strictness of airspace control, set to 0.6; ω2 represents the weight of flight restriction conditions, set to 0.4, and the sum of the two is 1; This represents the quantification value of the airspace control strictness corresponding to the j-th candidate solution; This represents a quantitative value indicating the level of strictness of standard controls corresponding to this airspace category; This represents the quantized value of the flight constraint conditions corresponding to the j-th candidate solution; This represents the quantified value of the standard flight restrictions corresponding to this airspace category; following the iterative rules of the cuckoo search algorithm, the candidate solution set is updated step by step until the maximum number of iterations is reached. The core formula of the iterative process is: ;in, Indicates the first t+1 After the nth iteration j There are 10 candidate solutions; Indicates the first t After the nth iteration j There are 10 candidate solutions; α represents the step size factor, which is set to 0.1. Let represent the random search path for the Levy flight, with λ set to 1.5. After each iteration, the fitness value of each candidate solution is calculated, and the top N candidate solutions with the smallest fitness values ​​are retained, while also being sorted according to their discovery probability. pThe algorithm randomly eliminates inferior solutions with high fitness values ​​and regenerates new candidate solutions to supplement the candidate solution set, ensuring diversity. When the maximum number of iterations T (50) is reached, iteration stops. The candidate solution with the lowest fitness value in the candidate solution set at this point is the optimal basic risk level matching result for that airspace category. The risk level corresponding to this matching result is used as the initial airspace level value. If the flight mission involves multiple different airspace categories, the above-mentioned Cuckoo Search algorithm optimization process is performed for each airspace category to obtain the basic airspace risk level corresponding to each airspace, i.e., the initial airspace level value for each airspace. After optimization, the system automatically outputs the basic airspace risk level corresponding to each airspace and uses this basic risk level as the initial airspace level value. The technical effect of the Cuckoo Search algorithm in a mixed airspace scenario is illustrated using an actual flight mission as an example: The mission route is 18 kilometers long, with approximately 40% of the route passing through the overlapping boundary area of ​​controlled airspace (Category D) and monitored airspace, and the remaining 60% of the route within the reported airspace. Directly consulting tables and selecting the highest-risk airspace category corresponding to the highest-risk airspace level ignores the fact that most flights actually occur in airspace with lower control intensity, leading to an overall overestimation of risk levels and potentially causing missions that should be flyable to be rejected. The Cuckoo Search algorithm is used to quantify the strictness of airspace control. C j And flight restriction condition quantification value L j As input to the fitness function, the mixed structure with 40% controlled airspace and 60% monitored and reported airspace is iteratively optimized. The initial airspace level output is medium to high risk, which is one level lower than the result of direct table lookup. This more accurately reflects the actual comprehensive risk level of the mixed airspace, improves the precision of risk assessment and the rationality of approval decisions.

[0026] Based on the airspace access constraints in the compliance assessment results, meteorological limit thresholds are extracted and compared item by item with wind speed, visibility, and precipitation data in the meteorological data to determine whether the meteorological conditions are within the flyable range, thus obtaining a meteorological flyability judgment. Specifically, this includes: retrieving the compliance assessment results; using the system's parsing module to accurately extract the meteorological limit thresholds from the airspace access constraints; these meteorological limit thresholds are determined based on factors such as the airspace category of the flight area, the flight mission type, and the drone model parameters; and represent the minimum meteorological requirements to ensure safe drone flight. Specifically, these include three core thresholds: maximum permissible wind speed, minimum visibility, and maximum permissible precipitation. The maximum permissible wind speed is determined based on the drone model's wind resistance rating, while the minimum visibility and maximum permissible precipitation are determined based on the airspace category and flight mission requirements. The system retrieves complete meteorological data through a meteorological data interface, covering the flight area and its surrounding preset range. The system automatically extracts three core meteorological parameters—actual wind speed, actual visibility, and actual precipitation—from real-time and forecast meteorological data for the flight area. During extraction, the system verifies the validity of the meteorological data, removing abnormal and missing data to ensure the accuracy and validity of the extracted parameters. Actual wind speed is calculated from the average wind speed and the instantaneous maximum wind speed during the flight period; actual visibility is calculated from the minimum visibility during the flight period; and actual precipitation is calculated from the cumulative precipitation during the flight period. After parameter extraction, the system precisely compares each of the three actual meteorological parameters with its corresponding meteorological limit thresholds, adhering strictly to preset judgment rules to prevent comparison deviations or judgment errors. The specific judgment rules are as follows: actual wind speed, including average wind speed and instantaneous maximum wind speed, is less than or equal to the maximum permissible wind speed; actual visibility is greater than or equal to the minimum visibility; and actual precipitation is less than or equal to the maximum permissible precipitation. Only when all three conditions mentioned above are met simultaneously will the system determine that the current weather conditions are within the flyable range, and the weather flyability judgment result will be flyable. If any one of the conditions is not met, whether it is that the actual wind speed exceeds the maximum allowable wind speed, the actual visibility is lower than the minimum visibility, or the actual precipitation exceeds the maximum allowable precipitation, the system will determine that the current weather conditions are not within the flyable range, and the weather flyability judgment result will be flyable. At the same time, the specific parameters that are not met and the deviation values ​​will be recorded.

[0027] Based on the initial airspace classification and meteorological flyability assessment, when the meteorological flyability assessment indicates no flight capability, the initial airspace classification is increased by one level; otherwise, the initial airspace classification remains unchanged, resulting in the airspace risk level. Specifically, this involves: retrieving the initial airspace classification and meteorological flyability assessment results, accurately correlating the two sets of data to ensure a one-to-one correspondence between the initial airspace classification and the corresponding meteorological flyability assessment result for each airspace, without misalignment or confusion; initiating the final airspace risk level determination process; adjusting the initial airspace classification for each airspace according to preset unified adjustment rules; and finally determining the airspace risk level for each airspace. When the meteorological flyability assessment result indicates no flight capability, the system automatically increases the initial airspace classification for that airspace by one risk level. During the increase process, the risk level ranking rules are strictly followed, while ensuring that the increased risk level is consistent with the initial airspace classification. The airspace risk level shall not exceed the highest risk level preset by the system. If the initial airspace risk level is already high, it shall be maintained and not adjusted. When the weather flyability assessment result is flyable, the system shall directly maintain the initial airspace risk level corresponding to that airspace and use it as the final airspace risk level for that airspace without any adjustment. If the flight mission involves multiple airspaces of different categories and the weather flyability assessment results of each airspace are different, each airspace shall be adjusted independently according to the above adjustment rules to determine the final airspace risk level of each airspace. The final risk levels of all airspaces shall be summarized. If the final risk levels of multiple airspaces are inconsistent, the airspace with the highest risk level shall be used as the airspace risk level of the entire flight mission to ensure that the risk assessment of the flight mission fully covers all involved airspaces.

[0028] Finalize the risk levels for all airspace.

[0029] In this embodiment of the invention, effective airspace data screening is triggered based on dual compliance conditions of aircraft type and flight path, and invalid airspace information is removed according to a three-layer elimination rule. This overcomes the technical problems of interference assessment of invalid airspace data and inconsistent screening standards, achieving the technical effect of accurate extraction of effective airspace data and reproducible screening conditions. The cuckoo search algorithm is used to iteratively optimize the matching results of the airspace risk classification table, and the matching accuracy of airspace control strictness and flight restriction conditions is optimized through the fitness function. Therefore, it overcomes the problems of coarse matching of basic risk levels and strong subjectivity, achieving the technical effect of objective optimization of initial airspace level values ​​and globally optimal matching results. The meteorological limit threshold in airspace access constraints is compared with actual wind speed, visibility, and precipitation item by item, and the airspace risk level is dynamically adjusted according to the comparison results. This overcomes the technical problems of separate assessment of meteorological conditions and airspace risk, and failure to timely upgrade the risk level when it is not flyable. This achieves the technical effect of accurate judgment of meteorological flyability, adaptive correction of airspace risk level, and the use of the highest risk level as the final output in multi-airspace scenarios.

[0030] In a preferred embodiment of the present invention, step 4 above includes: Based on the airspace risk level, the corresponding communication link redundancy threshold, energy redundancy threshold, and alternate landing density threshold are extracted from a preset risk threshold table to obtain a three-level threshold set. Specifically, this includes: retrieving the airspace risk level of the entire flight mission through the system data interaction interface; if the flight mission involves multiple different types of airspace, the highest risk level among all airspaces is used as the input basis, and the determined airspace risk level is used as the unique input parameter, passed to the system's built-in, preset, and unmodifiable risk threshold table. After receiving the airspace risk level input, the system automatically performs precise field-by-field matching in the risk threshold table to prevent level mismatches and incorrect threshold selection, and extracts the communication link redundancy threshold, energy redundancy threshold, and alternate landing density threshold corresponding one-to-one with the airspace risk level. The specific threshold standards for each level are as follows: the communication link redundancy threshold for the low-risk level is 1.2, and the energy redundancy threshold is... The threshold values ​​are as follows: 0.3 kWh / km for medium-risk level, 0.5 alternate landings / km for energy redundancy, 1.5 for medium-risk level, 0.4 kWh / km for energy redundancy, and 0.8 for alternate landings / km for medium-high risk level; 1.8 for medium-high risk level, 0.5 kWh / km for energy redundancy, and 1.2 for alternate landings / km for medium-high risk level; and 2.0 for high risk level, 0.6 kWh / km for energy redundancy, and 1.5 for alternate landings / km. These threshold values ​​are determined based on engineering practices in UAV operation safety risk management and statistical analysis of multiple batches of flight test data. They have been verified through specialized technical review and can be revised as industry standards are updated. After extracting the three threshold values, the system automatically categorizes, organizes, and stores them in association, combining them to form a three-level threshold set applicable to the current flight mission.

[0031] Based on the communication link redundancy thresholds in the three-level threshold set, and combined with the actual number of links and the number of backup links in the communication and identification capability parameters, the ratio between the actual redundancy and the threshold is calculated to obtain the communication link redundancy. Specifically, this includes: retrieving the communication link redundancy thresholds from the three-level threshold set to clarify the communication link redundancy judgment criteria corresponding to the current flight mission; and accurately retrieving the actual number of links and the number of backup links in the communication and identification capability parameters through the system parameter retrieval module. The actual number of links refers to the number of primary communication links currently used by the UAV for flight control, data transmission, and identification; the number of backup links refers to the number of backup communication links equipped by the UAV for automatic switching in case of primary link failure. During the retrieval process, the validity of both parameters is verified to ensure... The parameters are accurate and complete, with no missing or abnormal parameters. After verification, the system begins calculating the communication link redundancy step by step. The total number of communication links is calculated by adding the actual number of links to the number of backup links. For example, if there is one actual link and two backup links, the total number of links is 1 plus 2, which equals three. The actual redundancy value is then calculated by dividing the total number of links by the actual number of links. Continuing with the example above, three links divided by one gives an actual redundancy value of 3. Finally, the actual redundancy value is divided by the communication link redundancy threshold, resulting in the ratio between the two. This ratio represents the communication link redundancy for the current flight mission. For example, if the airspace risk level for the current flight mission is medium risk, the corresponding communication link redundancy threshold is 1.5, and the actual redundancy value is 3, then the communication link redundancy is 2. After calculation, the system automatically records the specific value of the communication link redundancy and labels all parameters and thresholds used in the calculation process.

[0032] Based on the energy redundancy thresholds in the three-level threshold set, and combining the current battery level in the energy system parameters with the planned flight distance in the flight mission parameters, the difference between the actual energy redundancy ratio and the threshold is calculated to obtain the flight range energy margin value. Specifically, this includes: retrieving the energy redundancy thresholds from the three-level threshold set to clarify the energy redundancy judgment criteria corresponding to the current flight mission; and retrieving the current battery level from the energy system parameters and the planned flight distance from the flight mission parameters through the system parameter retrieval module. The current battery level refers to the actual usable battery level after the UAV is fully charged before flight, minus the energy consumed during startup and preheating. The planned flight distance refers to the preset total flight distance from the takeoff point to the destination for the current flight mission. After retrieval, both parameters are verified to ensure the current battery level value is accurate and the planned flight distance is precise. After successful verification, the calculation of the flight range energy margin value is gradually carried out. Here, the flight range energy margin value is the difference between the actual usable energy per unit of flight distance and the energy redundancy threshold. A positive value indicates sufficient energy supply per unit, while a negative value indicates insufficient energy supply per unit. This is compared with the flight range energy redundancy ratio calculated in step 1 above based on the ratio of usable flight distance to the mission-required flight distance. R These two methods have different meanings, assessing endurance capabilities from two dimensions: energy density and range, respectively, and complement each other. The first method calculates the energy consumption per unit range by dividing the current battery level by the planned range. For example, if the current battery level is 10 kWh and the planned range is 20 km, then the energy consumption per unit range is 0.5 kWh / km. The second method calculates the range energy margin by subtracting the energy redundancy threshold from the calculated energy consumption per unit range. This difference represents the range energy margin for the current flight mission. For example, if the current flight mission's airspace risk level is medium risk, the corresponding energy redundancy threshold is 0.4 kWh / km, and the energy consumption per unit range is 0.5 kWh / km, then the range energy margin is 0.1 kWh / km. If the calculated difference is positive, it means the current battery level can meet the planned range and redundancy requirements; if the difference is negative, it means the current battery level cannot meet the redundancy requirements, and it is necessary to replenish the battery or adjust the planned range. After the calculation is completed, the system automatically records the specific value of the range energy margin and the calculation process.

[0033] Based on the alternate landing density thresholds in the three-level threshold set, and combined with the number of alternate landing points and the flight path coordinate range in the emergency alternate landing capability parameters, the ratio between the actual alternate landing point density and the threshold is calculated to obtain the alternate landing point setting density. Specifically, this includes: retrieving the alternate landing density thresholds from the three-level threshold set to clarify the alternate landing point density judgment criteria for the current flight mission; and retrieving the number of alternate landing points from the emergency alternate landing capability parameters and the flight path coordinate range from the flight mission parameters through the system parameter retrieval module. The number of alternate landing points refers to the total number of pre-set sites along and around the current flight path that meet the UAV alternate landing conditions; the flight path coordinate range refers to the actual length of the flight path from the start to the end point. Before calculation, the flight path needs to be converted into a two-dimensional polygon structure based on its latitude and longitude coordinates. The actual length of the flight path is then accurately calculated using the minimum bounding rectangle algorithm for polygons. The specific implementation process is as follows: The latitude and longitude coordinates of the flight path are converted to Cartesian coordinates using the WGS-84 coordinate system transformation formula to obtain the set of vertices of the corresponding two-dimensional polygon. It means that, among them, Let represent the set of vertices of the flight path polygon; n represents the number of vertices in the flight path polygon; for the set of vertices of the flight path polygon... The process involves deduplication and sorting to remove duplicate vertices. The vertices are also sorted clockwise to ensure the polygon structure is closed and error-free. After preprocessing, the set of valid vertices is retained. ;in, m < n , m The effective number of vertices; the minimum bounding rectangle of the polygon is solved using the rotating caliper algorithm, with the formula as follows: , ,in, This represents the length of the smallest enclosing rectangle; This represents the width of the minimum enclosing rectangle; Represents the set of valid vertices All vertices in x The maximum value of the axis coordinates; Represents the set of valid vertices All vertices in x Minimum value of the axis coordinates; Represents the set of valid vertices All vertices in y The maximum value of the axis coordinates; Represents the set of valid vertices All vertices in yThe minimum value of the axis coordinates is used to calculate the length and width of the minimum enclosing rectangle. Then, considering the flight path, the side length aligned with the flight path is taken as the calculation benchmark. Based on the result of the minimum enclosing rectangle calculation and the actual direction of the flight path polygon, the actual length of the flight path is calculated using a distance correction formula. The correction formula is as follows: ,in, Indicates the actual length of the flight path; This represents the length of the smallest enclosing rectangle; This represents the width of the minimum enclosing rectangle; η represents the route correction factor, which is set according to the curvature of the route, ranging from 0.95 to 1.05. The straighter the route, the closer η is to 1.0. For example, if the length of the minimum enclosing rectangle is... L =8 kilometers, width W =6 km, route correction factor η=1.0. After calculating the actual length of the flight route, the calculation of the alternate landing point density is carried out step by step. The actual alternate landing point density value is calculated by dividing the number of alternate landing points by the actual length of the flight route. For example, if there are 10 alternate landing points and the actual length of the flight route is 10 km, then the actual alternate landing point density value is 1 / km. The alternate landing point setting density is calculated by dividing the calculated actual alternate landing point density value by the alternate landing point density threshold. The ratio between the two is the alternate landing point setting density of the current flight mission. For example, if the airspace risk level of the current flight mission is medium risk, the corresponding alternate landing point density threshold is 0.8 / km, and the actual alternate landing point density value is 1 / km, then the alternate landing point setting density is 1.25. After the calculation is completed, the system automatically records the specific value of the alternate landing point setting density, and marks the number of alternate landing points, the actual length of the flight route, and the alternate landing point density threshold, etc.

[0034] Based on communication link redundancy, range energy margin, and alternate landing point density, risk scores are obtained in three dimensions by comparing them against preset scoring intervals. The three scores are then weighted and summed to obtain the technical dimension risk score. Specifically, this involves retrieving three core data points: communication link redundancy, range energy margin, and alternate landing point density. The validity of these three data points is verified to ensure accurate calculations, absence of anomalies, and compliance with preset numerical ranges. After verification, the three data points are input into the system's built-in preset scoring intervals. Each dimension corresponds to an independent scoring interval, divided into five levels: 0-20 (low risk), 21-40 (relatively low risk), 41-60 (medium risk), 61-80 (relatively high risk), and 81-100 (high risk). The system precisely matches the three core data points with the corresponding dimension's scoring interval, resulting in the communication dimension risk score. The risk scores are calculated for the energy dimension and the alternate landing dimension. For example, a communication link redundancy of 2 results in 20 points after matching the communication dimension scoring range; a range energy margin of 0.1 kWh / km results in 25 points after matching the energy dimension scoring range; and an alternate landing point density of 1.25 results in 20 points after matching the alternate landing dimension scoring range. The system's preset weights for the three dimensions are retrieved, with the communication dimension weight set at 0.4, the energy dimension weight at 0.3, and the alternate landing dimension weight at 0.3, summing to 1. These weights are determined based on the relative contribution of three types of accidents—UAV communication link failure, energy depletion, and alternate landing failure—to the low-altitude operational safety statistics. Communication link failure has the most direct impact on flight control, hence its highest weight. To ensure reasonable weight allocation and meet the core requirements of technical dimension risk assessment, a weighted summation calculation is performed. The formula for calculating the weighted score for the communication dimension is: ;in, This represents the weighted score for the communication dimension, which is the final score after weighting the risk score for the communication dimension. ω1 represents the communication dimension risk score, which is obtained after the communication link redundancy matching system presets a scoring range; ω1 represents the communication dimension weight, which is preset by the system and has a specific value of 0.4; the energy dimension weighted score is calculated using the following formula: ;in, This represents the weighted score for the energy dimension, which is the final score after the energy dimension risk score has been weighted and converted. ω1 represents the energy dimension risk score, which is obtained by matching the range energy margin value with a pre-set scoring range; ω2 represents the energy dimension weight, which is preset by the system and has a specific value of 0.3. The formula for calculating the weighted score of the alternate landing dimension is as follows: in, This represents the weighted score for the alternative landing dimension, which is the final score after the risk score for the alternative landing dimension has been weighted and converted. This represents the risk score for the alternate landing dimension, which is obtained after setting a pre-defined scoring range in the alternate landing point density matching system; ω3 represents the weight for the alternate landing dimension, which is preset by the system and has a specific value of 0.3. The formula for calculating the risk score for the technical dimension is as follows: ;in, This represents the final technical risk score; This represents the weighted score for the communication dimension; This represents the weighted score based on the energy dimension; This represents the weighted score for the alternative landing dimension, which is the result calculated using the third formula, resulting in the risk score for the technology dimension.

[0035] In this embodiment of the invention, a three-level threshold is extracted based on the airspace risk level, and the redundancy ratio, energy difference, and density ratio are calculated in combination with the actual link, power, flight range, and alternate landing point distribution. At the same time, the rotating caliper algorithm is used to solve the minimum enclosing rectangle of the route polygon to accurately calculate the route length. The weighted summation is used to obtain the technical dimension risk score. This overcomes the technical problems of lack of quantitative standards for technical assessment, inaccurate density estimation due to route curvature, and ambiguity of the weights of each dimension. It achieves the technical effects of three-level threshold self-adaptation, accurate quantification of redundancy and energy, accurate calculation of route length, and comprehensive and objective quantification of technical risks.

[0036] In a preferred embodiment of the present invention, step 5 above includes: Based on the technical dimension risk score, the number of polygon nesting analysis levels is determined by comparing the preset score range with the nesting level mapping table. Specifically, this involves retrieving the technical dimension risk score, validating the score to ensure accuracy, absence of anomalies, and compliance with the preset score range. After successful validation, the score is used as the core input parameter and passed to the preset score range and nesting level mapping table. Different polygon nesting analysis levels are set according to different technical dimension risk score ranges. The mapping relationship is clear, unique, and reproducible. Specifically, a low-risk score range corresponds to 1 level of nesting, a relatively low-risk score range corresponds to 2 levels of nesting, and a medium-risk score range corresponds to... The system should have 3 levels of nesting, with 4 levels corresponding to higher risk score ranges and 5 levels corresponding to high risk score ranges. This ensures that the determination of the nesting level is systematic. Based on the specific values ​​of the risk scores in the technical dimensions, the system performs precise matching in the mapping table to determine the corresponding number of polygon nesting analysis levels, outputs the final number of nesting levels, and records and labels this number. After the number of nesting levels is determined, the system retrieves sensitive target distribution data. This data covers detailed information such as polygon boundary coordinates, type, and location of all sensitive targets around the flight path. The system parses the sensitive target distribution data, extracts the polygon boundary coordinates corresponding to each sensitive target, and forms a set of sensitive target polygons.

[0037] Based on the nesting level, each sensitive target polygon in the sensitive target distribution data is shifted inward layer by layer to generate a multi-layered nested polygon set from the outside in, specifically including: Based on the determined nesting level, each sensitive target polygon in the sensitive target distribution data is analyzed individually. Core parameters such as boundary coordinates, number of vertices, and polygon type are extracted for each sensitive target polygon, creating a separate polygon processing file. After analysis, the system preprocesses each sensitive target polygon, removing redundant vertices on the polygon boundaries and correcting coordinate deviations. Following preprocessing, each sensitive target polygon undergoes an inward, layer-by-layer offset operation. The offset process strictly follows preset offset rules, explicitly specifying that the offset direction is perpendicular to the polygon boundary and inward, and the offset trajectory remains parallel to the polygon boundary. This ensures that the resulting nested polygons have the same shape and proportions as the original sensitive target polygons, and that the offset distance for each layer remains consistent. The offset distance is not a fixed value but is preset based on the type and size of the sensitive target. Specifically, for high-priority sensitive targets, such as densely populated areas and important transportation hubs, the offset distance is set to 0.05 meters; for medium-priority sensitive targets, the offset distance is set to... For ordinary residential areas and general roads, the offset distance is set to 0.1 meters; for low-priority sensitive targets, such as open areas and non-residential areas, the offset distance is set to 0.15 meters. The above offset distance parameters are determined based on the analysis of the geospatial influence range of sensitive targets and the engineering statistics of UAV crash diffusion radius. After verification by multiple sets of ground risk simulation experiments, the generation process of each layer of nested polygons is recorded in real time, including detailed information such as offset distance, offset time, and coordinate calibration results. Multiple layers of nested polygons are generated in sequence from the outer layer to the inner layer. The generation order strictly corresponds to the number of nesting levels. That is, if the number of nesting levels is 3, then the outer, middle, and inner layers of nested polygons are generated in sequence to ensure that the levels are clear and without omissions. After each layer of nested polygons is generated, the system will compare and verify it with the original sensitive target polygon, checking the positional relationship and shape similarity between the nested polygons and the original polygons to ensure that the nested polygons are always inside the original sensitive target polygons and that there is no intersection or overlap between levels. Once all levels of nested polygons have been generated, the system will associate and combine all nested polygons corresponding to the same sensitive target to form a complete set of nested polygons. Each set of nested polygons is labeled with core information such as the sensitive target number, the number of nesting levels, and the offset distance of each level.

[0038] Based on the nested polygon set, the ratio of the area of ​​each nested polygon to the area of ​​the original sensitive target polygon is extracted. The average vertical distance between the boundaries of adjacent polygon layers is measured as the overlap depth. This process yields the area ratio and overlap depth for each layer. Specifically, the complete nested polygon set is retrieved, and its validity is verified. Verification includes checking if the number of nested polygons matches the determined nesting level, if the boundaries of each nested polygon are closed, if the coordinates are accurate, and if there are any overlaps or errors. This ensures the nested polygon set is complete, valid, and without anomalies. After successful verification, the system initiates the data extraction and calculation process, sequentially extracting the boundary coordinates of each nested polygon layer and calculating the area of ​​each layer using the polygon area calculation formula. The specific formula is as follows: Nested polygon area calculation formula: ;in, Indicates the first o Area of ​​nested polygons o This refers to the nesting hierarchy number; p Indicates the first o The total number of vertices in a nested polygon; Indicates the first o Nested polygons t vertices x Axis coordinates; Indicates the first o Nested polygons t vertices y Axis coordinates; simultaneously records the area value of each nested polygon. The system retrieves the area data of the corresponding original sensitive target polygon. The formula for calculating the area ratio is: ;in, Indicates the first o The ratio of the area of ​​the nested polygon to the area of ​​the original sensitive target polygon; Indicates the first o The area of ​​nested polygons; This represents the area of ​​the original sensitive target polygon, yielding the area ratio for each layer. ; The system initiates the distance measurement process, employing a geometric distance measurement algorithm to measure the average vertical distance between the boundaries of two adjacent polygon layers as the overlap depth. The specific formula is as follows: ;in, Indicates the first o Layer and First o+1 The overlap depth between nested polygons; q This indicates the total number of measurement points selected on the boundary of two adjacent polygon layers; Indicates the first t At the measurement point, the first... o Layer and Firsto+1 The perpendicular distance between the boundaries of the polygonal layers is measured by uniformly selecting several measurement points on the boundaries of adjacent polygonal layers during the measurement process, and calculating the perpendicular distance between each measurement point. Then, use this formula to apply to all The overlap depth between adjacent layers is obtained by averaging the results. After measurement, record the overlap depth value for each layer. Then, record the area ratio of each layer. and corresponding overlap depth Perform associative storage and output the corresponding data for each layer. and .

[0039] Based on the area ratio and overlap depth of each layer, the area ratio and overlap depth of each layer are fused to obtain the risk superposition coefficient. Specifically, this includes retrieving the corresponding area ratio for each layer. With overlap depth A special data verification process was initiated, which consists of three core steps: verifying the validity of data at each level, and checking... Check if it is within the preset reasonable range [0,1]. If any data point is greater than 0, it is considered abnormal and the system automatically returns to the previous step to recalculate the area ratio and overlap depth for that level; it also verifies data correlation to confirm the relationship between each level. and One-to-one correspondence, that is, the first o Layer Must match the first o Layer and First o+1 Between layers Using a hierarchical numbering comparison method, and The hierarchical numbers are matched one by one. If a mismatch is found, data calibration is immediately triggered to re-associate the corresponding hierarchical data; data accuracy is verified, and checks are performed. Should three decimal places be retained? Whether to retain two decimal places; if the precision is insufficient, automatically round the data to ensure consistent precision. After all verification steps are completed without any anomalies, the calculation process for the risk superposition coefficient is officially initiated, using a weighted fusion algorithm to calculate the risk superposition coefficient for the same layer. and The fusion process strictly follows preset fusion rules, employing a fixed weight allocation method and area ratio. The weight is set to 0.6, denoted as Overlap depth The weight is set to 0.4, denoted as The sum of the two weights is strictly maintained at 1; the above weight allocation has been determined through multiple rounds of UAV flight safety risk assessment experiments and industry standard calibration. The area ratio directly reflects the degree of coverage of sensitive targets and has a higher weight in contributing to risk. To eliminate and To address the dimensional differences, the two datasets were standardized before the formal fusion calculation. A max-min normalization algorithm was used to obtain the standardized area ratio. and overlap depth After standardization is completed, the formal fusion computing process is initiated. The specific steps are as follows: Retrieve the first... o Layer standardization and and preset weights and The formula for calculating the weighted value of the area ratio is as follows: ; Calculate the overlap depth weighted value, the formula is as follows The two weighted values ​​are added together to obtain the risk superposition coefficient corresponding to that layer. The formula is After the calculation is completed, the result is rounded to three decimal places. A validity check is performed to verify if the result falls within the [0,1] interval. If it exceeds this interval, it is considered a calculation anomaly, and the standardization and fusion calculation steps are automatically re-executed until the result meets the requirements. Following the fusion calculation process described above, the system sequentially standardizes and fuses the area ratio and overlap depth of each layer to obtain the risk superposition coefficient for each layer. ,in o As nested hierarchical sequence numbers, after the risk superposition coefficients are calculated, the system combines them in hierarchical order to form a complete risk superposition coefficient sequence. ,in, The risk superposition coefficient corresponding to the outermost nested layer, The risk superposition coefficient corresponding to the innermost nested layer.

[0040] Based on the risk overlay coefficient, the population density values ​​within the flight route buffer zone in the population density distribution data are corrected, and the facility weights in the transportation infrastructure data are also corrected, resulting in corrected population density and facility weights. Specifically, this involves retrieving the complete risk overlay coefficient sequence. Retrieve population density distribution data, which is stored in raster format with a resolution of 10m x 10m grids. Each grid cell corresponds to a unique original population density value. It also includes auxiliary information such as the coordinate range and projected coordinate system of the raster; it retrieves traffic infrastructure data, which is stored in vector data format and covers complete information on all traffic infrastructure within the route buffer zone, specifically including facility type, geographical coordinates, facility scale parameters, and original weights. etc., where the original weights The initial weight is set based on the importance of the facility, with a value range of [0,1]. The higher the importance, the greater the initial weight. For example, the initial weight for airports and railway hubs is set to 0.9-1.0, and the initial weight for ordinary highways is set to 0.3-0.5. After data retrieval, a comprehensive verification of the population density distribution data and transportation infrastructure data was conducted. Specific verification steps are as follows: For population density distribution data, the verification points include: checking whether all grid cells within the flight path buffer zone have corresponding original population density values. There are no missing or blank rasters. If missing rasters exist, they are automatically filled using the average value of adjacent rasters, and the missing records are marked. The original population density values ​​are checked. Whether it is within a reasonable range [0, 10000], for abnormal data, automatically return to the data source to retrieve it again. If it cannot be retrieved again, use the average value of the three adjacent grids around the area to replace it, and record the abnormal handling process; check whether the projected coordinate system of the population density grid data is consistent with the coordinate system of the flight path buffer range data. If they are inconsistent, automatically perform coordinate transformation to ensure that the two coordinate systems are consistent and avoid errors in the correction range due to coordinate deviation. For traffic infrastructure data, the key points of verification include: checking whether all traffic infrastructure has complete information such as type, coordinates, and original weights, and whether there are any missing information items. If there are missing items, automatically mark them and remind staff to supplement and improve them. After the supplementation is completed, proceed to the next process; check whether the facility coordinates are within the flight path buffer range. If there is facility data outside the buffer, automatically remove it and check the original weights. If the data is outside the [0,1] range, it is automatically adjusted to the corresponding boundary value, and the adjustment process is recorded. The system checks for duplicate facility data; if duplicate data exists, a complete record is automatically retained, and redundant data is deleted. After all verification steps are completed without any abnormalities, the data correction process is officially initiated, and population density values ​​are corrected. The correction process strictly adopts a linear correction model. First, the correspondence between correction levels is determined, and the grid area within the flight path buffer is precisely matched with the level of the nested polygon. Based on the grid's coordinate position, it is determined that the grid belongs to the [0,1] level. o The coverage area of ​​nested polygons is determined, thereby identifying the risk overlay coefficient corresponding to that raster. The formula for correction is clearly defined, specifically as follows: ,in, This is the corrected population density value. This is the original population density value. This is the risk superposition coefficient for the corresponding level of the raster; correction calculations are performed raster-by-raster, with the system retrieving the values ​​of each raster in order of their raster numbers. and corresponding The values ​​are substituted into the correction formula for calculation. After the calculation, the results are rounded to one decimal place. The result is then verified after correction, and this process is repeated for each grid cell. Perform a validity check to see if it falls within the reasonable range [0, 12000]. If it exceeds the range, it is judged as a correction anomaly and automatically rechecked. and The values ​​are then recalculated and corrected until the results meet the requirements; all raster values ​​are corrected. The data is reorganized according to coordinate location to form a complete and corrected population density raster. Simultaneously, the weights of transportation infrastructure are corrected, also using a linear correction model. The correction logic is consistent with the population density correction. The hierarchical correspondence of facilities is determined, and based on the geographical coordinates of each transportation infrastructure, it is determined which level the facility belongs to. o The coverage area of ​​nested polygons is determined to identify the risk superposition coefficient corresponding to the facility. For large facilities spanning multiple nested levels, the corresponding level should be determined according to the level where the main facility is located. The correction formula is specified as follows: ,in, For the corrected facility weights, For the original weight of the facility, This is the risk superposition coefficient for the corresponding level of the facility; a correction calculation is performed for each facility, and the system retrieves the risk coefficient for each facility sequentially according to its facility number. and corresponding Substitute the values ​​into the correction formula for calculation; perform boundary clamping, as the physical meaning of facility weights requires their values ​​to be within the [0,1] interval, therefore, the corrected values ​​are... Perform boundary clamping operation, the specific logic is as follows: If If >1, it will be automatically adjusted to 1.0; if If the value is less than 0, it will be automatically adjusted to 0.0; if it is in the range [0,1], the original value will remain unchanged. After adjustment, the boundary clamping status will be recorded; after correction, verification will be performed on each facility. The system performs a check to see if the value is within the [0,1] range and if it matches the facility type. For example, the corrected weight for an airport should not be lower than 0.8. If any anomalies are found, the system automatically rechecks the value. and Repeat the correction and clamping operations until the result is satisfactory. After all corrections are complete, the corrected population density will be displayed. With the revised facility weights As the final output.

[0041] Based on the revised population density and facility weights, and combined with the flight path buffer zone, the potential for casualties and the probability of facility damage per unit area are assessed to obtain a comprehensive ground risk level. This specifically includes retrieving the revised population density obtained in the previous step. With the revised facility weights Simultaneously, the data on the flight path buffer zone is retrieved, including core parameters such as the boundary coordinates and area of ​​the buffer zone. This initiates the comprehensive ground risk level assessment process, and the potential for casualties is calculated: a preset casualty coefficient α is set, which is based on the type of sensitive target and population density. The potential for casualties per unit area is calculated. The calculation formula is: ;in, This represents the potential for casualties per unit area. The corrected population density is represented by α, which represents the preset casualty coefficient. The calculation of the facility damage probability is as follows: The preset facility damage coefficient is set as β, which is preset based on the importance and resilience of the transportation infrastructure. The value represents the facility damage probability per unit area. The calculation formula is: ;in, This represents the probability of facility damage per unit area. This represents the corrected facility weights, where β represents the preset facility damage coefficient; the overall ground risk assessment score is calculated as follows: the weight of the potential for casualties is set as... The weight of the facility damage probability value is ,satisfy The specific calculation steps are as follows: Calculate the weighted value of the potential for casualties. The weighted average value for calculating the probability of facility damage is... ; Calculate the total score of the comprehensive ground risk assessment Comprehensive ground risk level determination: A preset risk level range mapping table is invoked, and the total score of the comprehensive ground risk assessment is calculated. For precise matching with intervals, the mapping table is as follows: Indicates low risk; This indicates a lower risk. Indicates medium risk; This indicates a higher risk; This indicates a high risk level. After matching is complete, the final comprehensive ground risk level is output.

[0042] In this embodiment of the invention, a risk scoring system based on technical dimensions is used to map the number of nested levels. A set of nested polygons is generated by shifting sensitive target polygons inward layer by layer. The area ratio and overlap depth of each layer are extracted and then weighted and fused to obtain a risk superposition coefficient. This overcomes the technical problems of lacking hierarchical quantification of the spatial impact of sensitive targets and the difficulty in accurately representing the superposition effect of multi-level buffer zones. It achieves the technical effect of accurately calculating the risk superposition coefficient layer by layer and quantifying the attenuation of spatial impact with distance. Furthermore, the risk superposition coefficient is used to linearly correct population density and facility weights, and a weighted sum is obtained to obtain the comprehensive ground risk level. This overcomes the technical problems of fixed weights for ground risk elements and lack of linkage with spatial levels. It achieves the technical effect of dynamically adaptively correcting the ground risk level and tightly coupling the assessment results with geometric features.

[0043] In a preferred embodiment of the present invention, step 6 above includes: Based on the comprehensive ground risk level, and by comparing it with the preset fault occurrence frequency benchmark table, the occurrence frequency benchmark values ​​for each of the five types of fault scenarios are determined, and the occurrence frequency of each type of fault is obtained. Specifically, this includes: retrieving the comprehensive ground risk level obtained from the previous assessment, which is specifically divided into five categories: low risk, lower risk, medium risk, higher risk, and high risk; verifying the validity of the comprehensive ground risk level; confirming that the correspondence between the level and the total score of the comprehensive ground risk assessment is correct, and that there are no abnormalities such as incorrect level determination or numerical misalignment. After successful verification, the system invokes a pre-set fault occurrence frequency benchmark table. This benchmark table is a standardized table built into the system and has been pre-set based on industry standards for UAV flight safety risk assessment, extensive flight test data, and fault statistics. The table clearly corresponds to each comprehensive ground risk level and sets the occurrence frequency benchmark values ​​for five types of fault scenarios. The five types of fault scenarios include communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure. The occurrence frequency benchmark values ​​for each type of fault scenario are presented as percentages, and there are clear differences in the benchmark values ​​corresponding to different comprehensive ground risk levels. The higher the comprehensive ground risk level, the higher the occurrence frequency benchmark values ​​for each type of fault scenario. The system accurately compares the currently determined comprehensive ground risk level with the fault occurrence frequency benchmark table. Based on the level matching results, it extracts the occurrence frequency benchmark values ​​corresponding to each of the five types of fault scenarios one by one. After extraction, the extracted benchmark values ​​are checked for consistency to ensure that the occurrence frequency benchmark values ​​for each type of fault scenario are within the preset reasonable range and that there are no abnormally high or low values. After successful verification, the extracted benchmark values ​​are determined as the occurrence frequencies of each type of fault.

[0044] Based on the frequency of occurrence of various types of faults, and combined with the communication link redundancy, range energy margin, and alternate landing point density in the technical dimension risk score, the frequency of occurrence of each type of fault is corrected to obtain the corrected frequency of occurrence of each type of fault. Specifically, this includes: retrieving the frequency of occurrence of each type of fault, retrieving the three core parameters included in the technical dimension risk score, namely communication link redundancy, range energy margin, and alternate landing point density, and after retrieval, verifying the validity of the three parameters and standardizing them to ensure that the parameter data is complete, accurate, and conforms to the preset data standards. Among them, communication link redundancy is used to characterize the backup guarantee capability of the UAV's communication link, with a value ranging from 0 to 1. The larger the value, the stronger the communication link redundancy capability and the lower the probability of failure. Range energy margin is used to characterize the redundancy guarantee level of the UAV's range endurance, with a value ranging from 0 to 2. The larger the value, the more sufficient the range energy redundancy and the lower the probability of failure in the power system and other related systems. Alternate landing point density is used to characterize the density of alternate landing points around the UAV's flight path. The larger the value, the denser the alternate landing point distribution, the more timely the handling of failures, and the lower the actual impact and probability of related failures. During the standardization process, the system uniformly maps the three parameters to the range of 0 to 1, and uses the maximum-minimum normalization method to eliminate the dimensional differences between different parameters. After standardization, the system initiates a fault occurrence frequency correction process. For each type of fault, corrections are made based on the standardized values ​​of the three parameters. The correction process employs a linear correction method, assigning different correction weights to the three parameters according to the characteristics of different fault scenarios. For communication link faults, the weights are 0.6, 0.2, and 0.2 respectively; for power system faults, the weights are 0.2, 0.6, and 0.2 respectively; and for navigation system faults, flight control system faults, and weather adaptation faults, the weights are 0.2, 0.2, and 0.6 respectively. The sum of the weights for each fault scenario is 1. During the correction calculation, the weighted sum of the three parameters is first calculated. Then, the original occurrence frequency of each fault type is multiplied by 1 and subtracted from this weighted sum to obtain the corrected occurrence frequency of each fault type. The corrected occurrence frequency is still presented as a percentage. After the correction is completed, the system performs a validity check on the frequency of occurrence of the correction. It checks whether the frequency of occurrence of each type of fault after correction is within a reasonable range, whether the correction range meets the preset standard, and whether there are any abnormal correction results. After the verification is passed, the frequency of occurrence of each type of fault after correction is determined.

[0045] Based on the corrected frequency of occurrence of various faults, and comparing it with the preset impact level judgment table, the impact range level of personnel casualties and facility damage that each type of fault may cause is determined, resulting in the impact level of each type of fault. Specifically, this includes: retrieving the corrected frequency of occurrence of various faults, classifying and organizing the frequency of occurrence of each type of fault, and labeling the corresponding corrected frequency of occurrence for each fault type. The system calls the preset impact level judgment table, which is a standardized table built into the system. It has been preset in advance based on the statistical data of the impact range of UAV faults and the distribution characteristics of ground personnel and facilities. The table clearly divides the interval range of the corrected occurrence frequency, and for each frequency interval, there are clear personnel casualty impact range level and facility damage impact range level. The impact range level is divided into five levels: Level 1 has the smallest impact range, Level 2, Level 3, Level 4, and Level 5 has the largest impact range. The higher the corrected occurrence frequency, the higher the corresponding personnel casualty and facility damage impact range level, and the two are positively correlated. For each type of fault, the system precisely compares its corrected occurrence frequency with the frequency range in the impact level determination table to determine the range within which the fault's occurrence frequency falls. Based on the range matching results, it extracts the corresponding personnel casualty impact range level and facility damage impact range level one by one. After extraction, the system performs a correlation check on the two extracted impact range levels to check if they conform to the preset correspondence, i.e., the difference between the personnel casualty impact range level and the facility damage impact range level does not exceed one level. If the difference exceeds the range, it is determined as a mismatch, and the system automatically re-matches the corrected occurrence frequency with the determination table until the matching result meets the requirements. After the verification passes, the personnel casualty impact range level and facility damage impact range level corresponding to each type of fault are jointly determined as the impact level of that type of fault.

[0046] Based on the corrected frequency and impact levels of various fault types, the frequency and impact levels of each fault type are comprehensively assessed to obtain individual risk quantification values ​​for each of the five fault types. Specifically, this involves retrieving the corrected frequency and impact levels of each fault type, correlating the two sets of data to ensure there are no data misalignments, confusions, or other anomalies. After correlation, the validity of the two sets of data is verified, checking whether the corrected frequency is accurate and whether the impact level meets the assessment criteria, ensuring the data can be directly used for comprehensive assessment. The system initiates the comprehensive assessment process, which uses a weighted summation method to integrate the corrected frequency and impact level of each fault type to obtain the individual risk quantification value for that fault type. The impact level must first be converted into a quantified score: Level 1 corresponds to 10 points, Level 2 to 20 points, Level 3 to 30 points, Level 4 to 40 points, and Level 5 to 50 points; the corrected frequency is converted to decimal form for calculation. In the weighted summation process, the weight of the corrected occurrence frequency is set to 0.4, and the weight of the impact level quantification score is set to 0.6, with the sum of the two weights being 1. This weight allocation is determined based on the degree of influence of each on the individual risk of the fault. The impact level directly reflects the severity of the damage caused by the fault and has a higher weight, while the occurrence frequency reflects the probability of the fault occurring and has a relatively lower weight. The specific calculation process is as follows: first, multiply the corrected occurrence frequency by the corresponding weight to obtain the occurrence frequency weighted value; then, multiply the impact level quantification score by the corresponding weight to obtain the impact level weighted value; finally, add the two weighted values ​​to obtain the individual risk quantification value for this type of fault. The system performs a comprehensive judgment on each of the five types of faults according to the above process, obtaining the individual risk quantification value for each type of fault. After the calculation is completed, the validity of each individual risk quantification value is verified to check whether the quantification value is within the preset reasonable range and whether there are any abnormally high or low values. After the verification is passed, the individual risk quantification value for each of the five types of faults is determined.

[0047] The system sorts the individual risk quantification values ​​of the five types of faults by value to obtain the operational safety risk quantification value. Based on the operational safety risk quantification value, it compares it with the preset risk level and mitigation measure mapping table, selects the corresponding mitigation measure combination, generates a risk control plan, and completes the safety risk quantification assessment. Specifically, this includes: sorting the five quantification values ​​in descending order of individual risk quantification value; if two or more individual risk quantification values ​​are equal during the sorting process, sorting them according to the priority of the fault type, from high to low priority: flight control system fault, power system fault, navigation system fault, communication link fault, and weather adaptation fault, ensuring that the sorting results are standardized and reasonable. After sorting, the five individual risk quantification values ​​are summed to obtain the operational safety risk quantification value. After the operational safety risk quantification value is determined, the system performs a validity verification to check whether the quantification value is within the preset reasonable range and whether there are any abnormal values. After the verification is passed, the quantification value is used as the core quantitative indicator of UAV flight operation safety risk and is synchronously stored in the system database. The system invokes a pre-defined risk level and mitigation measure mapping table. This table, a standardized table built into the system, has been pre-set based on UAV flight safety management requirements, fault handling experience, and risk control standards. The table clearly defines the range of quantitative values ​​for operational safety risks, with each range corresponding to a specific risk level: low, relatively low, medium, relatively high, and high. For each risk level, a complete set of mitigation measures is pre-defined, covering three dimensions: fault prevention measures, fault handling measures, and emergency response measures. The level of detail and execution intensity of the mitigation measures varies depending on the risk level; the higher the risk level, the more comprehensive and intense the mitigation measures. The system precisely compares the determined quantitative values ​​for operational safety risks with the ranges in the mapping table to determine the corresponding risk level. Then, it extracts the corresponding mitigation measure combination based on the risk level. After extraction, the system verifies the rationality of the mitigation measure combination, checking whether it covers all three dimensions, matches the fault type and risk level, and is executable. If any inconsistencies are found, the system automatically adjusts and optimizes the mitigation measure combination until it meets the requirements. After successful verification, the system organizes the mitigation measures, clarifies the execution sequence, standards, responsible parties, and execution time for each measure, and generates a complete risk control plan. This plan includes a quantitative value of operational safety risk, the corresponding risk level, detailed descriptions of each mitigation measure, and the execution process. The risk control plan is then output, simultaneously completing the quantitative assessment of safety risks. The assessment results, the quantitative value of operational safety risks, the risk control plan, and all relevant records from the assessment process are stored together, thus completing the quantitative assessment of operational safety risks for low-altitude unmanned aerial vehicles (UAVs).

[0048] In this embodiment of the invention, a comprehensive ground risk level is used to match the fault occurrence frequency benchmark. The frequency is linearly corrected by combining communication link redundancy, flight range energy margin, and alternate landing point density. Then, the impact level judgment table is matched to obtain the personnel casualty and facility damage levels. The individual risk quantification values ​​of the five types of faults are obtained by weighted summation and sorted and summed to obtain the operational safety risk quantification value. Finally, mitigation measures are matched to generate a risk control plan. This overcomes the technical problems of a single fault frequency benchmark, lack of technical parameters in the correction, separation of impact level and frequency assessment, and lack of quantitative ranking basis. It achieves the technical effects of dynamic fault frequency correction, accurate calculation of individual risk quantification values, sortable operational safety risks, and automatic matching of mitigation measures.

[0049] like Figure 2 As shown, embodiments of the present invention also provide a system for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations, comprising: The data acquisition module is used to collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. The judgment module is used to determine the compliance of the operating entity based on aircraft type parameters and flight mission parameters, and obtain the compliance judgment result; based on the compliance judgment result, combined with airspace environment data and meteorological data, the airspace level and weather flyability are assessed to obtain the airspace risk level; The scoring module is used to obtain a technical risk score based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters. The analysis module is used to perform polygon nested hierarchical analysis on the distribution data of sensitive targets based on the risk score of the technical dimension, extract the area ratio and overlap depth of each layer and calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. The processing module is used to sequentially deduce five preset failure scenarios—communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure—based on the comprehensive ground risk level, calculate the occurrence frequency and impact level of each type, and obtain the quantitative value of operational safety risk; match mitigation measures according to the quantitative value of operational safety risk, generate risk control plan, and complete the assessment of the quantitative safety risk.

[0050] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0051] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0052] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0053] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations, characterized in that, The method includes: Step 1: Collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. Step 2: Based on the aircraft type parameters and flight mission parameters, determine the compliance of the operating entity and obtain the compliance determination result; Step 3: Based on the compliance assessment results, combined with airspace environmental data and meteorological data, assess the airspace level and weather flyability to obtain the airspace risk level. Step 4: Based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters, obtain the technical dimension risk score; Step 5: Based on the risk score of the technical dimension, perform polygon nested hierarchical analysis on the distribution data of sensitive targets, extract the area ratio and overlap depth of each layer, calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. Step 6: Based on the comprehensive ground risk level, simulate five preset failure scenarios in sequence: communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure. Calculate the occurrence frequency and impact level of each scenario to obtain the quantitative value of operational safety risk. Match mitigation measures according to the quantitative value of operational safety risk, generate a risk control plan, and complete the assessment of the quantitative safety risk.

2. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 1, characterized in that, Collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data, including: Collect flight area coordinates from flight mission parameters to obtain airspace environment data; Based on the airspace category in the airspace environment data, the meteorological collection range is determined, and the wind speed, visibility and precipitation data of the flight area are obtained through the meteorological service interface to obtain meteorological data; Based on the flight time and wind resistance rating in the aircraft parameters and the wind speed in the meteorological data, the range energy margin is calculated, and the communication link configuration and battery capacity are read to obtain the communication and identification capability parameters and energy system parameters. Based on the flight route coordinate range in the flight mission parameters, population density, transportation network and sensitive facility distribution data are extracted within the preset buffer zones on both sides of the route to obtain population density distribution data, transportation infrastructure data and sensitive target distribution data.

3. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 2, characterized in that, Based on aircraft type parameters and flight mission parameters, the compliance of the operating entity is determined, and the compliance determination result is obtained, including: Based on the flight area coordinates in the flight mission parameters, determine the airspace management category of the area and the corresponding operational access conditions to obtain airspace access constraints; Based on the maximum takeoff weight and wind resistance rating in the aircraft parameters, the weight limit and meteorological limit in the airspace access constraints are compared item by item to determine whether the physical capabilities of the aircraft meet the access requirements, and thus the aircraft compliance judgment is obtained. Based on the flight route coordinate range in the flight mission parameters, extract information on special airspaces or no-fly zones that the route passes through, and combine this with airspace access constraints to determine whether the route violates airspace use regulations, thus obtaining a flight track compliance judgment. Based on the compliance assessment of aircraft type and flight path, a comprehensive determination is made as to whether the operating entity meets all compliance requirements, resulting in a compliance assessment result.

4. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 3, characterized in that, Based on the compliance assessment results, combined with airspace environmental data and meteorological data, the airspace grade and weather-related flyability are evaluated to obtain the airspace risk level, including: Based on the aircraft type compliance judgment and flight track compliance judgment in the compliance judgment results, a subset of airspace environment data that has met the access conditions is selected to obtain valid airspace data; Based on the airspace categories in the valid airspace data, and by referring to the preset airspace risk classification table, the basic airspace risk level is determined, and the initial airspace level value is obtained. Based on the airspace access constraints in the compliance assessment results, meteorological limit thresholds are extracted and compared with wind speed, visibility and precipitation data in the meteorological data to determine whether the meteorological conditions are within the flyable range, thus obtaining a meteorological flyability assessment. Based on the initial airspace level and weather-based flightability assessment, if the weather-based flightability assessment indicates that the airspace is not flyable, the initial airspace level is increased by one level; otherwise, the initial airspace level is maintained, thus obtaining the airspace risk level.

5. The method for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operation according to claim 4, characterized in that, In the step of determining the basic airspace risk level, the cuckoo search algorithm is used to iteratively optimize the matching results between airspace categories and risk levels to obtain an initial airspace level value, including: The risk level corresponding to the preset airspace risk classification table is used as the initial candidate solution, and the population size is set. N=20 Maximum number of iterations T=50 Probability of discovery p=0.25 Step size factor α=0.1 Construct a set of candidate solutions Fitness function: , where ω1 =0.6 ω2 is the weight for the strictness of airspace control. =0.4 As the weight of flight restriction conditions, and The standard quantization value for the corresponding airspace category; Update candidate solutions using iterative rules: , where λ =1.5 After reaching the maximum number of iterations, the risk level corresponding to the candidate solution with the smallest fitness value is taken as the initial value of the spatial domain level.

6. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 4, characterized in that, Based on the airspace risk level, and combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters, a technical dimension risk score is obtained, including: Based on the airspace risk level, the corresponding communication link redundancy threshold, energy redundancy threshold and alternate landing density threshold are extracted from the preset risk threshold table to obtain a three-level threshold set. Based on the communication link redundancy threshold in the three-level threshold set, and combined with the actual number of links and the number of backup links in the communication and identification capability parameters, the ratio between the actual redundancy and the threshold is calculated to obtain the communication link redundancy. Based on the energy redundancy threshold in the three-level threshold set, and combined with the current battery charge in the energy system parameters and the planned flight range in the flight mission parameters, the difference between the actual energy redundancy ratio and the threshold is calculated to obtain the range energy margin value. Based on the alternate landing density threshold in the three-level threshold set, and combined with the number of alternate landing points and the flight route coordinate range in the emergency alternate landing capability parameters, the ratio between the actual alternate landing point density and the threshold is calculated to obtain the alternate landing point setting density. Based on communication link redundancy, range energy margin, and alternate landing point density, risk scores are obtained in three dimensions by comparing them with preset scoring intervals. The three scores are then weighted and summed to obtain the technical dimension risk score.

7. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 6, characterized in that, In the step of calculating the alternate landing point density, the rotating caliper algorithm is used to solve for the minimum enclosing rectangle of the flight path polygon to accurately calculate the actual length of the flight path, thereby obtaining the alternate landing point density, including: The latitude and longitude coordinates of the flight path are converted into Cartesian coordinates based on the WGS-84 coordinate system to obtain the vertex set. After deduplication and clockwise sorting, the set of valid vertices is obtained. ; The minimum enclosing rectangle is found using the rotating caliper algorithm, and its length is calculated. and width ; The actual length of the route is calculated using the distance correction formula. η is the route correction factor, which ranges from 0.95 to 1.05 and is determined based on the curvature of the route. Divide the number of alternate landing points by The actual alternate landing point density is obtained, and then divided by the alternate landing point density threshold to obtain the alternate landing point setting density.

8. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 5, characterized in that, The number of nesting levels is determined based on the risk score from the technical dimension. Polygonal nesting hierarchy analysis is performed on the distribution data of sensitive targets to extract the area ratio and overlap depth of each layer and calculate the risk superposition coefficient. Population density and transportation facility weights are then adjusted to obtain the comprehensive ground risk level, including: Based on the risk score of the technical dimension, compare the preset score range with the nesting level mapping table to determine the number of polygon nesting analysis levels and obtain the nesting level number. Based on the number of nesting levels, each sensitive target polygon in the sensitive target distribution data is shifted inward layer by layer to generate a multi-layer nested polygon set from the outside to the inside. Based on the nested polygon set, the ratio of the area of ​​each nested polygon layer to the area of ​​the original sensitive target polygon is extracted, and the average vertical distance between the boundaries of two adjacent polygon layers is measured as the overlap depth to obtain the area ratio of each layer. With overlap depth ; Based on the area ratio of each layer With overlap depth For the area ratio of each layer With overlap depth The standardized area ratio is obtained by using the max-min normalization algorithm. and standardized overlap depth Based on area ratio weighting Overlap depth weight Calculate the risk superposition coefficient for each layer: ; and form a risk superposition coefficient sequence by combining them in a hierarchical order. ,in, Corresponding to the outermost layer, For the innermost layer, the risk superposition coefficient is obtained; Based on the risk superposition coefficient, the population density value within the flight route buffer in the population density distribution data is corrected, and the facility weight in the transportation infrastructure data is corrected to obtain the corrected population density and facility weight. Based on the revised population density and facility weights, combined with the flight path buffer zone, the potential for casualties and the probability of facility damage per unit area are assessed to obtain the comprehensive ground risk level.

9. The method for quantitatively assessing the operational safety risks of low-altitude unmanned aerial vehicles according to claim 8, characterized in that, Based on the comprehensive ground risk level, five preset failure scenarios—communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure—are simulated in sequence. The occurrence frequency and impact level of each type are calculated to obtain the quantitative value of operational safety risk. Based on the quantitative value of operational safety risks, mitigation measures are matched to generate risk control plans, and a quantitative assessment of safety risks is completed, including: Based on the comprehensive ground risk level, and by comparing with the preset fault occurrence frequency benchmark table, the occurrence frequency benchmark values ​​of the five types of fault scenarios are determined, and the occurrence frequency of each type of fault is obtained. Based on the frequency of occurrence of various types of faults, and combined with the communication link redundancy, flight energy margin, and alternate landing point density in the technical risk score, the frequency of occurrence of each type of fault is corrected to obtain the corrected frequency of occurrence of each type of fault. Based on the frequency of occurrence of various types of faults after the correction, and by comparing with the preset impact level judgment table, the impact range level of personnel casualties and facility damage that may be caused by each type of fault is determined, and the impact level of each type of fault is obtained. Based on the frequency and impact level of each type of fault after the correction, the frequency and impact level of each type of fault are comprehensively judged to obtain the individual risk quantification value of each of the five types of faults. The individual risk quantification values ​​of the five types of faults are sorted by value to obtain the operational safety risk quantification value. Based on the operational safety risk quantification value, the preset risk level and mitigation measure mapping table is compared, the corresponding mitigation measure combination is selected, the risk control plan is generated, and the safety risk quantification assessment is completed.

10. A system for quantitatively assessing the safety risks of low-altitude unmanned aerial vehicle (UAV) operations, wherein the system implements the method as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect basic operational data, including UAV model parameters, flight mission parameters, airspace environment data, meteorological data, communication and identification capability parameters, energy system parameters, emergency landing capability parameters, population density distribution data below the flight path, transportation infrastructure data, and sensitive target distribution data. The judgment module is used to determine the compliance of the operating entity based on aircraft type parameters and flight mission parameters, and obtain the compliance judgment result; based on the compliance judgment result, combined with airspace environment data and meteorological data, the airspace level and weather flyability are assessed to obtain the airspace risk level; The scoring module is used to obtain a technical risk score based on the airspace risk level, combined with communication and identification capability parameters, energy system parameters, and emergency landing capability parameters. The analysis module is used to perform polygon nested hierarchical analysis on the distribution data of sensitive targets based on the risk score of the technical dimension, extract the area ratio and overlap depth of each layer and calculate the risk superposition coefficient, and correct the population density and transportation facility weights to obtain the comprehensive ground risk level. The processing module is used to sequentially deduce five preset failure scenarios based on the comprehensive ground risk level: communication link failure, power system failure, navigation system failure, flight control system failure, and weather adaptation failure. It calculates the occurrence frequency and impact level of each type to obtain the quantitative value of operational safety risk. Based on the quantitative value of operational safety risks, mitigation measures are matched to generate risk control plans and complete the quantitative assessment of safety risks.

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