Air traffic control signal path optimization method and path optimization system based on energy consumption perception

Abstract

The application provides an air traffic signal path optimization method and system based on energy consumption perception, wherein the path optimization method comprises the following steps: collecting air traffic signals through grid-distributed signal receiving nodes; monitoring the power supply state of each node in real time, and predicting the energy consumption trend of the node; evaluating the quality level of the transmission of each node according to the power supply state and the energy consumption trend of each node; excluding unusable nodes according to the quality level of the transmission of each node, and re-forming a new available grid; and selecting an optimal path planning algorithm according to the size of the new available grid, and performing signal transmission. Through monitoring the power supply state of each node and predicting the energy consumption trend of the node, the application can dynamically avoid abnormal nodes, reduce the signal interruption probability, select an optimal path planning algorithm according to the size of the new available grid, reduce the occupation of computing resources, and improve the transmission efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air traffic control, and in particular to an air traffic control signal path optimization method and system based on energy consumption awareness. BACKGROUND

[0002] Currently, in the field of civil air traffic control, with the rapid development of business, the communication, navigation and surveillance business guarantee faces the situation of increasing system complexity and rapid growth of equipment scale.

[0003] The air traffic control system relies on signal receiving nodes (such as radar equipment) to monitor the position of the aircraft. In the prior art, signal transmission path planning is mostly based on fixed algorithms, ignoring the influence of energy consumption state on signal quality. In actual application, energy consumption fluctuation will cause signal receiving power to decrease and transmission delay to increase, affecting the reliability of air traffic control signal transmission.

[0004] Therefore, how to improve the signal transmission reliability of the air traffic control system is a problem to be solved at present. SUMMARY

[0005] The purpose of the present application is to provide an air traffic control signal path optimization method and system based on energy consumption awareness, which can improve the signal transmission reliability of the air traffic control system.

[0006] In order to achieve the above purpose, the present application provides an air traffic control signal path optimization method based on energy consumption awareness, comprising:

[0007] Collecting air traffic signals through grid-distributed signal receiving nodes;

[0008] Real-time monitoring of the power supply state of each node and prediction of the energy consumption trend of the node;

[0009] According to the power supply state and energy consumption trend of each node, the quality level of the transmission of each node is evaluated;

[0010] According to the quality level of the transmission of each node, the unusable nodes are excluded, and a new available grid is formed;

[0011] According to the size of the new available grid, the optimal path planning algorithm is selected for signal transmission.

[0012] In an optional solution, the power supply mode of the node adopts a hierarchical power supply mode and can be adaptively adjusted.

[0013] In an optional solution, the method for predicting the energy consumption trend of the node comprises:

[0014] Using a machine learning algorithm to analyze historical energy consumption data and predicting the future energy consumption trend of the node according to the function of the node.

[0015] In an optional solution, the quality level of transmission of each node is evaluated according to the power supply state and energy consumption trend of each node, including:

[0016] A mapping relationship between the energy consumption data and the node working state is established in advance, a threshold is set, and the quality level of each node is determined.

[0017] In an optional solution, the optimal path planning algorithm is selected according to the size of the available grid, including:

[0018] According to historical data, a relationship curve of the calculation time and the size of the grid of each path algorithm is obtained in advance, and an inflection point is found;

[0019] The applicable range of each path algorithm is determined according to the size of the grid corresponding to the inflection point;

[0020] The optimal path algorithm is selected according to the size of the currently available grid.

[0021] The application also provides an air traffic signal path optimization system based on energy consumption perception, including:

[0022] The signal acquisition module is configured to acquire air traffic signals through a plurality of signal receiving nodes distributed in a grid shape;

[0023] The energy monitoring module monitors the power supply state of each node in the layered power supply topology and predicts the energy consumption trend of the node in real time;

[0024] The evaluation module evaluates the quality level of transmission of each node according to the power supply state and energy consumption trend of each node; according to the quality level of transmission of each node, the unusable nodes are excluded, and a new available grid is reformed;

[0025] The path optimization module contains a plurality of path planning algorithms, and the optimal path planning algorithm is selected according to the size of the available grid to perform signal transmission.

[0026] In an optional solution, the energy monitoring module includes;

[0027] The layered power supply monitoring unit uses a smart meter to monitor the total power supply and floor power supply state in real time;

[0028] The battery management unit monitors the power and health state of the backup battery and predicts the remaining use time;

[0029] The fault detection unit detects power supply abnormalities through real-time data stream analysis and triggers an alarm.

[0030] In an optional solution, the quality level of transmission of each node is evaluated according to the power supply state and energy consumption trend of each node, including:

[0031] The mapping relationship between energy consumption data and node working state is established in advance, a threshold is set, and the quality level of each node is determined.

[0032] In an optional solution, the optimal path planning algorithm is selected according to the size of the available grid, and the optimal path planning algorithm includes:

[0033] According to historical data, the relationship curve of the calculation time and the size of the grid of each path algorithm is obtained in advance, and the inflection point is found;

[0034] According to the size of the grid corresponding to the inflection point, the applicable range of each path algorithm is determined;

[0035] According to the size of the currently available grid, the optimal path algorithm is selected.

[0036] The beneficial effects of the present application are that the present application can dynamically avoid abnormal nodes and reduce the probability of signal interruption by monitoring the power supply state of each node and predicting the energy consumption trend of the node. According to the size of the new available grid, the optimal path planning algorithm is selected to reduce the occupation of computing resources and improve the transmission efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0037] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description of exemplary embodiments of the present application taken in conjunction with the accompanying drawings, in which like reference characters refer to the like parts throughout the figures, and in which:

[0038] Figure 1 The flowchart of the air traffic control signal path optimization method based on energy consumption perception in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The present application will be further described in detail with reference to the accompanying drawings and specific embodiments. According to the following description and drawings, the advantages and features of the present application will be more apparent, however, it should be noted that the technical solution of the present application can be implemented in various forms and is not limited to the specific embodiments described herein. The drawings are greatly simplified and use non-precise proportions, and are only used to facilitate and clarify the purpose of illustrating the embodiments of the present application.

[0040] It will be understood that when an element or layer is referred to as being "on" or "connected to" another element or layer, it can be directly on or connected to the other element or layer or intervening elements or layers can be present. In contrast, when an element is referred to as being "directly on" or "directly connected to" another element or layer, there are no intervening elements or layers present. It will be understood that, although the terms first, second, third, etc. can be used herein to describe various elements, components, regions, layers and / or sections, these elements, components, regions, layers and / or sections should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer or section from another element, component, region, layer or section. Thus, a first element, component, region, layer or section discussed below could be termed a second element, component, region, layer or section without departing from the teachings of the present application.

[0041] Spatially relative terms, such as "beneath", "below", "lower", "under", "above", "upper" and the like, can be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, elements described as "below" or "beneath" other elements or features would then be oriented "above" or "over" the other elements or features. Thus, the exemplary term "below" can encompass both an orientation of above and below. The device can be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.

[0042] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising", when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0043] Embodiment 1

[0044] Referring to Figure 1 The embodiment provides an energy consumption-aware air traffic control signal path optimization method, comprising:

[0045] Air traffic signals are collected by signal receiving nodes distributed in a grid pattern.

[0046] The power supply state of each node is monitored in real time, and the energy consumption trend of the node is predicted.

[0047] According to the power supply state and energy consumption trend of each node, the quality level of the transmission of each node is evaluated.

[0048] According to the quality level of the transmission of each node, the unusable nodes are excluded, and a new available grid is formed.

[0049] According to the size of the new available grid, the optimal path planning algorithm is selected for signal transmission.

[0050] Specifically, signals are collected by nodes distributed in a grid pattern. Each node covers a certain area, and the grid structure ensures no blind area monitoring. The node spacing is optimized based on the signal attenuation model to avoid interference.

[0051] In this embodiment, the power supply mode of the node adopts a hierarchical power supply mode and can be adjusted adaptively. Hierarchical power supply means that a node (device) has multiple power supply modes, such as a total power supply layer: external power grid provides direct power; building power supply layer: the building generator supplies power to the devices in the whole building, and the power supply of a certain floor of the building is distributed by the intelligent distribution box and the branch state is monitored. Device-level power supply layer: the node is powered by the built-in battery. The node adopts a hierarchical power supply mode, which can switch to a generator power supply mode or an internal battery power supply mode when the external power grid cannot supply power, which can effectively ensure the power supply of the node and prevent the node power failure from affecting signal transmission.

[0052] Real-time monitoring of power supply state, such as power supply mode, voltage stability, etc., and using machine learning algorithm to analyze historical energy consumption data, according to the difference of node function (such as signal receiving intensity, data processing load), to predict the future energy consumption trend of the node. Specifically: collect historical energy consumption data of the node (such as power consumption curve, load change), use time series database (such as InfluxDB) to store data, train LSTM network to identify energy consumption mode, adjust model weight according to node function (such as high frequency signal processing node energy consumption is higher), output future energy consumption trend, provide basis for quality evaluation.

[0053] Based on the power supply state and energy consumption trend, the quality level of the node transmission is evaluated. The quality level is divided according to the preset threshold (such as high quality, available, unavailable), and the unusable nodes are excluded to form a new available grid, ensuring the reliability of the path. The method for evaluating the quality level of the transmission of each node includes: pre-establishing the mapping relationship between energy consumption data and node working state (such as when the power is less than 20%, the node quality is degraded), setting threshold, dividing the node into multiple quality levels (such as A level: energy sufficient, signal high quality; C level: energy insufficient, need to be avoided), guiding path selection.

[0054] The optimal algorithm is selected according to the size (number of nodes x number of layers) of the new grid. The algorithm selection is based on historical performance curves to avoid waste of computing resources. The selection of the optimal path planning algorithm includes: according to historical data, obtaining the relationship curve of the calculation time of each path algorithm and the size of the grid in advance, and finding the inflection point; according to the grid size corresponding to the inflection point, determining the applicable range of each path algorithm; according to the size of the current available grid, selecting the optimal path algorithm. More specifically, based on historical data, draw the time consumption-grid size curve of each algorithm (such as A* and Dijkstra), and fit the function to find the inflection point (efficiency mutation point). The grid size corresponding to the inflection point is used as the algorithm switching threshold, the current grid size is compared with the inflection point, and the algorithm with the minimum time consumption is selected. For example, Dijkstra is used for small-scale grid and A* is used for large-scale grid.

[0055] Embodiment 2

[0056] The embodiment provides an air traffic signal path optimization system based on energy consumption perception, comprising:

[0057] A signal acquisition module configured to acquire air traffic signals through a plurality of signal receiving nodes distributed in a grid shape;

[0058] An energy monitoring module for real-time monitoring of the power supply state of each node in the hierarchical power supply topology and prediction of the energy consumption trend of the node;

[0059] An evaluation module for evaluating the quality level of each node transmission according to the power supply state and energy consumption trend of each node, excluding unusable nodes, and re-forming a new available grid;

[0060] A path optimization module containing a plurality of path planning algorithms, selecting the optimal path planning algorithm according to the size of the available grid, and performing signal transmission.

[0061] Specifically, the signal acquisition module includes radar sensors and pre-processing units on hardware, and realizes signal filtering and data encapsulation on software. The energy monitoring module includes: a hierarchical power supply monitoring unit that uses a smart meter to monitor the total power supply and floor power supply status in real time; a battery management unit that monitors the power and health status of the backup battery and predicts the remaining use time; a fault detection unit that detects power supply abnormalities (such as voltage sag) through real-time data stream analysis and triggers an alarm. The evaluation module pre-establishes a mapping relationship between energy consumption data and node working status, sets a threshold, and determines the quality level of each node. According to the quality level of each node transmission, unusable nodes are excluded, and a new available grid is formed. The path optimization module pre-obtains the relationship curve of each path algorithm about the calculation time and the size of the grid according to historical data, and finds the inflection point; according to the size of the grid corresponding to the inflection point, the applicable range of each path algorithm is determined; according to the size of the current available grid, the optimal path algorithm is selected.

[0062] The present application can dynamically avoid abnormal nodes and reduce signal interruption probability by monitoring the power supply status of each node and predicting the energy consumption trend of the node. According to the size of the new available grid, the optimal path planning algorithm is selected to reduce the occupation of computing resources and improve transmission efficiency.

[0063] The above description is only a description of the preferred embodiments of the present application, and is not any limitation on the scope of the present application. Any modification or modification made by a person skilled in the art according to the above disclosure is within the protection scope of the claims.

Claims

1. An energy consumption-aware air traffic control signal path optimization method, characterized in that, The method comprises: Collecting air traffic signals through a grid of signal receiving nodes; Monitoring the power supply state of each node in real time and predicting the energy consumption trend of the node; Evaluating the quality level of the transmission of each node according to the power supply state and energy consumption trend of the node; Excluding unusable nodes according to the quality level of the transmission of each node and re-forming a new available grid; Selecting an optimal path planning algorithm according to the size of the available grid for signal transmission; The method of selecting an optimal path planning algorithm according to the size of the available grid comprises: Obtaining the relationship curve between the calculation time and the size of the grid for each path algorithm according to historical data, and finding the inflection point; Determining the applicable range of each path algorithm according to the size of the grid corresponding to the inflection point; Selecting the optimal path algorithm according to the size of the current available grid.

2. The energy consumption-aware air traffic control signal path optimization method according to claim 1, wherein, The power supply mode of the node adopts a hierarchical power supply mode and can be adaptively adjusted.

3. The energy consumption-aware air traffic control signal path optimization method according to claim 1, wherein, The method of predicting the energy consumption trend of the node comprises: Using a machine learning algorithm to analyze historical energy consumption data and predicting the future energy consumption trend of the node according to the function of the node.

4. The energy consumption-aware air traffic control signal path optimization method according to claim 1, wherein, Evaluating the quality level of the transmission of each node according to the power supply state and energy consumption trend of the node comprises: Pre-establishing the mapping relationship between energy consumption data and node working state, setting a threshold, and determining the quality level of each node.

5. An energy consumption-aware based air traffic control signal path optimization system, characterized in that, The method comprises: A signal collection module configured to collect air traffic signals through a grid of multiple signal receiving nodes; An energy monitoring module for monitoring the power supply state of each node in a hierarchical power supply topology structure in real time and predicting the energy consumption trend of the node; An evaluation module for evaluating the quality level of the transmission of each node according to the power supply state and energy consumption trend of the node; Excluding unusable nodes according to the quality level of the transmission of each node and re-forming a new available grid; A path optimization module containing multiple path planning algorithms, which selects an optimal path planning algorithm according to the size of the available grid for signal transmission; The method of selecting an optimal path planning algorithm according to the size of the available grid comprises: Obtaining the relationship curve between the calculation time and the size of the grid for each path algorithm according to historical data, and finding the inflection point; Determining the applicable range of each path algorithm according to the size of the grid corresponding to the inflection point; Selecting the optimal path algorithm according to the size of the current available grid.

6. The energy consumption-aware based air traffic control signal path optimization system of claim 5, wherein, The energy monitoring module comprises: A hierarchical power supply monitoring unit that uses a smart meter to monitor the total power supply and floor power supply state in real time; A battery management unit that monitors the power and health status of the backup battery and predicts the remaining use time; A fault detection unit that detects power supply abnormalities through real-time data stream analysis and triggers an alarm.

7. The energy consumption-aware based air traffic control signal path optimization system of claim 5, wherein, Evaluating the quality level of the transmission of each node according to the power supply state and energy consumption trend of the node comprises: Pre-establishing the mapping relationship between energy consumption data and node working state, setting a threshold, and determining the quality level of each node.

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