Airport ground intelligent command and dispatching system based on cloud edge collaborative computing

Abstract

This invention relates to an intelligent airport ground command and dispatch system based on cloud-edge collaborative computing, comprising a mobile terminal for viewing lighting plans and their business attribute information via 4G / 5G / WiFi networks; a desktop terminal for monitoring the operating status of lighting fixtures, reviewing and adjusting lighting usage plans, and performing overall inspections and information maintenance; and an edge computing module for connecting to front-end sensors, performing artificial intelligence calculations and fusion, communicating with the back-end network, and receiving lighting plans issued by the lighting center via the Internet of Things. The advantages of this invention are: it configures cloud-edge collaborative computing to construct a unified automated lighting control system, fully utilizing the convenience of internet access and the centralized control capabilities of local networks; it also achieves fully automated dynamic lighting processing for daily flight support tasks; and, combined with the intelligent access capabilities of the front end, it enables comprehensive automation and energy reduction in airport lighting and control and on-site access.

Description

Technical Field

[0001] This invention relates to an intelligent airport ground command and dispatch system based on cloud-edge collaborative computing, belonging to the field of airport floodlighting. Background Technology

[0002] With the rapid development of my country's economy, the domestic aviation industry has also grown rapidly. my country has announced to the world its goal of peaking carbon emissions by 2030 and achieving carbon neutrality by 2060. Electricity is the main energy consumption source for airports, accounting for approximately 66% of total energy consumption, with lighting accounting for about 20%. As a crucial means of airport carbon management, effectively ensuring intelligent scheduling of airport lighting is an issue that must be studied and addressed.

[0003] Chinese patent (application number CN202011353642.1) discloses a control method, control system, and computer storage medium for high mast lights: by acquiring the first illuminance value of a preset aircraft stand reference point within the airport flight area, and combining it with the current time and weather conditions, the high mast lights are turned on when the first illuminance value is less than the preset illuminance value. The relationship between the second illuminance value of the preset aircraft stand reference point and the preset illuminance value is further determined. If the second illuminance value is less than the preset illuminance value, an alarm is triggered to the electrical technicians, who are then notified to come and repair the lights. This makes maintenance more convenient, increases the energy-saving potential of the high mast lights, reduces operating costs, and meets the requirements for the construction of four types of airports.

[0004] Because airports cover a very large area, including terminals and aprons, as well as office buildings and municipal roads, and considering the varying flight volumes at airports of different sizes, and the uneven distribution of domestic and international, passenger and cargo flights, coupled with the significant fluctuations in flight volume due to the current pandemic, achieving excellent energy-saving results cannot be accomplished by a single method or system. A comprehensive approach must be taken, considering the functional needs of different areas, flight characteristics, and maintenance requirements. Taking the terminal and apron lighting as an example, the main problem with existing lighting systems is that they are based on the heavy workload of large airports, requiring consideration of sunrise and sunset times to determine lighting on and off. Therefore, this patent addresses this specific situation by proposing an airport lighting control system. This system uses indoor and outdoor sensor linkage to achieve lighting based on flight missions, solving the problem of difficult manual control of lighting in different areas under abnormal weather conditions, and enabling airport lighting to function more efficiently. While this patent can objectively play a certain synergistic role in meeting the requirements of green airports among the four types of airports, it still cannot effectively address the issues of low throughput and insufficient apron utilization in small and medium-sized airports, as well as the waste of airport lighting power caused by seasonal, pandemic, and special event flight changes in large airports. Furthermore, the apron area is large, and there are many actual lighting fixtures. Under long-term use, the performance of the lighting fixtures may degrade, leading to the failure of some equipment. Network remote control equipment may also fail and become offline. The numerous restrictions on non-stop construction operations on the airport apron also complicate on-site maintenance, necessitating improvements in equipment reliability and network self-healing capabilities.

[0005] In summary, existing technologies do not provide much explanation on how to achieve intelligent command and dispatch of airport floodlighting, improve the collaborative operation level and efficiency of various work units, and lack consideration for improving the reliability of equipment control systems. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention provides an intelligent airport ground command and dispatch system based on cloud-edge collaborative computing. The technical solution of this invention is as follows:

[0007] An intelligent airport ground command and dispatch system based on cloud-edge collaborative computing includes:

[0008] The mobile terminal is used to view the lighting plan and business attribute information of lighting fixtures via 4G / 5G / WiFi networks, and to directly and manually set the switch of the lighting fixtures, test the status of the lighting fixtures and report faults, and fill in inspection and maintenance data.

[0009] Desktop terminals are used to monitor the operating status of lighting fixtures, review and adjust lighting fixture usage plans, and conduct overall inspections and information maintenance.

[0010] The edge computing module is used to connect front-end sensors and network devices, perform artificial intelligence calculations and fusion, and communicate with the back-end network. It receives lighting plans from the lighting center through the Internet of Things, detects and reports power usage and lamp integrity, controls the execution of lighting plans and on-site manual forced switching settings, and realizes extended business according to business needs.

[0011] The fog computing module is used to interface with the lighting control center and the airport's business system. It imports information resources such as flight stand occupancy, business support lighting needs, fire alarm and alarm information, and fault and maintenance records. Combined with front-end sensor data provided by the edge computing module, it performs automated and unified lighting plan processing to optimize lighting. The fog computing module distributes data based on the cloud computing module, adjusts and issues warnings based on the front-end sensor data, and if it cannot receive data from the cloud computing module, it can automatically evaluate and adjust based on historical data to generate a conservative lighting plan.

[0012] The cloud computing module is used to collect information on the usage of information resources from the fog computing module and provide data publishing and mobile terminal access via the Internet.

[0013] The fog computing module, edge computing module, and mobile terminal are interconnected and access each other via the Internet of Things, and the accessed data content is encrypted.

[0014] It also includes a cloud-edge collaborative computing module, which combines the cloud computing module and the edge computing module, allocates tasks between the cloud computing module and the edge computing module, realizes the sinking of the cloud computing module, and extends cloud computing and cloud analytics to the edge computing module; when the server network fails, the cloud computing module automatically starts relevant lighting based on its own sensor data, and starts automatic lighting at night and in abnormal weather.

[0015] The cloud computing module obtains weather forecasts based on latitude and longitude coordinates, generates sunrise and sunset times, and generates an overall operation plan. The edge computing module, in the event of local server failure or network failure of some nodes, performs real-time dynamic business processing based on the previously executed lighting plan and combined with front-end sensor data. This edge computing module provides video and industrial protocol support according to the site environment, realizes scenario-based automated edge computing capabilities, and outputs analysis results in real time to achieve dynamic processing. Any network interruption will not affect the execution of the pre-scheduled plan and the data combined with on-site sensors and monitoring, ensuring business continuity.

[0016] It also includes a dispatch client, which is used to receive dispatch task information issued by the dispatch command center, complete the inspection and patrol of airport service resources, and report the inspection progress information.

[0017] When both the fog computing module and the cloud computing module fail to connect, after reaching the predefined number of network retries and timeout requirements, information is queried based on a pre-set list of peripheral devices. An M2M network is constructed for comprehensive judgment, and if necessary, associated lights are automatically activated for nighttime and abnormal weather conditions. The edge computing module integrates on-site sensor data, such as on-site illuminance and wind speed, and evaluates the current on / off status and time of the lights to confirm whether there is insufficient illuminance during the day when the lights are not turned on. Lights are turned on when there is cloudy or rainy weather. If the illuminance is insufficient after the lights are turned on at night, it is considered a performance failure of the lights, and a relevant alarm is sent to the fog computing module. When the fog computing module fails to connect but the cloud computing module connects, the associated lighting plan is obtained through the cloud computing module, and the lighting status is uploaded to the cloud computing module. The statistical data is temporarily stored locally and re-uploaded after the fog computing module recovers.

[0018] The fog computing module has complete local network front-end device and sensor information, and automatically configures the peripheral device list for the front-end edge computing module, which is used to build an M2M network and automatically determine the status of the server when it is offline.

[0019] The fog computing module is based on the on-site ring network or 4G / 5G / IoT network, combined with the location of on-site sensors and the layout of equipment in the airport, to automatically generate M2M network areas and send them to the front-end controller, enabling regional autonomy in the event of network interruption.

[0020] The fog computing module enables the access and distribution of multi-source dynamic business data. The business data is internal enterprise data, which is processed within the fog computing module to generate associated lighting plans.

[0021] The cloud computing module provides various front-end service distribution support based on internet-connected client programs, web pages, mobile apps, and WeChat mini-programs. The cloud computing module can provide services to multiple airports, enabling centralized lighting strategy management and data statistics.

[0022] The advantages of this invention are: it is equipped with cloud-edge collaborative computing, which constructs a unified automated lighting control system. It makes full use of the convenience of Internet access and the centralized control capabilities of local networks, ensuring reliable operation. At the same time, it realizes fully automated dynamic lighting processing for flight support tasks in daily operations. Combined with the intelligent access capabilities of the front end, it enables comprehensive automation of airport lighting and control and field access, reducing energy consumption. For medium-sized airports, the construction cost can be recovered in the year of commissioning through power saving, which greatly helps airport energy conservation and emission reduction. At the same time, it allows airport lighting maintenance personnel to shift their work focus from busy lighting control and inspection to more reasonable high-value work, thereby improving the overall level of collaborative operation and production efficiency. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the architecture of the present invention;

[0024] Figure 2 This is a schematic diagram of a single-node sensor and external network access in the field control network of the present invention.

[0025] Figure 3 This is a schematic diagram of the airport ground intelligent command and dispatch method of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0027] See Figures 1 to 3 This invention relates to an intelligent airport ground command and dispatch system based on cloud-edge collaborative computing, comprising: a cloud computing module S101, used to collect information resource usage of the fog computing module and provide data dissemination and mobile terminal access via the Internet; to obtain weather forecasts based on latitude and longitude coordinates, generate sunrise and sunset times, and generate an overall operation plan;

[0028] The fog computing module (local service) S102 is used to interface between the lighting control center and the airport business system. It imports information resources such as flight stand occupancy, business support lighting needs, fire alarm and alarm information, and fault and maintenance records. Combined with front-end sensor data provided by the edge computing module, it performs automated and unified lighting plan processing to optimize lighting. The fog computing module distributes data based on the cloud computing module and adjusts and issues warnings based on the front-end sensor data. If it cannot receive data from the cloud computing module, it can automatically evaluate and adjust based on historical data to generate a conservative lighting plan.

[0029] The edge computing module S103 connects front-end sensors and network devices, performs AI calculations and fusion, and communicates with the back-end network. It receives lighting plans from the lighting center via IoT, detects and reports power usage and luminaire integrity, controls the execution of lighting plans and allows for manual on-site switching, and expands services according to business needs. It supports multiple network accesses, including traditional fiber optic networks for ring network access and simplified deployment using carrier 4G / 5G networks. It can also expand to LoRa and WiFi networks. Depending on the site environment, it provides support for various protocols and technologies such as video and industrial protocols, supporting heterogeneous access, integrating multiple sensor information and AI technology to achieve scenario-based automated edge computing capabilities. It outputs analysis results in real time for dynamic processing. Any network interruption will not affect the execution of pre-scheduled plans and the integration of data from on-site sensors and monitoring, ensuring business continuity. Under normal circumstances, sensor data fusion and processing are performed by a local server. In case of server failure or network anomalies, the local edge computing node can take over, comparing sensor data with surrounding nodes via an M2M network to determine data reliability and automatically execute relevant services. Normal flight mission lighting is issued by the local server according to the flight plan. However, when the local node is offline, it can combine the access monitoring data to determine whether there is an aircraft in the gate. If there is, the lighting can be temporarily activated to achieve automated assistance.

[0030] The S104 mobile / desktop terminal is used to view lighting plans and related business attribute information of lighting fixtures in the office or on-site via 4G / 5G / WiFi networks. It can also directly and manually set the switch of the lights, test the status of the lights and report faults, fill in inspection and maintenance data, monitor the operating status of the lights, review and adjust the lighting usage plan, and perform overall system inspection and related information maintenance.

[0031] This invention is equipped with mobile / desktop terminals, cloud computing terminals, fog computing terminals, and edge computing terminals. It directly connects production operation information with sensor networks through IoT links, enabling the traditional lighting network to change from time-based lighting to a dynamic automatic lighting mode based on support. This greatly reduces human intervention, significantly improves the flexibility of airport lighting, and allows dispatchers to shift their focus from busy data entry to more rational planning and utilization of resources, thereby improving the overall level of collaborative operation and production efficiency.

[0032] When both the fog computing module and the cloud computing module fail to connect, after reaching the predefined number of network retries and timeout requirements, information is queried based on a pre-set list of peripheral devices. An M2M network is constructed for comprehensive judgment, and if necessary, associated lights are automatically activated for nighttime and abnormal weather conditions. The edge computing module integrates on-site sensor data, such as on-site illuminance and wind speed, and evaluates the current on / off status and time of the lights to confirm whether there is insufficient illuminance during the day when the lights are not turned on. Lights are turned on when there is cloudy or rainy weather. If the illuminance is insufficient after the lights are turned on at night, it is considered a performance failure of the lights, and a relevant alarm is sent to the fog computing module. When the fog computing module fails to connect but the cloud computing module connects, the associated lighting plan is obtained through the cloud computing module, and the lighting status is uploaded to the cloud computing module. The statistical data is temporarily stored locally and re-uploaded after the fog computing module recovers.

[0033] The fog computing module has complete local network front-end device and sensor information, and automatically configures the peripheral device list for the front-end edge computing module, which is used to build an M2M network and automatically determine the status of the server when it is offline.

[0034] The fog computing module is based on the on-site ring network or 4G / 5G / IoT network, combined with the location of on-site sensors and the layout of equipment in the airport, to automatically generate M2M network areas and send them to the front-end controller, enabling regional autonomy in the event of network interruption.

[0035] The fog computing module enables the access and distribution of multi-source dynamic business data. The business data is internal enterprise data (such as relevant flight information, ground support service resource needs, aircraft maintenance business needs, airfield surface maintenance and construction information, surface weather dynamics, etc.). The fog computing module performs business processing within the module to generate associated lighting plans.

[0036] The cloud computing module provides various front-end service distribution support based on internet-connected client programs, web pages, mobile apps, and WeChat mini-programs. The cloud computing module can provide services to multiple airports, enabling centralized lighting strategy management and data statistics.

[0037] For details, please refer to Figure 2 The edge computing module can integrate 4G / 5G / LoRa private network coverage, access to various indoor and outdoor sensors (sunlight, wind speed, etc.), and security-related access. Combined with existing fiber optic ring networks and operator network access, it can simplify front-end deployment, facilitate installation in outdoor environments and indoor environments within terminals, maximize airport IoT coverage, and improve the efficiency and management level of security.

[0038] Specifically, the system of this invention adopts a three-tier architecture: the three-tier architecture consists of cloud computing, fog computing, and local edge computing layers, working in conjunction with the client to achieve complete functionality. Loose coupling is maintained between each layer. The client layer primarily provides user interaction functions. The cloud computing and fog computing layers deploy all business logic as software components, enabling all user services. The edge computing layer enables business offloading, achieving high reliability and high-speed response. The application of this three-tier architecture gives the system numerous advantages and characteristics. The three-tier architecture reduces dependencies between layers, fully embodying the design philosophy of "high cohesion, low coupling," making the entire architecture and service provision more standardized, the structure clearer, and facilitating module reuse. This significantly reduces maintenance costs and time, while improving the system's "maintainability" and "scalability," making agile software development based on customer needs possible.

[0039] The system of this invention utilizes cloud computing to deploy one instance nationwide or across an entire airport group, achieving maximum management and convenient application. Combined with scheduling strategies and load control capabilities, it distributes reasonable workloads across multiple airports, greatly improving the product's service capabilities and the airport group's management and control capabilities, reducing the cost of purchasing services for users, and enhancing the user experience.

[0040] This invention's cloud computing module, fog computing module, and edge computing module all include a rule engine: frequently changing business rules are extracted from the program and placed into a rule base for management and modification. The basic process of the rule engine is to test and compare the submitted factual data objects with the business rules loaded in the engine, activate those business rules that match the current state of the factual objects, trigger corresponding operations in the system, and complete the change of certain state information in the information system. The rule engine can demonstrate significant advantages in situations where business rules change frequently and constraint relationships are complex. Based on the rule engine, this invention can achieve dynamic upgrades by distributing business constraint rules layer by layer, thereby enabling complex calculations such as business scenario simulation calculations, on-site emergency drills, and fault handling.

[0041] This invention achieves integrated automated processing of indoor and outdoor lighting by unifying sensors and IoT networks within an airport. It enables collaborative computing by configuring cloud computing, fog computing, and edge computing at the front end, thus integrating production information with lighting support. This significantly reduces the workload of dispatchers in their daily tasks, allowing them to shift their focus from busy information dissemination and entry to more rational planning and utilization of resources, thereby improving the overall level of collaborative operation and production efficiency.

[0042] The cloud-edge collaborative computing module tightly integrates cloud computing and edge computing. By rationally allocating tasks between cloud and edge computing, it achieves the decentralization of cloud computing, extending cloud computing and cloud analytics to the edge computing module. In the event of server network failure, the cloud computing module automatically activates relevant lighting based on its own sensor data, including automatic lighting at night and in abnormal weather conditions. The cloud computing module obtains weather forecasts based on latitude and longitude coordinates, generates sunrise and sunset times, and produces an overall operational plan. Local fog calculation can distribute data based on cloud computing data, fine-tuning and issuing warnings in conjunction with on-site sensor data. If cloud computing data is unavailable, it can automatically evaluate and adjust based on historical data, generating a conservative lighting plan. The edge computing component supports M2M networks, enabling highly reliable autonomous control. In the event of local server failure or network failures at some nodes, it can perform real-time dynamic business processing based on previous lighting plans and real-time sensor data, maximizing reliable processing and meeting flexible business needs. The edge computing module provides support for various protocols and technologies, including video and industrial protocols, depending on the site environment. It supports heterogeneous access, integrates multiple sensor information and AI technologies, and achieves scenario-based automated edge computing capabilities. It outputs analysis results in real-time for dynamic processing, ensuring that any network interruption will not affect the execution of pre-scheduled plans and the integration of data from on-site sensors and monitoring, guaranteeing business continuity. Under normal circumstances, the local server handles sensor data fusion and processing. In the event of server failure or network anomalies, the local edge computing node can take over, comparing sensor data from surrounding nodes via the M2M network to determine data reliability and automatically execute relevant business processes. Normal flight lighting is issued by the local server according to the flight plan. However, when the local node is offline, it can combine accessed monitoring data to determine the presence of aircraft at the gate outdoors and detect personnel indoors using infrared sensors. If personnel are present, lighting can be temporarily activated for automated assistance.

[0043] It also includes a dispatch client, which is used to receive dispatch task information issued by the dispatch command center, complete the inspection and patrol of airport service resources, and report the inspection progress information.

[0044] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An airport ground intelligent command and dispatch system based on cloud edge collaborative computing, characterized in that, include: The mobile terminal is used to view the lighting plan and business attribute information of lighting fixtures via 4G / 5G / WiFi networks, and to directly and manually set the switch of the lighting fixtures, test the status of the lighting fixtures and report faults, and fill in inspection and maintenance data. Desktop terminals are used to monitor the operating status of lighting fixtures, review and adjust lighting fixture usage plans, and conduct overall inspections and information maintenance. The edge computing module is used to connect front-end sensors and network devices, perform artificial intelligence calculations and fusion, and communicate with the back-end network. It receives lighting plans from the lighting center through the Internet of Things, detects and reports power usage and lamp integrity, controls the execution of lighting plans and on-site manual forced switching settings, and realizes extended business according to business needs. The fog computing module is used to interface with the lighting control center and the airport's business system. It imports information resources such as flight stand occupancy, business support lighting needs, fire alarm and alarm information, and fault and maintenance records. Combined with front-end sensor data provided by the edge computing module, it performs automated and unified lighting plan processing to optimize lighting. The fog computing module distributes data based on the cloud computing module and adjusts and issues warnings based on the front-end sensor data. If it cannot receive data from the cloud computing module, it can automatically evaluate and adjust based on historical data to generate a conservative lighting plan. The cloud computing module is used to collect information on the usage of information resources from the fog computing module and provide data publishing and mobile terminal access via the Internet. It supports centralized management and publishing for multiple airports, and realizes unified rule management and automated statistics for the group. It also includes a cloud-edge collaborative computing module, which combines the cloud computing module and the edge computing module, allocates tasks between the cloud computing module and the edge computing module, realizes the sinking of the cloud computing module, and extends cloud computing and cloud analytics to the edge computing module; when the server network fails, the cloud computing module automatically starts relevant lighting based on its own sensor data, and starts automatic lighting at night and in abnormal weather. When both the fog computing module and the cloud computing module fail to connect, after reaching the predefined number of network retries and timeout requirements, information is queried based on a pre-set list of peripheral devices. An M2M network is constructed for comprehensive judgment, and if necessary, associated lights are automatically activated for nighttime and abnormal weather conditions. The edge computing module integrates on-site sensor data, such as on-site illuminance and wind speed, and evaluates the current on / off status and time of the lights to confirm whether there is insufficient illuminance during the day when the lights are not turned on. Lights are turned on when there is cloudy or rainy weather. If the illuminance is insufficient after the lights are turned on at night, it is considered a performance failure of the lights, and a relevant alarm is sent to the fog computing module. When the fog computing module fails to connect but the cloud computing module connects, the associated lighting plan is obtained through the cloud computing module, and the lighting status is uploaded to the cloud computing module. The statistical data is temporarily stored locally and re-uploaded after the fog computing module recovers.

2. The cloud-edge collaborative computing-based airport ground intelligent command and dispatch system according to claim 1, characterized in that, The fog computing module, edge computing module, and mobile terminal are interconnected and access each other via the Internet of Things, and the accessed data content is encrypted.

3. The cloud-edge collaborative computing-based airport ground intelligent command and dispatch system according to claim 1, characterized in that, The cloud computing module obtains weather forecasts based on latitude and longitude coordinates, generates sunrise and sunset times, and generates an overall operation plan. The edge computing module, in the event of local server failure or network failure of some nodes, performs real-time dynamic business processing based on the previously executed lighting plan and combined with front-end sensor data. This edge computing module provides video and industrial protocol support according to the site environment, realizes scenario-based automated edge computing capabilities, and outputs analysis results in real time to achieve dynamic processing. Any network interruption will not affect the execution of the pre-scheduled plan and the data combined with on-site sensors and monitoring, ensuring business continuity.

4. The cloud-edge collaborative computing-based airport ground intelligent command and dispatch system according to claim 3, characterized in that, It also includes a dispatch client, which is used to receive dispatch task information issued by the dispatch command center, complete the inspection and patrol of airport service resources, and report the inspection progress information.

5. The cloud-edge collaborative computing-based airport ground intelligent command and dispatch system according to claim 1, characterized in that, The fog computing module has complete local network front-end device and sensor information, and automatically configures the peripheral device list for the front-end edge computing module, which is used to build an M2M network and automatically determine the status of the server when it is offline.

6. The cloud-edge collaborative computing based airport ground intelligent command and dispatch system according to claim 5, characterized in that, The fog computing module is based on the on-site ring network or 4G / 5G / IoT network, combined with the location of on-site sensors and the layout of equipment in the airport, to automatically generate M2M network areas and send them to the front-end controller, enabling regional autonomy in the event of network interruption.

7. The cloud-edge collaborative computing based airport ground intelligent command and dispatch system according to claim 1 or 6, characterized in that, The fog computing module enables the access and distribution of multi-source dynamic business data. The business data is internal enterprise data, which is processed within the fog computing module to generate associated lighting plans.

8. The cloud-edge collaborative computing based airport ground intelligent command and dispatch system according to claim 1 or 6, characterized in that, The cloud computing module provides various front-end business distribution support based on Internet access client programs, web pages, mobile APPs, and WeChat mini programs. The cloud computing module provides services to fog computing modules of multiple airports, including data statistics and rule recording.

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