Method and apparatus for dynamic construction of 3D aeronautical charts for low altitude

Abstract

This invention discloses a method and apparatus for dynamically constructing three-dimensional aeronautical maps for low-altitude airspace. The method includes collecting data from specified multi-source data sources; constructing a three-dimensional base map based on these data sources; spatializing airspace constraints based on airspace constraint data from the multi-source data sources to obtain a constraint layer spatially registered with the three-dimensional base map; acquiring change reporting events sent by the aircraft; and calculating environmental change confidence based on real-time environmental perception data detected by the aircraft. By treating low-altitude airspace as a living map composed of a high-precision three-dimensional base map, an airspace constraint layer, and dynamic airspace attributes, a two-way empowerment mechanism between the aircraft and the map is established. The aircraft is both a user and a contributor to the map; the map stores both geographic information and multi-dimensional airspace constraints and management attributes, achieving the integration of map and airspace rules.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a method and apparatus for dynamically constructing three-dimensional aeronautical charts for low-altitude environments. Background Technology

[0002] The low-altitude economy has become a national strategic emerging industry, with low-altitude aircraft, represented by eVTOL and industrial drones, being rapidly applied in urban air traffic, logistics distribution, and emergency rescue scenarios. In the future, every city will need to build "skyways"—a three-dimensional high-precision aeronautical chart and airspace management system for low-altitude flight. Unlike traditional high-altitude civil aviation, low-altitude flight (below 1000 meters above the ground) has the following characteristics: The environment is highly complex: buildings, high-voltage lines, trees, and temporary construction facilities are densely distributed. Rapidly changing dynamics: frequent occurrences of vehicle movement, crowd gatherings, temporary traffic control, and sudden weather changes; The positioning conditions are stringent: GPS signals are weak in urban canyons, requiring reliance on environmental feature matching; Flight density is soaring: In the future, the same airspace will need to support thousands of aircraft operating simultaneously; Multiple security risks: In addition to physical obstacles, there are also non-physical airspace constraints such as radio interference zones, countermeasure zones, and electromagnetically sensitive areas; Three-dimensional high-precision aeronautical charts are widely recognized in the industry as the "digital foundation" of the low-altitude economy. They are not only the ultimate guarantee of flight safety (forming redundancy with onboard sensors), but also the scheduling brain of airspace management, and the efficiency engine for large-scale applications.

[0003] Based on the analysis of existing technical solutions, the current low-altitude three-dimensional aeronautical chart technologies and their existing problems are as follows: (1) Separation between map building and map use: In the existing scheme, map building is completed by professional surveying and mapping, and the aircraft is only a "user" rather than a "contributor". When the environment changes (such as the topping out of a new building, temporary construction, or sudden events), the aircraft cannot feed back the observed changes into the map, resulting in poor map timeliness.

[0004] (2) Separation of static and dynamic: Most three-dimensional aerial maps store static base maps (buildings, roads) and dynamic obstacles (other aircraft, temporary facilities) in layers. When planning the flight path, the aircraft needs to query both the static map and the dynamic perception results at the same time, which is computationally complex. Moreover, the dynamic layer data comes from a single source (relying only on its own sensors), and cannot know the dynamic changes beyond the line of sight.

[0005] (3) Lack of airspace constraint information: Existing aeronautical charts mainly store geographic information (topography, buildings) and lack the ability to express non-physical airspace constraints—such as no-fly zones, radio interference zones, areas where countermeasures are effective, and electromagnetically sensitive areas. Although these areas have no physical obstacles, aircraft may face risks such as communication interruption, navigation failure, and countermeasures after entering them, which are important factors affecting low-altitude flight safety.

[0006] (4) Separation of airspace attributes from map data: Existing aeronautical charts mainly store geographic information, while the core elements of airspace management—such as route width, altitude restrictions, capacity limits, and dynamic scheduling rules—are usually stored in a separate airspace management system. This separation leads to a disconnect between "maps" and "traffic rules," making it impossible for aircraft to consider these factors in an integrated manner when planning their routes.

[0007] (5) High update cost and delay: Traditional 3D map updates require drone reflight or ground data collection, with a cycle of several weeks to several months, which cannot cope with rapid changes in the low-altitude environment (such as temporary flight restrictions for concerts, sudden strong winds, and temporary construction).

[0008] (6) Lack of multi-aircraft collaborative verification mechanism: The perception of a single aircraft may have errors or false alarms. Existing technology lacks a mechanism for updating the map after cross-verification by multiple aircraft, making it difficult to balance the timeliness and reliability of the update. Summary of the Invention

[0009] The main objective of this invention is to provide a method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations, in order to address the shortcomings of related technologies.

[0010] To achieve the above objectives, according to a first aspect of the present invention, a method for dynamically constructing a three-dimensional aeronautical chart for low-altitude environments is provided, comprising: collecting data from a specified multi-source data source; constructing a three-dimensional base map based on geographic information from the multi-source data source; spatializing the airspace constraints based on airspace constraint data from the multi-source data source to obtain a constraint layer spatially registered with the three-dimensional base map, wherein the static three-dimensional base map and the constraint layer constitute the three-dimensional aeronautical chart; acquiring change reporting events sent by aircraft; performing multi-aircraft collaborative verification based on the change reporting events; and updating the three-dimensional aeronautical chart after verifying that the environmental changes are real; wherein before the change reporting event is triggered, the aircraft calculates the confidence level of the environmental change, and triggers the change reporting event when the confidence level of the environmental change exceeds a preset threshold; the confidence level of the environmental change is calculated based on environmental perception data detected by the aircraft in real time.

[0011] According to a second aspect of the present invention, a dynamic three-dimensional aeronautical chart construction device for low-altitude environments is provided, comprising: a data acquisition unit for acquiring data from specified multi-source data sources and constructing a three-dimensional base map based on geographic information from the multi-source data sources; a construction unit for spatializing airspace constraints based on airspace constraint data from the multi-source data sources to obtain a constraint layer spatially registered with the three-dimensional base map, wherein the static three-dimensional base map and the constraint layer constitute the three-dimensional aeronautical chart; and an update unit for acquiring change reporting events sent by aircraft, wherein multi-aircraft collaborative verification is performed based on the change reporting events, and the three-dimensional aeronautical chart is updated after verifying that the environmental changes are real, wherein before the change reporting events are triggered, the aircraft calculates the environmental change confidence level, and triggers the change reporting events when the environmental change confidence level exceeds a preset threshold; the environmental change confidence level is calculated based on environmental perception data detected by the aircraft in real time.

[0012] According to a third aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing the computer to perform the method described in any one of the first aspects.

[0013] According to a fourth aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method described in any implementation of the first aspect.

[0014] This embodiment of a method for dynamically constructing a 3D aeronautical chart for low-altitude environments includes: collecting data from specified multi-source data sources; constructing a 3D base map based on geographic information from the multi-source data sources; spatializing airspace constraints based on airspace constraint data from the multi-source data sources to obtain a constraint layer spatially registered with the 3D base map, wherein the static 3D base map and the constraint layer constitute the 3D aeronautical chart; acquiring change reporting events sent by the aircraft; performing multi-aircraft collaborative verification based on the change reporting events; and updating the 3D aeronautical chart after verifying that the environmental changes are real. Before the change reporting event is triggered, the aircraft calculates the confidence level of the environmental change, and triggers the change reporting event when the confidence level exceeds a preset threshold. The environmental change confidence level is calculated based on the environmental perception data detected by the aircraft in real time. By treating low-altitude airspace as a "living map" composed of a three-dimensional high-precision base map, airspace constraint layers (no-fly zones / interference zones / countermeasure zones, etc.), and dynamic airspace attributes, a two-way empowerment mechanism between aircraft and maps is established. The aircraft is both a user and a contributor to the map; the map stores both geographic information and multi-dimensional airspace constraints and management attributes, realizing the integration of maps and airspace rules, thereby solving the technical problems existing in the current technology. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the method for dynamically constructing three-dimensional aeronautical charts for low-altitude flight according to an embodiment of the present invention; Figure 2 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] The architecture applicable to this method can be divided into five layers: data acquisition layer, map processing layer, collaboration management layer, application service layer, and aircraft terminal layer, forming a complete closed loop from data acquisition to application services.

[0021] Data acquisition layer: responsible for aggregating various data sources required to construct 3D aeronautical charts.

[0022] Map Processing Layer: The map processing layer is the core processing layer, responsible for fusing multi-source data into usable 3D aeronautical charts. It includes the following sub-modules: A 3D base map construction module, based on aerial triangulation and AI-assisted 3D reconstruction technologies, generates a high-precision 3D base map at the city level, with an accuracy of three to five centimeters, including static geographic features such as terrain, buildings, roads, and vegetation. A multi-dimensional airspace constraint layer module spatializes and stereoscopically processes non-physical airspace constraints such as no-fly zones, interference zones, and countermeasure zones, generating constraint layers that are spatially registered with the base map. Each constraint element is associated with attributes such as type (no-fly, interference, or countermeasure), intensity (e.g., interference power), effective range, and effective time period. An airspace attribute association module associates dynamic management information such as route attributes (width, speed limit, altitude layer), capacity attributes (maximum capacity, current occupancy), scheduling rules (priority, interval requirements), and risk levels on 3D spatial voxels (grid cells). A change detection and verification engine receives perception data reported by aircraft, calculates the confidence level of environmental changes, performs multi-aircraft collaborative verification, and triggers map updates after determining whether the changes are real. The confidence management module maintains a confidence coefficient for each map feature, with a value range of 0-1. The coefficient increases with the number of verifications and decreases over time, enabling self-assessment of map quality.

[0023] Collaborative Management Layer: This layer is responsible for dynamic collaborative airspace management based on maps. It includes a route dynamic optimization module, which automatically assesses the impact and generates alternative routes when new obstacles or airspace constraints are added to a region. A capacity scheduling module monitors airspace occupancy in real time for each flight segment and dynamically adjusts capacity limits and scheduling priorities. A temporary control conversion module automatically maps temporary control instructions issued by management to map updates. A conflict early warning module detects conflicts between aircraft planned paths and airspace constraints and issues early warnings.

[0024] Application Service Layer: The application service layer provides map services to aircraft and ground users. Intelligent route planning is integrated, considering 3D base maps, multi-dimensional constraint layers, dynamically updated layers, and airspace attributes to output the optimal flight path. Real-time map services push real-time updated map data and risk warnings to aircraft. Management decision support provides airspace management departments with visual monitoring, statistical analysis, and audit traceability functions.

[0025] At the aircraft end layer, the aircraft acts as both a user and contributor to the map, equipped with multi-source sensing modules, including visual cameras, LiDAR, spectrum analyzers, weather sensors, and a GNSS inertial navigation system. A local map caching module stores map data of the current location's surroundings, supporting offline navigation. A change detection module compares the sensing data with the local map in real time, triggering a report upon detecting anomalies. The path planning and execution module executes the flight mission based on the latest map data.

[0026] The overall architecture forms a closed-loop ecosystem of "use-as-you-go, collect-as-you-go, update-as-you-go": the data acquisition layer gathers data from multiple sources, the map processing layer completes the fusion and update, the collaborative management layer achieves dynamic optimization, the application service layer provides services to the outside world, and the aircraft terminal layer both uses the map and contributes data.

[0027] The method execution entity applicable to this embodiment can be located in the map processing layer. According to an embodiment of the present invention, a method is provided, such as... Figure 1 As shown, steps 101 to 103 are included below: Step 101: Collect data from specified multi-source data sources and construct a 3D base map based on the geographic information in the multi-source data sources.

[0028] In this step, various data sources required for constructing the 3D aeronautical chart are collected. These data sources include geographic information data, airspace constraint data, aircraft crowdsourced data, and meteorological data. The geographic information data is used to construct a static base map. The airspace constraint data includes no-fly zone data, interference zone data, and countermeasure zone data. The aircraft crowdsourced data includes perception data, spectrum monitoring data, and attitude information collected by the multi-source sensors on board the aircraft.

[0029] For example, regarding geographic information data: remote sensing imagery, oblique photography, laser point clouds, and publicly available GIS data are collected through professional surveying and satellite remote sensing methods to construct static base maps of terrain, buildings, roads, vegetation, etc. Regarding no-fly zone data: official data from the Civil Aviation Administration, military control zones, airport airspace protection zones, and government-controlled protection zones are accessed and collected through official interfaces or manual input. Regarding interference zone data: radio monitoring data, known interference source locations, and electromagnetic environment maps are acquired, with sources including ground monitoring stations and aircraft crowdsourced data collection. Regarding countermeasure zone data: information on agency deployments, countermeasure equipment locations, and effective ranges can be collected and maintained through management input and dynamic updates. Other data: In addition, aircraft crowdsourced data is acquired through real-time transmission from aircraft, including visual radar perception data, spectrum monitoring data, and attitude information; meteorological data is accessed through meteorological department interfaces from ground meteorological stations and radiosonde data.

[0030] When constructing a 3D base map, technologies such as aerial triangulation and AI-assisted 3D reconstruction can be used to generate a high-precision 3D base map of a city, which includes static geographic elements such as terrain, buildings, roads, and vegetation.

[0031] Step 102: Spatialize the spatial constraints based on the spatial constraint data from the multi-source data source to obtain a constraint layer that is spatially registered with the three-dimensional base map. The static three-dimensional base map and the constraint layer together form the three-dimensional aeronautical chart.

[0032] In this step, non-physical airspace constraints such as no-fly zones, interference zones, and countermeasure zones are spatialized and rendered in three dimensions to generate a constraint layer that is spatially registered with the base map. Each constraint element is associated with attributes such as type (no-fly, interference, or countermeasure), intensity (e.g., interference power), effective range, and effective time period.

[0033] Dynamic management information such as route attributes (width, speed limit, altitude layer), capacity attributes (maximum capacity, current occupancy), scheduling rules (priority, interval requirements), and risk levels are associated on three-dimensional space voxels (mesh cells).

[0034] As an optional implementation of this embodiment, spatial processing of non-physical airspace constraints is performed based on airspace constraint data from multiple data sources. This includes: converting the set two-dimensional planar constraint information into three-dimensional constraints, wherein, during the conversion, a height range is defined according to the constraint type; dividing the three-dimensional space into a voxel grid, wherein each voxel stores different types of fields; different types of fields include fields used to constrain voxels; when the same voxel contains multiple constraint field types, the multiple constraints of the same voxel are fused according to the risk level priority, so that when planning the path for the aircraft, the voxels corresponding to the constraints are avoided according to priority.

[0035] In this optional implementation, traditional 3D aeronautical charts mainly express physical geographic information, including terrain and buildings, but cannot express non-physical airspace constraints such as no-fly zones, interference zones, and countermeasure zones. This invention proposes a multi-dimensional airspace constraint layer to spatially and three-dimensionally express various airspace constraints.

[0036] In spatialization, two-dimensional planar constraints are stretched into three-dimensional constraints, and the altitude range is defined according to the constraint type. No-fly zones typically cover the entire altitude layer from zero to one kilometer; interference zones define the altitude of influence according to the type of interference source, with ground-based interference sources having a limited altitude of influence, while base station-type interference sources have a wider range of influence; countermeasure zones define the radius of action and altitude according to the performance of countermeasure equipment.

[0037] Furthermore, the three-dimensional space is divided into a fixed-size voxel grid, with a recommended size of 5 meters by 5 meters horizontally and 2 meters vertically. Each voxel stores the following information structures: static geographic fields, such as terrain elevation, building height, and surface type; airspace constraint fields, including constraint type (no-fly zone, interference, or countermeasure), intensity value, and effective time period; dynamic attribute fields, including temporary obstacles, temporary constraints, and confidence level; airspace management fields, including airway number, speed limit, current capacity, and maximum capacity; and statistical information fields, including last update time and verification count.

[0038] A single voxel may simultaneously contain multiple constraints, such as an area that is both a no-fly zone and subject to radio interference. Consolidation is performed according to risk level priority, from highest to lowest: no-fly zones are the highest priority, representing absolute prohibition; next are countermeasure zones, where there is a risk of being countermeasured; then are interference zones, where communication may be disrupted; and finally, physical obstacles, where there is a risk of collision. During aircraft path planning, these constraints are avoided sequentially according to their priority.

[0039] Step 103: Obtain the change reporting event sent by the aircraft. Based on the change reporting event, perform multi-aircraft collaborative verification. After verifying that the environmental change is real, update the three-dimensional flight map. Before the change reporting event is triggered, the aircraft calculates the confidence level of the environmental change. When the confidence level of the environmental change exceeds a preset threshold, the change reporting event is triggered. The confidence level of the environmental change is calculated based on the environmental perception data detected by the aircraft in real time.

[0040] In this step, existing technologies have shown that aircraft are merely users of maps, with map updates relying on professional surveying. This invention establishes a two-way empowerment mechanism, where the aircraft is both a user and a contributor to the map. During flight, the aircraft perceives the environment in real time, proactively reports discrepancies between the map and reality, and participates in the continuous updating of the map.

[0041] As an optional implementation of this embodiment, calculating the confidence level of environmental change based on the environmental perception data detected by the aircraft in real time includes: comparing the environmental perception data with the data of the locally cached three-dimensional aeronautical chart to calculate the confidence level of environmental change, wherein the confidence level of environmental change is determined by the weighted sum of geometric difference components, semantic difference components, radio environment difference components and time factor components.

[0042] As an optional implementation of this embodiment, performing multi-aircraft collaborative verification for environmental change confidence includes: when a change reporting event is triggered, acquiring specified data uploaded by the aircraft reporting the change reporting event; updating the locally cached 3D aeronautical chart based on the specified data, and marking the area indicated by the change reporting event as pending verification to avoid the same aircraft repeatedly reporting the same change; detecting whether the verification count corresponding to the area indicated by the change reporting event has reached a threshold; and verifying that the environmental change is true when the verification count reaches the threshold.

[0043] In this optional implementation, the process involves three stages. The first stage is environmental change detection. During flight, the aircraft compares the sensor data with the locally cached map in real time to calculate the confidence level of environmental changes.

[0044] The confidence score is calculated as a weighted sum of four components: geometric difference, semantic difference, radio environment difference, and time factor. Geometric difference refers to the normalized difference between point cloud elevation and map elevation; semantic difference refers to the matching degree between perceived semantic categories and map labels, ranging from 0 to 1; radio environment difference refers to the normalized difference between measured interference intensity and map label intensity; and the time factor is the ratio of the map's unupdated duration to the aging threshold, with a maximum value of 1. The four weighting coefficients are adjustable, with a default value of 0.25. When the confidence score exceeds a preset threshold (a threshold of 0.3 is recommended), it indicates a potential environmental change in the area, triggering a report.

[0045] The second step is the change reporting protocol. The aircraft packages and uploads the following information to the map server: location coordinates, including longitude, latitude, and altitude; change type, such as new obstacle, obstacle removal, new interference source, interference disappearance, or temporary control; confidence value and its components; raw sensing data, including point cloud fragments, image fragments, and spectrum data; and aircraft identification and timestamp.

[0046] The third step is updating the local map cache. After an aircraft reports a change, the local cache map is immediately updated, marking the area as "pending verification" to prevent the same aircraft from repeatedly reporting the same change. Considering the limited computing resources of aircraft, the change detection algorithm adopts a lightweight design. Geometric difference detection uses a simple difference calculation between point cloud elevation and map elevation; semantic difference detection is based on a pre-trained lightweight semantic segmentation model, such as a MobileNet-like network; and spectrum detection uses an energy detection method, eliminating the need for complex demodulation.

[0047] The perception of a single aircraft may contain errors due to factors such as sensor noise, changes in lighting conditions, and blind spots. Updating the map based solely on a single aircraft's report could lead to erroneous updates. This invention introduces a multi-aircraft collaborative verification mechanism, whereby the change is confirmed and the map is updated only when multiple different aircraft independently observe consistent changes.

[0048] Upon receiving a change report, the corresponding voxel is marked as "pending verification," and the initial reporting time, the list of reporting aircraft, and the cumulative verification count are recorded. A verification count threshold is set, for example, three times. The verification count increases when the following conditions are met: the reporting aircraft have different identifiers, excluding duplicate reports from the same aircraft; the reported location is within the voxel spatial neighborhood, considering the allowable range of positioning error; the change type is consistent; and the reporting confidence level is not lower than 0.6, with low-confidence reports not counted in the verification. When the verification count reaches the threshold, the change is deemed genuine, and a map update is performed. For newly added permanent obstacles, the static geographic layer is updated, with an initial confidence level set to 0.9. For temporary obstacles, a dynamic layer label is added, and the estimated duration is set, with an initial confidence level set to 0.85. For obstacle removal, the obstacle is cleared and marked as confirmed, with an initial confidence level set to 0.95. For new interference sources, the interference area layer is updated, and a positioning estimate is performed, with an initial confidence level set to 0.8. If the interference disappears, clear the interference area or lower the confidence level. The initial confidence level is set to 0.9.

[0049] As an optional implementation of this embodiment, if the verification count does not reach the threshold after the duration of the state to be verified exceeds the duration threshold, the state to be verified is cleared.

[0050] If a region enters a "pending verification" state and the verification count does not reach the threshold within the timeout period (a two-hour timeout threshold is recommended), the state will be automatically cleared, and observation data from unreporting aircraft will be discarded to avoid information accumulation. The multi-aircraft verification mechanism naturally suppresses false alarms from single aircraft; if only one aircraft reports a change while other aircraft do not confirm it, the change will not be included in the map.

[0051] As an optional implementation of this embodiment, a confidence coefficient is assigned to a specified element in the three-dimensional aeronautical chart to quantify the accuracy and timeliness of each element; the confidence coefficient is updated according to a preset rule to drive the self-maintenance of the element based on the confidence coefficient.

[0052] As an optional implementation of this embodiment, when the three-dimensional aeronautical chart is updated for the first time, the initial confidence level is set to k1 according to the change type; the confidence level increases by p1 for each additional verification count, with an upper limit of 1; when the verification count reaches m times or more, the confidence level is additionally increased by p1, with an upper limit of 1; the confidence level of the unupdated area decays exponentially over time; when the confidence level is lower than the review threshold k2, an active review event is triggered, and the review task is added to the task queue to guide recently passed aircraft to observe the area; and / or, the specified constraint decay coefficient is set to 0.

[0053] In this optional implementation, map information becomes outdated over time, requiring the establishment of a confidence management mechanism to quantify the accuracy and timeliness of each map element and drive map self-maintenance based on confidence.

[0054] For example, each map feature, including voxels, constraints, and flight segments, is associated with a confidence coefficient ranging from 0 to 1. A confidence coefficient of 0.8 or higher is considered high confidence and can be used as the primary basis for route planning; a confidence coefficient between 0.3 and 0.8 is considered medium confidence and can be used for reference, with the suggestion to add safety redundancy; a confidence coefficient below 0.3 is considered low confidence, and the information may be outdated, requiring caution when using it.

[0055] For the first update, the initial value is set to 0.8 to 0.95, depending on the update type. When adding a new validation, the confidence level increases by 0.05 for each validation count, up to a maximum of 1. For multi-machine consistent observations, the confidence level increases by an additional 0.1 when the validation count reaches 3 or more, up to a maximum of 1.

[0056] For regions that have not been updated, the confidence level decays exponentially over time, using the following formula: Current confidence level = initial confidence level × negative decay coefficient of natural constant × time difference raised to the power of 1. The decay coefficient varies depending on the feature type: permanent geographic features decay slowly, with a coefficient of 0.01 per day; temporary obstacles decay quickly, with a coefficient of 0.1 per day; interference zones have a decay coefficient of 0.05 per day; no-fly zones use official data and have a decay coefficient of 0, meaning no decay. The time difference is the number of days since the last update.

[0057] When the confidence level of a certain area falls below the re-examination threshold, it is recommended that the re-examination threshold be set to 0.3, and the system will trigger an active re-examination. Specifically, the re-examination task will be added to the task queue, guiding recently passed aircraft to focus on observing the area. If no aircraft passes through, a special mapping task will be arranged.

[0058] For definitive information such as no-fly zones released by official sources, the attenuation factor is set to 0, and the confidence level is always 1, unless the official source releases an update or revocation.

[0059] The confidence lifecycle is as follows: when a temporary obstacle is discovered and updated, the confidence is 0.85; after 7 days without new verification, it naturally decays to 0.42; after 10 days, it decays to 0.31, which is below the threshold and triggers a re-examination; after 12 days, the aircraft re-examines and finds that the obstacle has been removed, updates the status to "removed", and resets the confidence to 0.95.

[0060] In this embodiment, the multidimensional airspace constraint spatialization expression mechanism integrates no-fly zones, interference zones, countermeasure zones, and geographical baselines. Figure 1 Integrated storage provides the data foundation for subsequent mechanisms. The aircraft-map bidirectional empowerment mechanism enables aircraft to perceive, detect changes, and report to the server in real time, ensuring continuous data input. The multi-aircraft collaborative verification mechanism confirms changes through independent observation and cross-verification by multiple aircraft, guaranteeing data reliability. The confidence management and aging mechanism increases confidence after updates, decays over time, and proactively triggers reviews, forming a closed loop.

[0061] Four mechanisms work in synergy, with the "temporary deployment of countermeasures equipment" scenario as an example to illustrate its interaction process. In the multi-dimensional airspace constraint expression stage, public security organs input countermeasures equipment information, including location, radius, and time period, through the management terminal. The system automatically generates a countermeasures zone layer and updates the constraint type of the affected voxels to countermeasures zone. In the aircraft-map bidirectional empowerment stage, legitimate aircraft acquire the latest map before takeoff, and path planning automatically avoids countermeasures zones. If an aircraft inadvertently enters a countermeasures zone, the onboard spectrum monitoring module detects interference signals and triggers a change report. In the multi-aircraft collaborative verification stage, the server receives countermeasures zone interference signals reported by multiple aircraft. After the verification count reaches a threshold, the existence of the countermeasures zone is confirmed, and the confidence level is increased. In the confidence level management stage, the confidence level of the countermeasures zone increases to 1 with multi-aircraft verification. If there is no observation for a long period after the countermeasures equipment is removed, the confidence level gradually decays, triggering a re-examination task. Through the closed-loop collaboration of these four mechanisms, the dynamic construction, real-time updating, and adaptive maintenance of the "Skyway" three-dimensional aeronautical map are achieved.

[0062] For example, consider a low-altitude logistics distribution demonstration zone in a certain city, covering an area of ​​approximately 50 square kilometers, with an average of 200 drone sorties per day: (1) Implementation preparation First, a 3D aeronautical map base was constructed for the demonstration area. Approximately 20,000 images were collected using oblique photography by UAVs, and after aerial triangulation and AI 3D reconstruction, a 3D base map with an accuracy of 3-5 cm was generated, including geographical features such as buildings, roads, and power facilities. Simultaneously, airspace constraint data within the demonstration area was collected: the no-fly zone included the airport's clear zone and temporary military training airspace; the interference zone included three known interference sources, such as radio transmission towers and communication base stations; and the countermeasure zone included two fixed countermeasure devices deployed by public security authorities. The 3D space was divided into a 5m × 5m × 2m voxel grid, and each voxel was assigned static geographical attributes, airspace constraint attributes, and an initial confidence level. Ten delivery routes were pre-planned, associated with attributes such as a route width of 50 meters, a speed limit of 36-54 km / h, and a capacity of 30 flights per hour.

[0063] (2) Scenario Implementation Scenario 1: Handling Temporary Construction Obstacles At 9:00 AM, a tower crane, 85 meters high, was erected at an intersection. Drone A flew to a distance of 350 meters from the crane. Its onboard radar detected an anomaly in the point cloud. The change detection module calculated a geometric difference of 0.87 and a semantic difference of 0.82, with a combined confidence score of 0.72, exceeding the threshold and triggering a report. Drone A uploaded the crane's location to the server, marking the area as pending verification. Subsequently, Drones B and C detected the same crane from different directions within ten minutes and reported it, reaching a verification count of three times. The server determined the change was real and updated the map: marking the area's dynamic layer as a temporary obstacle, with a confidence score of 0.9, adjusting the risk level to high risk, and automatically generating two alternative detour routes. Subsequent drones will automatically select the detour route upon takeoff, achieving a single detection benefiting the entire network.

[0064] Scenario 2: Deployment of Mobile Countermeasure Equipment Before the concert, security personnel entered information about three mobile countermeasure devices into the management system, including their location, a 400-meter radius, and the time period from 18:00 to 23:00. The system automatically mapped the countermeasure zone to voxels, added airspace constraint markers, and set a confidence level of 1.0. The flight path dynamic optimization module detected three flight paths crossing the countermeasure zone, automatically generated detour plans or temporarily closed the flight paths, and completed all adjustments before 17:40. All drone delivery missions that evening successfully avoided the countermeasure zone, and no unauthorized incursions occurred.

[0065] Scenario 3: Detection and Location of Radio Interference Sources A company activated its high-frequency welding equipment, generating strong interference in the 2.4GHz band. Drone G detected interference of -35dBm and a radio environment difference of 0.85 while flying over it, triggering a report. Subsequently, drones H and I detected the same interference from different directions, verifying a count of 3 times. The system estimated the interference source location to be a factory area based on a triangulation algorithm, updated the interference zone layer, and established a confidence level of 0.8. The subsequent 23 drones successfully avoided the interference or switched communication bands in advance thanks to system warnings. After on-site verification by the radio management department, the interference source was confirmed, and the confidence level was raised to 1.0.

[0066] Scenario 4: Construction of a Temporary No-Fly Zone The marathon requires a temporary no-fly zone along the race route from 07:00 to 13:00. The organizing committee issues the no-fly zone instruction via an interface. The system automatically maps voxels and cuts off seven crossing routes, generating detour plans. The capacity scheduling module reassigns the affected 45 flights to alternative routes, with some flights shifted to other time windows. No aircraft inadvertently entered the no-fly zone during the no-fly period, and the system automatically restores the routes after the race.

[0067] Compared with existing technologies, this method has achieved several improvements in technical solutions, core mechanisms, and application effects, as detailed below: 1. Improvements to the map update mechanism In existing technologies, the construction of 3D aeronautical charts relies on periodic data collection by professional surveying and mapping units, with update cycles ranging from several weeks to several months, which cannot cope with the rapid changes in the low-altitude environment. Aircraft only act as users of the map and cannot contribute perception data.

[0068] This embodiment establishes a two-way empowerment mechanism between the aircraft and the map, using the aircraft as a distributed sensor to perceive environmental changes in real time during flight and actively report them. Through a multi-aircraft collaborative verification mechanism, the map is only confirmed and updated when multiple different aircraft independently observe consistent changes, ensuring both timely updates and effectively suppressing false alarms from a single aircraft. The time from the occurrence of a change to the completion of the map update is reduced to the minute level, which is more than two orders of magnitude faster than existing technologies.

[0069] 2. Improvement of Spatial Constraint Representation Existing 3D aeronautical charts primarily store physical geographic information such as terrain and buildings, but cannot represent non-physical airspace constraints such as no-fly zones, interference zones, and countermeasure zones. Aircraft path planning requires separate queries to both maps and airspace management systems, resulting in fragmented information and delayed response times.

[0070] This embodiment proposes a multi-dimensional airspace constraint layer, which integrates airspace constraints such as no-fly zones, interference zones, and countermeasure zones with the geographic background. Figure 1Integrated storage. Each voxel is simultaneously associated with static geographic attributes, airspace constraint attributes, route attributes, and dynamically updated information, achieving "one map containing everything." When planning aircraft paths, physical obstacles and multi-dimensional airspace constraints can be considered in an integrated manner, greatly simplifying the system architecture.

[0071] 3. Improvements in map timeliness management Existing technologies lack a quantitative assessment mechanism for the obsolescence of map information, making it impossible to proactively identify areas that need updating. This results in severe distortion of information after maps have not been updated for a long time, or ineffective updates being made blindly.

[0072] This embodiment establishes a confidence management and aging mechanism, maintaining a confidence coefficient for each map element. The coefficient increases with the number of multi-aircraft verifications and decreases over time. When the confidence level falls below a threshold, an active review task is automatically triggered, guiding the aircraft to focus on key observations or arranging specialized mapping. This enables self-assessment and adaptive maintenance of map quality, ensuring that aeronautical charts always maintain good timeliness.

[0073] 4. Improvements in airspace collaborative management In existing technologies, airspace management information such as route attributes, capacity limits, and scheduling rules are stored separately from map data, and temporary control instructions need to be entered manually, resulting in slow response times and a high risk of errors.

[0074] This embodiment integrates airspace attributes with map data, directly linking airway width, speed limits, capacity, and scheduling rules to voxels. After temporary control instructions are issued via the interface, the system automatically completes spatial mapping, voxel updates, and generates airway closure or detour plans without manual intervention, achieving a response time within minutes. Simultaneously, the capacity scheduling module can monitor airspace occupancy in real time, dynamically adjusting capacity limits and scheduling priorities to achieve refined airspace collaborative management.

[0075] Through the above improvements, the present invention achieves the following comprehensive technical effects: In terms of safety, the aircraft can be aware of physical obstacles, communication interference, and countermeasure risks beyond visual range in advance, shifting from passive detection to active avoidance. During the demonstration operation, the system issued warnings for a total of 14 temporary obstacles, countermeasure zones, and interference sources, involving more than 320 aircraft sorties, and the accident rate dropped from 5 incidents before deployment to 0.

[0076] In terms of operational efficiency, since the aircraft does not need to repeatedly detect risks already detected by other aircraft, each avoidance maneuver saves an average of 25 seconds of flight time and a cumulative flight distance of 120 kilometers. Dynamic capacity management increases airspace utilization by more than 30%.

[0077] In terms of management efficiency, the time from issuing temporary control orders to map activation has been reduced from hours of manual processing to minutes of system automation, increasing management efficiency by more than five times. The frequency of professional surveying has been reduced from once per quarter to once per year, and maintenance costs have been reduced by 60%.

[0078] By unifying geographic information, airspace constraints, route attributes, and dynamic updates into a 3D aeronautical chart model, aircraft path planning can read all constraints in one integrated manner, simplifying the design of aircraft-side systems and lowering the access threshold.

[0079] In summary, this invention has achieved significant technical improvements in the updating mechanism, constraint expression, timeliness management, and collaborative scheduling of low-altitude three-dimensional aeronautical charts, providing a reliable, efficient, and evolvable digital foundation for the large-scale application of the low-altitude economy.

[0080] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0081] According to an embodiment of the present invention, a dynamic construction system for low-altitude 3D aeronautical charts is also provided, comprising: collecting data from specified multi-source data sources; constructing a 3D base map based on geographic information from the multi-source data sources; spatializing airspace constraints based on airspace constraint data from the multi-source data sources to obtain a constraint layer spatially registered with the 3D base map, wherein the static 3D base map and the constraint layer constitute the 3D aeronautical chart; acquiring change reporting events sent by aircraft, wherein multi-aircraft collaborative verification is performed based on the change reporting events, and the 3D aeronautical chart is updated after verifying that the environmental change is real; wherein before the change reporting event is triggered, the aircraft calculates the environmental change confidence level, and triggers the change reporting event when the environmental change confidence level exceeds a preset threshold; the environmental change confidence level is calculated based on environmental perception data detected by the aircraft in real time.

[0082] According to embodiments of the present invention, the present invention also provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the methods described in any of the above embodiments.

[0083] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to perform the methods described in any of the above embodiments when executed.

[0084] According to embodiments of the present invention, the present invention also provides a computer program product that, when executed by a processor, can implement the methods described in any of the above embodiments.

[0085] Figure 2 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0086] like Figure 2 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0087] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0088] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed.

[0089] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0091] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Claims

1. A method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations, characterized in that, include: Collect data from specified multi-source data sources and construct a 3D base map based on these data sources; Spatial constraints are spatialized based on spatial constraint data from multiple data sources to obtain a constraint layer that is spatially registered with the three-dimensional base map. The static three-dimensional base map and the constraint layer together constitute the three-dimensional aeronautical chart. The system acquires change reporting events sent by the aircraft, performs multi-aircraft collaborative verification based on these events, and updates the 3D aeronautical chart after verifying that the environmental changes are real. Before the change reporting event is triggered, the aircraft calculates the confidence level of the environmental changes, and triggers the change reporting event when the confidence level exceeds a preset threshold. The confidence level of the environmental changes is calculated based on the environmental perception data detected by the aircraft in real time.

2. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude environments according to claim 1, characterized in that, Spatialization processing of non-physical spatial constraints based on spatial constraint data from multiple data sources includes: The set two-dimensional planar constraint information is converted into three-dimensional solid constraints, wherein the height range is defined according to the constraint type during the conversion; The three-dimensional space is divided into a voxel mesh, where each voxel stores different types of fields; the different types of fields include fields used to constrain the voxels; When the same voxel contains multiple constraint field types, the multiple constraints of the same voxel are merged according to the risk level priority, so that when planning the path for the aircraft, the voxel corresponding to the constraint is avoided according to the priority.

3. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude environments according to claim 1, characterized in that, Calculating the confidence level of environmental changes based on real-time environmental perception data detected by the aircraft includes: The environmental perception data is compared with the data of the locally cached 3D aeronautical chart to calculate the confidence level of environmental change, wherein the confidence level of environmental change is determined by the weighted sum of geometric difference components, semantic difference components, radio environment difference components and time factor components.

4. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations according to claim 3, characterized in that, The method also includes performing multi-machine collaborative verification for confidence levels regarding environmental changes: When a change reporting event is triggered, the specified data uploaded by the aircraft reporting the change reporting event is obtained; The local cached 3D aeronautical chart is updated based on the specified data, and the area indicated by the change reporting event is marked as pending verification to avoid the same aircraft repeatedly reporting the same change; Check whether the verification count corresponding to the area indicated by the change reporting event has reached the threshold; When the verification count reaches the threshold, the environmental change is verified to be true.

5. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations according to claim 4, characterized in that, If the verification count does not reach the threshold after the duration of the state to be verified exceeds the duration threshold, then the state to be verified is cleared.

6. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations according to claim 1, characterized in that, Specific elements in the three-dimensional aeronautical chart are assigned confidence coefficients to quantify the accuracy and timeliness of each element. The confidence coefficients are updated according to preset rules to drive the self-maintenance of elements based on the confidence coefficients.

7. The method for dynamically constructing three-dimensional aeronautical charts for low-altitude operations according to claim 6, characterized in that, The confidence coefficient is updated according to a preset rule, including: When the 3D aeronautical chart is updated for the first time, the initial confidence level is set to k1 according to the change type; for each additional verification count, the confidence level increases by p1, with an upper limit of 1; when the verification count reaches m times or more, the confidence level increases by an additional p1, with an upper limit of 1. The confidence level of unupdated regions decays exponentially over time; When the confidence level is lower than the review threshold k2, an active review event is triggered, and the review task is added to the task queue to guide recently passed aircraft to observe the area. And / or, set the specified constraint attenuation coefficient to 0.

8. A dynamic three-dimensional aeronautical chart construction device for low-altitude operations, characterized in that, include: The data acquisition unit collects data from specified multi-source data sources and constructs a 3D base map based on the geographic information in the multi-source data sources. The construction unit performs spatialization processing on the spatial constraints based on the spatial constraints data from multiple data sources to obtain a constraint layer that is spatially registered with the three-dimensional base map. The static three-dimensional base map and the constraint layer together constitute the three-dimensional aeronautical chart. The update unit acquires change reporting events sent by the aircraft, performs multi-aircraft collaborative verification based on the change reporting events, and updates the three-dimensional flight map after verifying that the environmental changes are real. Before the change reporting event is triggered, the aircraft calculates the environmental change confidence level, and triggers the change reporting event when the environmental change confidence level exceeds a preset threshold. The environmental change confidence level is calculated based on the environmental perception data detected by the aircraft in real time.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the at least one processor to perform the method according to any one of claims 1-7.

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