Intelligent traffic automatic traffic checkpoint management method under low-altitude economy

Abstract

The invention relates to the technical field of traffic checkpoint management, and provides an intelligent traffic automatic traffic checkpoint management method under low-altitude economy. Comprising the steps of using a multi-band radar and a photoelectric sensor to set a monitoring network to collect aircraft information, establishing an authority electronic fence under an identity authentication mechanism, checking license permission by means of a block chain, scheduling gate passing priorities according to a traffic flow prediction model, formulating a queuing sequence, and linking the ground and a low-altitude gate to cooperatively release. The technical problem of airspace congestion in a part of time periods caused by aircraft type difference and passing demands in different time periods is solved, and the purposes of establishing an aircraft identity authentication mechanism and an authority electronic fence, applying a traffic flow prediction model, combining aircraft multi-dimensional information and airspace load data, dynamically adjusting the gate passing priority, and improving the traffic flow prediction efficiency are achieved. The technical effects of reasonably determining an aircraft queuing sequence, planning an aircraft route and a ground traffic control strategy in advance, and improving the overall safety and passing efficiency of low-altitude and ground traffic are achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of traffic portal management, in particular to an intelligent traffic automatic traffic portal management method under a low-altitude economy. BACKGROUND

[0002] With the rapid rise of the low-altitude economy, unmanned aerial vehicle logistics, manned aircraft, urban air traffic (UAM) and other low-altitude aircraft application scenarios are increasingly rich, and the traffic flow in the low-altitude field presents an explosive growth. Intelligent and automatic management of low-altitude traffic has become a core requirement for ensuring airspace safety and improving traffic efficiency.

[0003] In the monitoring aspect, single-frequency radar or photoelectric equipment cannot balance long-distance detection and high-precision identification, often resulting in target missed detection and misjudgment problems, and multi-source data is difficult to fuse, resulting in incomplete aircraft information collection. In terms of safety authentication, the centralized electronic license verification system is vulnerable to network attacks, and the permission electronic fence update lags behind, and there is a risk of unauthorized aircraft entering the no-fly zone. SUMMARY

[0004] The application provides an intelligent traffic automatic traffic portal management method under a low-altitude economy, aiming to solve the technical problem of airspace congestion caused by differences in aircraft types and traffic demand at different times.

[0005] The application discloses an intelligent traffic automatic traffic portal management method under a low-altitude economy, which comprises the following steps:

[0006] A monitoring network is set up using multi-frequency radar and photoelectric sensors to collect aircraft position, aircraft speed and aircraft model information; an electronic fence permission under an aircraft identity authentication mechanism is established, and the aircraft electronic license and flight permission are verified in combination with blockchain technology; a traffic flow prediction model is used to dynamically schedule the portal traffic priority associated with the aircraft position, aircraft speed and aircraft model information, and determine the aircraft queuing sequence in combination with airspace load data; and the ground traffic portal and low-altitude traffic portal covered by the monitoring network are linked to perform coordinated release control according to the aircraft queuing sequence.

[0007] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0008] The intelligent traffic automation traffic checkpoint management method under the low-altitude economy uses a multi-band radar and a photoelectric sensor to set up a monitoring network, cooperates the multi-band radar and the photoelectric sensor to collect aircraft position, aircraft speed and aircraft model information, secondly, establishes an authority electronic fence under an aircraft identity authentication mechanism, combines a blockchain technology to send a verification request containing an electronic license hash value and a flight permit abstract to a blockchain node, authenticates the verification request, if the authentication is passed, generates a temporary pass token and synchronizes to a traffic checkpoint control terminal, in this way, the aircraft electronic license and the flight permit are verified, the low-altitude flight order is ensured, then, a traffic flow prediction model is used to determine the airspace traffic demand in different periods, based on this, the first mapping rule corresponding to the aircraft model information, the second mapping rule corresponding to the aircraft position and the third mapping rule corresponding to the aircraft speed are combined to obtain a checkpoint traffic priority, the aircraft queuing sequence is determined in combination with airspace load data, the checkpoint traffic priority dynamic scheduling associated with the aircraft position, the aircraft speed and the aircraft model information is realized, finally, according to the aircraft queuing sequence, the ground traffic checkpoint and the low-altitude traffic checkpoint covered by the monitoring network are linked to cooperatively release control, the aircraft route and the ground traffic control strategy are planned in advance, the aircraft identity authentication mechanism and the authority electronic fence are established, the traffic flow prediction model is used, the aircraft multi-dimensional information and the airspace load data are combined, the checkpoint traffic priority is dynamically adjusted, the aircraft queuing sequence is reasonably determined, the aircraft route and the ground traffic control strategy are planned in advance, and the overall safety and traffic efficiency of the low-altitude and ground traffic are improved.

[0009] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0011] Figure 1 It is a flowchart of the intelligent traffic automation traffic checkpoint management method under the low-altitude economy in an embodiment. DETAILED DESCRIPTION

[0012] The embodiment of the application provides an intelligent traffic automated traffic checkpoint management method under a low-altitude economy, and solves the technical problem of airspace congestion in some periods caused by differences in aircraft types and traffic demands in different periods.

[0013] The technical solutions in the embodiments of the application will be clearly and completely described in combination with the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the application.

[0014] It should be noted that the terms "comprising" and "having" and any change thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server comprising a series of steps or units does not have to be limited to the clearly listed steps or units, but can include other steps or modules that are not clearly listed or inherent to the process, method, product or device.

[0015] Embodiment one, as shown in the figure, the application provides an intelligent traffic automated traffic checkpoint management method under a low-altitude economy, the method comprises: Figure 1

[0016] A monitoring network is set up using a multi-frequency radar and an optical-electric sensor to collect aircraft position, aircraft speed and aircraft model information; an authority electronic fence under an aircraft identity authentication mechanism is established, and the aircraft electronic license and flight permission are verified in combination with a block chain technology.

[0017] Specifically, the frequency radar is a device capable of transmitting and receiving multiple radar signals of different frequencies, and radars of different frequency bands have advantages in detection distance, accuracy and target characteristic identification, etc. The optical-electric sensor is a device that collects visual information such as images through optical principles, and the combination of the two can realize the collection of aircraft position, speed and model information. The establishment of the authority electronic fence under the aircraft identity authentication mechanism means that a virtual geographical boundary (electronic fence) is delimited through technical means, only the aircraft that meets the authorization requirements after identity authentication can enter, and the combination of the block chain technology for verifying the aircraft electronic license and flight permission can ensure the authenticity and security of the information.

[0018] ​The monitoring network is set up using multi-band radars and photoelectric sensors, which can take advantage of the different distance and precision detection capabilities of multi-band radars. For example, low-frequency radars can achieve long-range detection, while high-frequency radars can provide high-precision target information. At the same time, photoelectric sensors can obtain visual features such as the shape and identification patterns of aircraft. Through spatio-temporal alignment and fusion processing of these data, accurate collection and correlation analysis of aircraft position, speed, and model information can be achieved, improving target recognition accuracy and monitoring reliability. An electronic fence with permission under the aircraft identity authentication mechanism is established, and the aircraft electronic license and flight permission are verified using blockchain technology to ensure that only legal and compliant aircraft can enter specific airspace.

[0019] A traffic flow prediction model is used to dynamically schedule the pass-through priority of the aircraft position, speed, and model information correlation, and determine the aircraft queuing sequence based on airspace load data. According to the aircraft queuing sequence, the ground traffic checkpoints and low-altitude traffic checkpoints covered by the monitoring network are linked to perform coordinated release control.

[0020] Specifically, a multi-band radar is a device that transmits and receives radar signals at multiple different frequencies to detect targets, effectively covering different detection distances and precision requirements. Photoelectric sensors are devices that capture images or other visual data using optical principles, providing features such as the shape and identification of aircraft. A traffic flow prediction model is a mathematical model that predicts future traffic flow based on historical and real-time data, allowing for early understanding of traffic conditions and making appropriate decisions. Dynamic scheduling refers to the process of flexibly adjusting resource allocation or process arrangement based on real-time conditions. Pass-through priority refers to the order in which aircraft pass through a checkpoint based on specific rules. Airspace load data refers to data that describes the density and speed distribution of aircraft in the airspace, reflecting the use of airspace. The aircraft queuing sequence is a list of aircraft waiting to pass through the checkpoint in a certain order and priority. Coordinated release control refers to the coordination between ground traffic checkpoints and low-altitude traffic checkpoints to ensure smooth and safe traffic flow.

[0021] Based on the traffic flow prediction model, combined with the multi-dimensional information such as the position, speed and model of the aircraft, the accurate prediction of the air traffic demand in different time periods can be realized, for example, through the analysis of historical data and real-time monitoring data, the peak period of logistics distribution can be predicted, so as to take measures in advance; based on these prediction results, the priority of the aircraft passing through the card mouth is dynamically scheduled, at the same time, combined with the air space load data, the aircraft density, speed variance and other indicators in the air space are comprehensively evaluated, so as to determine the reasonable aircraft queuing sequence, for example, when the air congestion reaches a certain degree, the passing speed of non-emergency task aircraft is appropriately reduced, and the passage is given to high-priority aircraft. According to the determined queuing sequence, the ground traffic card mouth and the low-altitude traffic card mouth covered by the monitoring network are linked to perform cooperative release control, specifically, after the low-altitude traffic card mouth releases a batch of aircraft, the ground traffic card mouth can adjust the traffic signal according to the actual situation to avoid the conflict between ground vehicles and low-altitude aircraft, ensure the overall safety and efficiency of traffic, realize the cooperative management of low-altitude and ground traffic, and improve the operation and efficiency reliability of the whole traffic system.

[0022] Further, a monitoring network is set by using a multi-band radar and an optoelectronic sensor to collect aircraft position, aircraft speed and aircraft model information, and the method comprises:

[0023] The optoelectronic sensor is used for image acquisition to extract aircraft features including contour and identification pattern; according to the aircraft features, the aircraft model information is matched and determined; at the same time, the multi-band radar and the optoelectronic sensor are subjected to spatio-temporal alignment and fusion processing.

[0024] Specifically, the optoelectronic sensor is a device capable of converting optical signals into electrical signals, which can collect image information of the aircraft through optical imaging principle, and then extract features such as the contour and identification pattern of the aircraft. The contour refers to the overall shape and structural features of the aircraft, and the identification pattern may include the logo, number and other specific patterns on the surface of the aircraft, which are helpful for identifying the model of the aircraft. Matching and determining the aircraft model information means comparing the extracted aircraft features with the known aircraft model database to accurately determine the specific model of the aircraft. Spatio-temporal alignment and fusion processing means calibrating and integrating the data collected by the multi-band radar and the optoelectronic sensor in the time and space dimensions, so that the data from different sources can be comprehensively analyzed in the same reference system, improving the accuracy and reliability of the data.

[0025] When using photoelectric sensors for image acquisition, their high-resolution imaging capabilities can clearly capture detailed features such as the shape and identification patterns of the aircraft. For example, a certain type of logistics drone has a unique hexagonal shape and specific identification patterns, and photoelectric sensors can accurately extract these features and match them with information in the database, quickly determining the model information of the aircraft with an accuracy rate of over 95%. Meanwhile, spatiotemporal alignment and fusion processing of multi-band radar and photoelectric sensors is a key step. Multi-band radar can provide information such as the position and speed of the aircraft, but its accuracy may be limited by the single frequency band; while photoelectric sensors can provide rich visual features, but may lack in distance and speed measurement. Through spatiotemporal alignment, the data of the two are accurately matched in time and space, and then fused, which can fully leverage their respective advantages and make up for each other's shortcomings, providing more accurate and comprehensive data support for subsequent steps such as aircraft identity authentication, permission management, and traffic scheduling, effectively improving the performance and reliability of the entire system.

[0026] Further, an electronic fence of permissions under the aircraft identity authentication mechanism is established, and the blockchain technology is used to verify the electronic license of the aircraft and the flight permit. The method comprises:

[0027] Sending a verification request containing the hash value of the electronic license and the summary of the flight permit to the blockchain node; establishing an electronic fence of permissions under the aircraft identity authentication mechanism, authenticating the verification request, and generating a temporary access token if the authentication is passed.

[0028] Specifically, the blockchain node is an independent computer system in the blockchain network, responsible for tasks such as data storage, verification, and dissemination, which collectively maintain the security and immutability of data on the blockchain. Hash value is a string of fixed length converted from data of arbitrary length through a specific algorithm, with uniqueness and irreversibility, which can be used to verify the integrity and consistency of data. The summary of the flight permit is a highly summarized version of the key information of the flight permit, retaining the core content but not containing all the details. The verification request is a message sent to the blockchain node, requesting verification of specific hash values and summary information to confirm their authenticity and reliability. The electronic fence of permissions is a virtual geographical boundary that restricts and manages the flight area of the aircraft through technical means, and only aircraft that have passed identity authentication and obtained corresponding permissions can enter specific areas. The temporary access token is a time-limited electronic certificate used to authorize the aircraft to pass through specific traffic checkpoints within a certain time period, which expires automatically after expiration.

[0029] The process of sending a verification request containing the electronic license hash value and the flight permit summary to the blockchain node refers to packaging the hash value obtained by processing the electronic license of the aircraft through a hash algorithm and the summary information obtained by highly summarizing the content of the flight permit into a verification request message and sending it to the nodes in the blockchain network for verification. After receiving the verification request, the blockchain node will check and verify the hash value and summary information according to the smart contract and historical data stored on the blockchain. If the verification is passed, it means that the electronic license and flight permit are real and valid, at which time the permission electronic fence under the aircraft identity authentication mechanism is established, and a temporary access token is generated. The permission electronic fence will allow the aircraft to fly in the specified airspace according to the authentication result of the aircraft, and monitor its flight path and area in real time. The temporary access token serves as a voucher for the aircraft to pass through the traffic checkpoint, ensuring that only legal and compliant aircraft can enter the specified area, not only ensuring the security and reliability of the aircraft identity authentication, preventing unauthorized aircraft from entering the restricted area, but also effectively improving the management efficiency and safety of low-altitude traffic by combining with subsequent traffic scheduling and coordinated release control.

[0030] Further, the temporary access token is synchronized to a traffic checkpoint control terminal; the traffic checkpoint control terminal is in communication with a ground traffic checkpoint and a low-altitude traffic checkpoint.

[0031] Specifically, the traffic checkpoint control terminal is a device for managing and controlling traffic checkpoint access, and can receive and process various access instructions and data. Synchronizing the temporary access token to the traffic checkpoint control terminal means sending and storing the previously generated temporary access token information to the terminal device. Communication means that the traffic checkpoint control terminal establishes a stable communication connection with the ground traffic checkpoint and the low-altitude traffic checkpoint, realizes real-time transmission of data and timely issuance of instructions.

[0032] The temporary access token is synchronized to the traffic portal control terminal to ensure that the portal control terminal can obtain the latest aircraft access authorization information in a timely manner. For example, after the aircraft passes the identity authentication and obtains the temporary access token, the token is synchronized to the traffic portal control terminal within a few seconds. The traffic portal control terminal has a special area for storing these token information, and the information is encrypted to ensure data security. The traffic portal control terminal is connected with the ground traffic portal and the low-altitude traffic portal, which means that the portal control terminal can send instructions to the ground and low-altitude traffic portals in real time to control the opening and closing of the portals. For example, after receiving the temporary access token, the portal control terminal sends a release instruction to the corresponding portal according to the information in the token, such as access time and access rights, to ensure that the aircraft can pass through the portal according to the predetermined rules and order, avoiding unauthorized aircraft entering the airspace, and improving the efficiency and safety of traffic management, so that the low-altitude and ground traffic can run in an orderly and smooth manner.

[0033] Further, the traffic flow prediction model is used to dynamically schedule the portal access priority associated with the aircraft position, aircraft speed, and aircraft model information. The method includes:

[0034] The traffic flow prediction model is used to determine the airspace access demand at different time periods. Based on the airspace access demand at different time periods, the first mapping rule corresponding to the aircraft model information, the second mapping rule corresponding to the aircraft position, and the third mapping rule corresponding to the aircraft speed are combined to obtain the portal access priority.

[0035] Specifically, the traffic flow prediction model is a mathematical model that predicts future traffic flow by analyzing historical and real-time data. It can predict the traffic flow changes in the airspace according to different time periods and aircraft types. The airspace access demand refers to the number and type of aircraft that the airspace can accommodate within a certain time period, reflecting the usage and carrying capacity of the airspace. The mapping rule is a rule that corresponds the aircraft model, position, speed, and other information to the access priority. By applying these rules, the access priority of different aircraft can be determined.

[0036] Based on the traffic flow prediction model, the system can comprehensively analyze historical data and real-time monitoring information to determine the airspace traffic demand in different time periods, and the traffic demand of the manned aircraft in the tourist peak period may increase. Based on these prediction results, the system will combine the first mapping rule corresponding to the aircraft model information (such as logistics unmanned aircrafts will be given a higher weight coefficient due to higher task urgency), the second mapping rule corresponding to the aircraft position (such as aircrafts closer to the target area may have priority access), and the third mapping rule corresponding to the aircraft speed (such as faster aircrafts have priority access under safety conditions), and perform combined operations on these rules to ultimately obtain the access priority of the checkpoint. By reasonably allocating the access priority of different aircrafts, the system can optimize the use efficiency of airspace resources, reduce the waiting time of aircrafts in the air, and improve the overall access efficiency.

[0037] Further, based on the airspace traffic demand in different time periods, the method comprises:

[0038] According to the logistics unmanned aircraft and the manned aircraft under the aircraft model information, a differentiated weight coefficient is set; based on the differentiated weight coefficient, the first mapping rule corresponding to the aircraft model information is configured.

[0039] Specifically, the differentiated weight coefficient refers to the different importance or priority values allocated to different aircraft models (such as logistics unmanned aircrafts and manned aircrafts) according to their characteristics and task requirements, which are used to reflect their differences in access priority calculation. The first mapping rule refers to the rule of associating aircraft model information with its corresponding differentiated weight coefficient. Through this rule, the priority order of different types of aircrafts in the access of the checkpoint can be determined.

[0040] According to the logistics unmanned aerial vehicle, manned aircraft and other different types under the aircraft model information, different weight coefficients are set. For example, the logistics unmanned aerial vehicle is given a higher weight coefficient due to its timeliness of task, while the manned aircraft is given a higher weight coefficient due to its higher safety requirements. Based on these differentiated weight coefficients, the first mapping rule corresponding to the aircraft model information is configured, that is, different types of aircraft and their corresponding weight coefficients are bound by rules. In the whole low-altitude traffic management system, this process can ensure that different types of aircraft are given reasonable priority in the card mouth passing according to their task nature and demand. Specifically, during the peak period of air traffic demand, manned aircraft can pass through the card mouth according to its higher weight coefficient, thereby ensuring the efficiency and safety of personnel transportation; logistics unmanned aerial vehicles can also obtain appropriate priority according to the urgency of their tasks, ensuring the timeliness of logistics distribution; in this way, airspace resources are more accurately allocated, overall traffic efficiency is improved, the operation requirements of different aircraft are met, and low-altitude traffic management is more flexible and efficient.

[0041] Further, the aircraft queuing sequence is determined in combination with the airspace load data, and the method further comprises:

[0042] Based on the monitoring network, a multi-level grid unit is divided; the aircraft density and speed variance in the multi-level grid unit are traversed to evaluate the degree of airspace congestion; and the passing capacity threshold corresponding to the multi-level grid unit is adjusted according to the degree of airspace congestion.

[0043] Specifically, the multi-level grid unit refers to dividing the airspace covered by the monitoring network into multiple different levels of grids, each grid unit having a specific spatial range and attributes. The aircraft density refers to the number of aircraft in a unit airspace, and the speed variance refers to the dispersion degree of aircraft speed, both of which are important indicators for measuring airspace traffic conditions. The degree of airspace congestion is evaluated by comprehensively considering factors such as aircraft density and speed variance, and is used to reflect whether the traffic flow in the airspace is close to or exceeds the carrying capacity. The passing capacity threshold refers to the maximum number of aircraft that can be accommodated or passed through by the airspace or card mouth under the premise of ensuring flight safety and efficiency.

[0044] Based on the monitoring network, the airspace is divided into multiple levels of grid units, forming a hierarchical grid structure. For example, the city low-altitude airspace can be divided into several primary grid units, each of which is further divided into multiple secondary grid units, and so on. The edge length of each grid unit can be set to 100 meters to several kilometers according to actual needs, and then these multiple levels of grid units are traversed to monitor and calculate the aircraft density and speed variance in each grid in real time. According to these indicators, the degree of airspace congestion is evaluated, and if the congestion degree reaches the preset moderate congestion level, the corresponding multiple levels of grid units are adjusted to the traffic capacity threshold, and the adjusted threshold information is sent to the traffic kiosk control terminal. According to the new traffic capacity threshold, the traffic kiosk control terminal dynamically adjusts the traffic arrangement of the aircraft, such as appropriately extending the release interval or guiding part of the aircraft to re-plan the route, thereby relieving the airspace congestion and ensuring the smooth and safe low-altitude traffic. Through accurate evaluation of the degree of airspace congestion and timely adjustment of the traffic capacity threshold, the utilization efficiency of airspace resources is improved, the risk of aircraft delay is reduced, and the efficient operation of the low-altitude traffic system is ensured.

[0045] Further, the method further comprises:

[0046] Through the aircraft queuing sequence, the queuing number, waiting time, and estimated traffic window are determined; and dynamic route guidance is performed according to the queuing number, waiting time, and estimated traffic window.

[0047] Specifically, the aircraft queuing sequence refers to a sequence formed by arranging the aircrafts waiting to pass through the traffic kiosk in order. The queuing number is the unique identifier of the aircraft in the queuing sequence, used to distinguish the queuing of different aircrafts. The position waiting time refers to the time required for the aircraft to obtain traffic permission from entering the queuing sequence. The estimated traffic window refers to the time period during which the aircraft is estimated to be able to pass through the kiosk according to the current traffic conditions and scheduling plan. Dynamic route guidance refers to providing dynamic adjusted flight route guidance for the aircraft according to real-time traffic information and aircraft queuing conditions, in order to optimize its traffic path and time.

[0048] Through the aircraft queuing sequence, a unique queuing number is assigned to each aircraft, while combining the current airspace traffic conditions, the passage capacity of the toll gate and other factors, the waiting time and the predicted passage window of each aircraft are calculated, dynamic flight path guidance is carried out, and the best flight route and speed adjustment suggestion for the aircraft are planned, for example, for the above-mentioned logistics unmanned aerial vehicle, it can be suggested to fly at an economic cruising speed during the waiting period, and adjust to the holding area near the toll gate before the predicted passage window opens to ensure on-time passage; by providing accurate queuing information and dynamic flight path guidance for the aircraft, not only the passage efficiency of the aircraft is improved, the invalid waiting time of the aircraft in the air is reduced, the secondary scheduling problem caused by too long waiting time or uncertain passage window of the aircraft is effectively avoided, thereby the operation efficiency and safety of the entire traffic system are improved.

[0049] To sum up, the embodiments of the present application have at least the following technical effects:

[0050] The embodiments of the present application include using a multi-band radar and an optical-electric sensor to set up a monitoring network to collect aircraft position, aircraft speed and aircraft model information; establishing an authority electronic fence under the aircraft identity authentication mechanism, verifying the aircraft electronic license and flight permission in combination with the block chain technology; using a traffic flow prediction model to dynamically schedule the passage priority of the toll gate associated with the aircraft position, aircraft speed and aircraft model information, and determine the aircraft queuing sequence in combination with the airspace load data; according to the aircraft queuing sequence, the ground traffic toll gate and the low-altitude traffic toll gate covered by the monitoring network are linked to perform cooperative release control. Through the spatio-temporal fusion processing of the multi-band radar and the optical-electric sensor, the aircraft position accuracy is improved, the aircraft identity authentication mechanism and the authority electronic fence are established, the traffic flow prediction model is used, the passage priority of the toll gate is dynamically adjusted in combination with the multi-dimensional information of the aircraft and the airspace load data, the aircraft queuing sequence is reasonably determined, the aircraft flight path and the ground traffic control strategy are planned in advance, and the technical effects of improving the overall safety and passage efficiency of the low-altitude and ground traffic are achieved.

[0051] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The above describes a specific embodiment of the present application. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0052] The above only describes the preferred embodiments of the present application and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0053] The specification and drawings are, of course, to be regarded in an illustrative rather than a restrictive sense. It is to be understood that any such modifications, variations, combinations or equivalents that fall within the scope of the application are intended to be embraced herein.

Claims

1. A method for intelligent and automated traffic checkpoint management under low-altitude economy, characterized in that, The method includes: A monitoring network is set up using multi-band radar and photoelectric sensors to collect information on the aircraft's position, speed, and model. Establish an electronic fence for permissions under the aircraft identity authentication mechanism, and combine blockchain technology to verify the aircraft's electronic license and flight permit; Using a traffic flow prediction model, the checkpoint passage priority associated with the aircraft's location, speed, and model information is dynamically scheduled, and the aircraft queuing sequence is determined by combining airspace load data. Based on the aircraft queuing sequence, the ground traffic checkpoints and low-altitude traffic checkpoints covered by the monitoring network are coordinated to carry out release control.

2. The method as described in claim 1, characterized in that, The method involves setting up a monitoring network using multi-band radar and photoelectric sensors to collect information on aircraft position, speed, and model. The photoelectric sensor is used to acquire images and extract aircraft features, including the shape outline and marking patterns. Based on the aircraft characteristics, the aircraft model information is determined by matching. Simultaneously, spatiotemporal alignment and fusion processing are performed on the multi-band radar and the photoelectric sensor.

3. The method as described in claim 2, characterized in that, Establishing an electronic fence for permissions under an aircraft identity authentication mechanism, and verifying the aircraft's electronic license and flight permit using blockchain technology, the method includes: Send a verification request containing the electronic license hash and flight permit summary to the blockchain node; Establish an electronic fence for permissions under the aircraft identity authentication mechanism, authenticate the verification request, and generate a temporary access token if the authentication is successful.

4. The method as described in claim 3, characterized in that, Synchronize the temporary access token to the traffic checkpoint control terminal; The traffic checkpoint control terminal is connected to ground traffic checkpoints and low-altitude traffic checkpoints.

5. The method as described in claim 1, characterized in that, Using a traffic flow prediction model, the method dynamically schedules checkpoint passage priorities associated with the aircraft's location, speed, and model information. Using traffic flow prediction models, airspace traffic demand at different times is determined; Based on the airspace passage requirements at different times, the first mapping rule corresponding to the aircraft model information, the second mapping rule corresponding to the aircraft position, and the third mapping rule corresponding to the aircraft speed are combined to obtain the checkpoint passage priority.

6. The method as described in claim 5, characterized in that, Based on airspace passage requirements at different times, the method includes a first mapping rule corresponding to the aircraft model information, comprising: Based on the aircraft model information, set differentiated weighting coefficients for logistics drones and manned aircraft; Based on the differentiated weighting coefficients, configure the first mapping rule corresponding to the aircraft model information.

7. The method as described in claim 6, characterized in that, The method for determining aircraft queuing sequences by combining airspace load data further includes: Based on the monitoring network, a multi-level grid unit is defined; The density and velocity variance of aircraft within the multi-level grid cells are traversed to assess the degree of airspace congestion. Based on the degree of airspace congestion, the passage capacity threshold corresponding to the multi-level grid unit is adjusted accordingly.

8. The method as described in claim 7, characterized in that, The method further includes: The queue number, waiting time, and expected passage window are determined by the aircraft queuing sequence; Dynamic route guidance is provided based on the queue number, waiting time, and expected passage window.

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