Low-altitude airspace operation risk index classification calculation and comprehensive evaluation method and system
By constructing a multi-dimensional risk indicator system and determining weights using the entropy weight method, the multi-dimensional problem of low-altitude UAV operation risk assessment was solved, and the accurate quantification and scientific management of low-altitude airspace operation risks were achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
- Filing Date
- 2026-04-16
- Publication Date
- 2026-05-29
AI Technical Summary
Existing risk assessment methods for low-altitude unmanned aerial vehicle (UAV) operations fail to fully consider multiple factors such as traffic density, airspace saturation, and airspace structural complexity, resulting in assessment results that cannot scientifically reflect the risks of low-altitude operations and affecting airspace traffic allocation and route planning.
A multi-dimensional risk indicator system is adopted, and objective weights are determined by the entropy weight method to construct a low-altitude airspace operation risk assessment model, including low-altitude airspace grid unit division, risk assessment indicator design, indicator standardization, and risk value calculation.
It has enabled the precise quantification of low-altitude airspace operation risks, provided a scientific basis for airspace management decisions, and improved the safety and efficiency of low-altitude airspace operation.
Smart Images
Figure CN122114651A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of airspace operation assessment technology, specifically relating to a method and system for classifying, calculating, and comprehensively assessing low-altitude airspace operation risk indicators. Background Technology
[0002] The large-scale operation of low-altitude drones has made the airspace operating environment increasingly complex, and operational risk assessment is crucial to ensuring the safe and efficient operation of the airspace. Existing risk assessment methods are mostly designed for high-altitude civil aviation, and their indicator systems are not adapted to the characteristics of low-altitude drone operations, and the determination of indicator weights lacks objectivity.
[0003] Currently, low-altitude risk assessments often employ single indicators or subjective weighting methods, failing to fully consider the comprehensive impact of multiple dimensions such as traffic density, airspace saturation, and airspace structural complexity. This results in assessments that cannot fully reflect the operational risks of low-altitude airspace, making it difficult to provide a scientific basis for airspace traffic allocation and route planning, and consequently leading to problems such as airspace congestion and increased conflict risks.
[0004] Current airspace operation risk assessments primarily target high-altitude public transport aviation, comprehensively evaluating airspace operation risk status from aspects such as airspace structure, traffic conditions, and conflict risks. Existing technologies lack expertise in constructing a multi-dimensional risk indicator system and objectively and comprehensively assessing low-altitude airspace risks. There is an urgent need to develop a scientific risk assessment methodology to achieve accurate quantification of low-altitude operation risks. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for classifying, calculating and comprehensively evaluating low-altitude airspace operation risk indicators. By designing multi-dimensional risk indicators, using the entropy weight method to determine objective weights, and constructing a comprehensive evaluation model, the invention achieves accurate quantification of low-altitude operation risks and provides support for airspace management decisions.
[0006] The first aspect of this application provides a method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, including the following steps: Step 1: Divide the low-altitude airspace into grid cells; Step 2: Construct and calculate the risk assessment indicator design system; Step 3: Standardization of indicators; Step 4: Determine the index weights based on the entropy weight method; Step 5: Calculate the low-altitude airspace operation risk value by combining the indicator value and weight.
[0007] In one embodiment of this application, step 1, the low-altitude airspace grid cell division, includes the following steps: Step 1.1: Perform three-dimensional raster subdivision of the low-altitude airspace with a raster granularity of 30-100m; Step 1.2: Encode the grid cells using the reverse "Z" pattern rule to complete grid positioning and indexing.
[0008] In one embodiment of this application, step 2, constructing and calculating the risk assessment index design system, includes the following steps: Step 2.1: Calculate the drone traffic density to characterize the density of drones within the grid; the formula for calculating the drone traffic density is as follows: ; In the formula Indicates traffic density. Indicates the number of drones. Indicates the spatial raster granularity; Step 2.2: Calculate the spatial saturation, which characterizes grid capacity matching and congestion risk; the formula for calculating the spatial saturation is as follows: ; In the formula Indicates spatial saturation. This indicates the number of drones in the airspace grid. Indicates spatial capacity; Step 2.3: Calculate the average cross-convergence coefficient, which characterizes the complexity of airspace structure and flight path intersections; the formula for calculating the average cross-convergence coefficient is as follows: ; In the formula Represents the average cross-convergence coefficient. This indicates the number of drones passing through the airspace grid cells. This indicates the number of drones within a specific airspace; the average cross-convergence coefficient is used to characterize airspace complexity. The higher the average cross-convergence coefficient, the more flight paths cross the airspace grid, meaning the higher the airspace complexity of the airspace grid.
[0009] In one embodiment of this application, step 3, the index standardization process includes: The indicators are distinguished as positive or negative, and normalization is performed using corresponding formulas to unify the dimensions of the indicators.
[0010] In one embodiment of this application, the step of performing normalization using the corresponding formula includes: Step 3.1, determine the indicator weights: Assumption This represents the number of low-altitude airspace grid cells. The initial decision matrix is determined by the number of risk assessment indicators for low-altitude airspace operations. for: ; In the formula This represents the corresponding indicator value; Step 3.2, indicator normalization processing: Normalize the values of each indicator, that is, convert the indicator values from absolute values to relative values. For positive indicators, the following formula is used for normalization: ; For negative indicators, the following formula is used for normalization: .
[0011] In one embodiment of this application, step 4, determining the index weights based on the entropy weight method, includes the following steps: Step 4.1: Calculate the proportion of each evaluation index value in each spatial raster to the total proportion of that evaluation index in all spatial raster cells. The specific formula is as follows: ; In the formula Indicates the proportion of the indicator; Step 4.2: Based on the indicator ratio The information entropy values of each indicator are calculated using the following formula: ; In the formula Indicates the first The information entropy of the item, and ,in ; Step 4.3: Calculate the weights of each item based on the information entropy, using the following formula: ; In the formula Indicates the first Weight of each indicator.
[0012] In one embodiment of this application, step 5, calculating the low-altitude airspace operation risk value by combining the index value and weight, includes: Based on the normalized index value and the entropy weight method, a risk assessment function is constructed to output the low-altitude airspace operation risk value; the formula of the risk assessment function is as follows: ; In the formula This indicates the risk value for operations in low-altitude airspace. , and These represent the normalized index values, , and Represents the indicator weight, and satisfies .
[0013] The second aspect of this application provides a system for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, including: The airspace raster division module is used for dividing low-altitude airspace raster units; The risk indicator calculation module is used to construct and calculate the risk assessment indicator design system; The indicator standardization module is used for indicator standardization processing; The entropy weighting module determines the weights of indicators based on the entropy weighting method. The risk assessment module is used to calculate the risk value of low-altitude airspace operation by combining indicator values and weights.
[0014] A third aspect of this application provides an electronic device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators as described above.
[0015] A fourth aspect of this application provides a storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the storage medium to perform the low-altitude airspace operation risk index classification calculation and comprehensive evaluation method as described above.
[0016] The beneficial effects of this invention are: This invention proposes a method and system for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators. It designs a multi-dimensional risk indicator system encompassing traffic density, airspace saturation, and average convergence coefficient, employing the entropy weight method to determine objective weights, thus avoiding the limitations of subjective weighting. The comprehensive evaluation model can fully reflect the low-altitude operation risk status, with accurate and reliable quantitative results. This provides a scientific basis for airspace traffic allocation, route planning, and risk warning, effectively improving the safety and management efficiency of low-altitude airspace operations.
[0017] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.
[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0019] 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.
[0020] Figure 1 Flowchart of the method for classifying, calculating and comprehensively assessing risk indicators for low-altitude airspace operations; Figure 2 This is a schematic diagram of raster information; Figure 3 This is a rasterized partitioned image of the spatial domain. Figure 4 A 3D flight path diagram for the drone; Figure 5 The index value is for the 0-60m airspace. Figure 6 The index value is for the 60-120m airspace. Figure 7 The airspace index value is 120-180m. Figure 8 The figure shows the experimental results of the impact of traffic flow on airspace operation risks. Detailed Implementation
[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0022] This application provides a method and system for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, which will be described in detail below. It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments of this application. Furthermore, in the following embodiments, the descriptions of each embodiment have their own emphasis; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments.
[0023] See Figure 1 In one embodiment of this application, the method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators includes the following steps: Step 1: Divide the low-altitude airspace into grid cells; Step 2: Construct and calculate the risk assessment indicator design system; Step 3: Standardization of indicators; Step 4: Determine the index weights based on the entropy weight method; Step 5: Calculate the low-altitude airspace operation risk value by combining the indicator value and weight.
[0024] Specifically, in step 1, the low-altitude airspace grid cell division includes the following steps: Step 1.1: Perform three-dimensional raster subdivision of the low-altitude airspace with a raster granularity of 30-100m; Step 1.2: Encode the raster cells using a reverse "Z" pattern to complete raster positioning and indexing. Digital encoding of the spatial raster is a crucial step in algorithm optimization. The spatial location of a spherical raster on the Earth's surface is represented by a unique raster encoding string, which serves as a precise reference for identifying and locating the raster's spatial position. To ensure the accuracy of spatial information and the efficiency of processing, detailed naming conventions for spatial rasters are established, and the various elements contained within the raster are clearly categorized, such as... Figure 2 The diagram illustrates the raster information. These standards and classifications help to organize and manage airspace data more systematically, thereby improving the efficiency and security of the entire airspace management process.
[0025] In one embodiment, for example, an experimental spatial domain of 600m × 600m × 180m is constructed, and the spatial domain is rasterized with a 60m grid granularity. The entire spatial domain is divided into three layers, with each layer being 60m. In a two-dimensional plane, the spatial domain is divided into 10 rows and 10 columns, totaling 300 spatial grid cells. A reverse "Z" encoding rule is adopted from front to back and from bottom to top to locally encode the spatial grid cells. The spatial domain rasterization diagram is shown below. Figure 3 As shown.
[0026] Furthermore, airspace operation risk assessment is a primary basis for airspace planning, traffic flow allocation, and other critical decisions in air traffic management. It aims to comprehensively analyze and evaluate the airspace operation status within a specific time period. Airspace operation status encompasses not only airspace safety but also operational efficiency. Through a systematic assessment of airspace operation risks, the airspace operation status is quantitatively analyzed. Airspace operation risk assessment enables refined management, optimizes the allocation of airspace resources, improves airspace utilization efficiency, and ensures flight safety.
[0027] Currently, airspace operation risk assessment mainly targets high-altitude public transport aviation, comprehensively evaluating the airspace operation risk status from aspects such as airspace structure, traffic conditions, and conflict risks. For low-altitude operation risk assessment, no assessment standards have yet been established. This embodiment constructs low-altitude operation risk assessment indicators based on the characteristics of low-altitude operations and fundamental airspace assessment theories.
[0028] Specifically, step 2, which involves constructing and calculating the risk assessment indicator design system, includes the following steps: Step 2.1: Calculate the drone traffic density to characterize the density of drones within the grid. Traffic density represents the density of drones within the airspace grid and can be defined as the number of drones within a single airspace grid at a given time. The formula for calculating the drone traffic density is as follows: ; In the formula Indicates traffic density. Indicates the number of drones. Indicates the spatial raster granularity; Traffic density can be used to characterize potential risks in airspace operations. The higher the traffic density, the more drones are in the grid cell. In such a high-density traffic environment, when conflicts occur between drones, the complexity of conflict resolution increases significantly. Once a conflict occurs, a drone's conflict resolution decision will not only directly affect its own flight path, but may also affect other drones in the vicinity, thereby affecting the safety of the entire airspace operation.
[0029] Step 2.2: Calculate airspace saturation to characterize grid capacity matching and congestion risk. In actual operation scenarios, each airspace grid cell has its own airspace capacity, and airspace saturation represents the degree of saturation of the actual number of UAV flights relative to the airspace capacity. Airspace saturation can be defined as the ratio of the actual number of UAV flights to the airspace capacity. The formula for calculating airspace saturation is as follows: ; In the formula Indicates spatial saturation. This indicates the number of drones in the airspace grid. Indicates spatial capacity; Airspace saturation can be used to characterize the airspace grid cell's capacity and flow matching degree, and can be used to identify air traffic congestion anomalies. High airspace saturation means that UAVs have low fault tolerance when flying along predetermined routes, and unplanned disturbances can easily cause air congestion risks; the airspace grid cell can accept fewer UAV operations. Conversely, low airspace saturation means that UAVs have high degrees of freedom and high fault tolerance when flying within the airspace grid, and are more capable of responding to emergencies; the airspace grid cell can accept more UAV operations.
[0030] Step 2.3: Calculate the average cross-convergence coefficient, which characterizes the complexity of airspace structure and flight path intersections. The average cross-convergence coefficient reflects the complexity of airspace structure and can be defined as the ratio between the number of UAVs with intersecting flight paths in a specific airspace within a certain time period and the total number of UAVs operating in that airspace. The formula for calculating the average cross-convergence coefficient is as follows: ; In the formula Represents the average cross-convergence coefficient. This indicates the number of drones passing through the airspace grid cells. This indicates the number of drones within a specific airspace.
[0031] The average cross-convergence coefficient can be used to characterize airspace complexity. The higher the average cross-convergence coefficient, the more flight paths pass through the airspace grid, which means the airspace complexity of the airspace grid is higher.
[0032] Furthermore, the risk assessment of low-altitude airspace operation is essentially a comprehensive evaluation of the feasibility of airspace operation. This assessment requires comprehensive consideration of multiple factors and indicators. However, currently, there is no unified quantitative standard for the importance of each indicator, resulting in a lack of objective basis in practical applications. In addition, low-altitude airspace management is still in its early stages, and existing experience and data are insufficient to support the reasonable determination of the weights of these three types of indicators. Given these challenges, it is particularly important to objectively assign weights to each evaluation indicator using the entropy weight method. The entropy weight method is an information theory-based weighting method that reflects the importance and influence of each indicator in decision-making by calculating its information entropy. Therefore, a low-altitude airspace operation risk assessment model can be constructed by objectively assigning weights to the evaluation indicators based on the entropy weight method.
[0033] In step 3, the standardization of indicators includes: The indicators are distinguished as positive or negative, and normalization is performed using corresponding formulas to unify the dimensions of the indicators.
[0034] Furthermore, the step of performing normalization using the corresponding formula includes: Step 3.1, determine the indicator weights: Assumption This represents the number of low-altitude airspace grid cells. The initial decision matrix is determined by the number of risk assessment indicators for low-altitude airspace operations. for: ; In the formula This represents the corresponding indicator value; Step 3.2, indicator normalization processing: To eliminate the influence of different dimensions on the evaluation results, the values of each indicator are normalized, that is, the indicator values are converted from absolute values to relative values. For positive indicators, the following formula is used for normalization: ; For negative indicators, the following formula is used for normalization: .
[0035] Furthermore, step 4, determining the index weights based on the entropy weight method, includes the following steps: Step 4.1: Calculate the proportion of each evaluation index value in each spatial raster to the total proportion of that evaluation index in all spatial raster cells. The specific formula is as follows: ; In the formula Indicates the proportion of the indicator; Step 4.2: Based on the indicator ratio The information entropy values of each indicator are calculated using the following formula: ; In the formula Indicates the first The information entropy of the item, and ,in ; Step 4.3: Calculate the weights of each item based on the information entropy, using the following formula: ; In the formula Indicates the first Weight of each indicator.
[0036] In one embodiment of this application, step 5, calculating the low-altitude airspace operation risk value by combining the index value and weight, includes: Based on the normalized index value and the entropy weight method, a risk assessment function is constructed to output the low-altitude airspace operation risk value; the formula of the risk assessment function is as follows: ; In the formula This indicates the risk value for operations in low-altitude airspace. , and These represent the normalized index values, , and Represents the indicator weight, and satisfies .
[0037] In one embodiment, for example, 400 drones are set up in the experimental airspace. The drones include four types with safe intervals of 10m, 15m, 25m, and 33m, respectively. Multiple start and end points are set up within the airspace. An initial flight plan for the drones is constructed. The drones move at a constant speed according to their standard speeds. The three-dimensional flight paths of the drones are as follows: Figure 4As shown, the study investigates the operational risks of airspace within a 10-minute timeframe. The operational risk assessment index of each airspace grid is calculated using the initial traffic flow to evaluate the operational risks of the airspace grid.
[0038] The proportions of 150 drones of different categories were set to 1:1:1:1. Three indicators of airspace operational status at the start of operation (1 minute) were statistically analyzed. The weights of these indicators were determined based on their statistical values. Specific indicator data are as follows: Figures 5-7 As shown.
[0039] Figures 5-7 The three layers from top to bottom represent the parameters of three evaluation indicators—traffic density, airspace saturation, and average cross-convergence coefficient—in each grid cell. Based on this data, the weights of the indicators are determined using the entropy weight method. , , Considering the sensitivity of the entropy weight method to data quality, in order to reduce the impact of uncontrollable factors such as data quality on the experimental results, [the following will be implemented]. , , As the indicator weights for the three indicators in subsequent experiments.
[0040] The airspace operation risk values calculated based on the indicator weights show that the raster risk at different altitude layers exhibits significant differences (e.g., Figures 5-7 (The bottom layer shown). This difference is mainly due to the different flight altitudes of the drones at different flight stages. The time period selected for the experiment was during the drone takeoff stage, when the drones were still in the takeoff and climb phase. Most drones were concentrated in the 0-60m altitude layer, so the high-risk areas of the lower airspace grid were more densely distributed.
[0041] Considering that the differences in UAV distribution within airspace grids at different altitude levels are influenced by the characteristics of UAV flight missions, UAV traffic flow within the range of (1 min, 2 min) at an altitude of 60-120 m was selected to study the impact of UAV traffic flow on airspace operational risks. The experimental results are as follows: Figures 5-7 As shown.
[0042] Depend on Figure 8It can be seen that the operational risk value of low-altitude airspace and the flow of UAVs in the airspace grid show roughly the same trend, thus indicating a positive correlation between the operational risk value and UAV flow. This is mainly because there is a close relationship between UAV flow and airspace operational risk assessment indicators. High UAV flow indicates a dense airway network within the airspace grid, which increases the number of airway intersections, resulting in a high average intersection-convergence coefficient. Simultaneously, high UAV flow directly increases the traffic density within the grid. However, it can be observed that some airspace grids exhibit a weak correlation between risk value and flow. This is primarily because airspace UAV flow statistics represent the number of UAVs passing through the grid space within a certain time frame. The experiment simulated the heterogeneous characteristics of low-altitude airspace, merging the four categories of UAVs previously classified. The different safety intervals for each category of UAVs also contribute to situations where the flow is the same but the risk value differs within the same granularity airspace grid.
[0043] Experimental verification shows that the low-altitude airspace operation risk assessment model based on the entropy weight method constructed in this embodiment can effectively reflect the low-altitude airspace operation risk situation, which is of great significance for assessing the airspace operation status and planning air routes.
[0044] An embodiment of this application also provides a system for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, including: The airspace raster division module is used for dividing low-altitude airspace raster units; The risk indicator calculation module is used to construct and calculate the risk assessment indicator design system; The indicator standardization module is used for indicator standardization processing; The entropy weighting module determines the weights of indicators based on the entropy weighting method. The risk assessment module is used to calculate the risk value of low-altitude airspace operation by combining indicator values and weights.
[0045] It should be noted that the system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0046] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the system described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0047] An embodiment of this application also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators as described above.
[0048] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The electronic device may include, but is not limited to, a processor and a memory.
[0049] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.
[0050] The memory can be used to store the computer program. The processor implements various functions of the electronic device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.
[0051] Another embodiment of the present invention provides a storage medium, which is a computer-readable storage medium. A computer program is stored in the computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0052] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.
Claims
1. A method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, characterized in that, Includes the following steps: Step 1: Divide the low-altitude airspace into grid cells; Step 2: Construct and calculate the risk assessment indicator design system; Step 3: Standardization of indicators; Step 4: Determine the index weights based on the entropy weight method; Step 5: Calculate the low-altitude airspace operation risk value by combining the indicator value and weight.
2. The evaluation method according to claim 1, characterized in that, Step 1, the division of low-altitude airspace grid cells, includes the following steps: Step 1.1: Perform three-dimensional raster subdivision of the low-altitude airspace with a raster granularity of 30-100m; Step 1.2: Encode the grid cells using the reverse "Z" pattern rule to complete grid positioning and indexing.
3. The evaluation method according to claim 2, characterized in that, Step 2, which involves constructing and calculating the risk assessment indicator design system, includes the following steps: Step 2.1: Calculate the drone traffic density to characterize the density of drones within the grid; the formula for calculating the drone traffic density is as follows: ； In the formula Indicates traffic density. Indicates the number of drones. Indicates the spatial raster granularity; Step 2.2: Calculate the spatial saturation, which characterizes grid capacity matching and congestion risk; the formula for calculating the spatial saturation is as follows: ； In the formula Indicates spatial saturation. This indicates the number of drones in the airspace grid. Indicates spatial capacity; Step 2.3: Calculate the average cross-convergence coefficient, which characterizes the complexity of airspace structure and flight path intersections; the formula for calculating the average cross-convergence coefficient is as follows: ； In the formula Represents the average cross-convergence coefficient. This indicates the number of drones passing through the airspace grid cells. This indicates the number of drones within a specific airspace; the average cross-convergence coefficient is used to characterize airspace complexity. The higher the average cross-convergence coefficient, the more flight paths cross the airspace grid, meaning the higher the airspace complexity of the airspace grid.
4. The evaluation method according to claim 3, characterized in that, In step 3, the standardization of indicators includes: The indicators are distinguished as positive or negative, and normalization is performed using corresponding formulas to unify the dimensions of the indicators.
5. The evaluation method according to claim 4, characterized in that, The steps for performing normalization using the corresponding formula include: Step 3.1, determine the indicator weights: Assumption This represents the number of low-altitude airspace grid cells. The initial decision matrix is determined by the number of risk assessment indicators for low-altitude airspace operations. for: ； In the formula This indicates the corresponding indicator value; Step 3.2, index normalization processing: Normalize the values of each indicator, that is, convert the indicator values from absolute values to relative values. For positive indicators, the following formula is used for normalization: ； For negative indicators, the following formula is used for normalization: 。 6. The evaluation method according to claim 5, characterized in that, Step 4, determining the index weights based on the entropy weight method, includes the following steps: Step 4.1: Calculate the proportion of each evaluation index value in each spatial raster to the total proportion of that evaluation index in all spatial raster cells. The specific formula is as follows: ； In the formula Indicates the proportion of the indicator; Step 4.2: Based on the indicator ratio The information entropy values of each indicator are calculated using the following formula: ； In the formula Indicates the first The information entropy of the item, and ,in ; Step 4.3: Calculate the weights of each item based on the information entropy, using the following formula: ； In the formula Indicates the first Weight of each indicator.
7. The evaluation method according to claim 6, characterized in that, In step 5, the calculation of the low-altitude airspace operation risk value by combining the indicator value and weight includes: Based on the normalized index value and the entropy weight method, a risk assessment function is constructed to output the low-altitude airspace operation risk value; the formula of the risk assessment function is as follows: ； In the formula This indicates the risk value for operations in low-altitude airspace. , and These represent the normalized index values, , and Represents the indicator weight, and satisfies .
8. A system for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators, characterized in that, include: The airspace raster division module is used for dividing low-altitude airspace raster units; The risk indicator calculation module is used to construct and calculate the risk assessment indicator design system; The indicator standardization module is used for indicator standardization processing; The entropy weighting module determines the weights of indicators based on the entropy weighting method. The risk assessment module is used to calculate the risk value of low-altitude airspace operation by combining indicator values and weights.
9. An electronic device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for classifying, calculating, and comprehensively evaluating low-altitude airspace operation risk indicators as described in any one of claims 1-7.
10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the method for classifying, calculating and comprehensively evaluating low-altitude airspace operation risk indicators as described in any one of claims 1-7.