Airport device transmission frequency adjustment method and system based on multi-dimensional risk assessment
By using a multi-dimensional risk assessment method to dynamically adjust the transmission frequency of airport equipment, the safety hazards and resource waste caused by fixed frequencies have been resolved, and efficient airport equipment monitoring and network resource management have been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-03
- Publication Date
- 2026-03-13
AI Technical Summary
The existing airport equipment uses a fixed transmission frequency, which cannot provide sufficient monitoring granularity in high-risk scenarios, leading to security risks. At the same time, blindly increasing the frequency will increase power consumption and occupy network resources, affecting the communication of other terminals.
A multi-dimensional risk assessment method is adopted, which uses the ray casting method to determine the risk level of the device area. Combined with multi-threaded parallel processing to calculate the device density and environmental risk level, a weighted model is constructed to dynamically adjust the transmission frequency, priority set and bandwidth allocation strategy to ensure high-frequency transmission of critical devices.
It has enabled precise monitoring of the core sensitive areas of the apron, improved the speed of identifying and responding to safety hazards, increased the utilization rate of network resources, and reduced the power consumption and packet loss rate of key devices.
Smart Images
Figure CN121661870A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of digital and information technology for civil airports in the civil aviation industry, and in particular to a method and system for adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment. Background Technology
[0002] In recent years, with the continuous growth of air traffic, the airport apron operating environment has become increasingly complex, and the activity density of various ground support equipment, vehicles and aircraft has increased significantly, putting enormous pressure on apron safety operations. In order to improve the level of operational safety, some airports have begun to install status monitoring devices on key apron operating elements (such as aircraft tow bars, ground power devices, etc.) to collect their location, status and other data in real time and transmit them back to the monitoring platform in order to achieve comprehensive perception of the apron operating situation and risk warning.
[0003] Currently, such monitoring devices generally employ a fixed-frequency data transmission mechanism, meaning that data is transmitted at preset time intervals regardless of the risk level of the environment in which the equipment is located. This approach has significant limitations: First, in high-risk operational scenarios (such as aircraft pushback, high-density vehicle cross-operations, etc.), a fixed frequency may not provide sufficient monitoring granularity, making it difficult to capture abnormal dynamics in a timely manner, thus creating potential safety hazards. Second, blindly increasing the transmission frequency of all devices to meet the monitoring needs in high-risk situations will lead to a significant increase in device power consumption, shorten the working time of battery-powered equipment, and also squeeze the limited wireless network bandwidth, potentially affecting the normal communication of other terminals and even causing network congestion. Therefore, the existing fixed-frequency transmission mode cannot balance the effectiveness of safety monitoring with the economy of resource utilization. At present, there is a need for a method and system for adjusting the transmission frequency of airport devices based on multi-dimensional risk assessment. Summary of the Invention
[0004] This invention provides a method and system for dynamically adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment, in order to solve the problems caused by fixed transmission frequencies in existing technical solutions.
[0005] In a first aspect, the airport device transmission frequency adjustment method based on multi-dimensional risk assessment provided by the present invention adopts the following technical solution: A method for adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment, comprising: S1. In response to the transmission frequency recalculation event, obtain the target device list, and obtain the reference value set, type set, accuracy set, and transmission status set of the target devices based on the target device list; S2. Based on the target device list, obtain the target device location list and the preset area list, and use the ray method to determine the area where each location in the target device location list is located, and determine the risk level set of the target device area. S3. Traverse the target device list, calculate the positional relationship between each target device and other devices and aircraft, and determine the environmental density of the target devices by combining multi-threaded parallel processing to obtain the density level set. S4. Acquire meteorological data and equipment anomaly data, calculate the target equipment location change level, and use an empirical rule-based decision tree algorithm to integrate meteorological factors, location change level factors, and equipment anomaly state factors to calculate the target equipment environmental risk level. S5. Standardize the environmental risk level set, regional risk level set, type set and accuracy set, and construct a weighted model by setting the weights of each parameter based on the standardization results, and calculate the target device priority set. S6. Obtain the set of device transmission networks, calculate the available bandwidth of each transmission network, and determine the transmission frequency of each target device by combining the target device list, target device priority set and device transmission status, and issue an update command.
[0006] Furthermore, the step of obtaining the basic parameter set of the target device based on the target device list includes: S11. Obtain the target device reference value set based on the target device list. , , For the first The target device baseline value is n, where n is the number of target devices; S12. Obtain the target device type set , For the first One target device type, A value of 1 or 2 represents low power consumption and continuous power supply, respectively. S13. Obtain the target device accuracy set , For the first The accuracy of the target device The value is 1, 2, or 3, representing low precision, medium precision, and high precision, respectively. S14. Obtain the target device transmission status set , For the first Transmitting status of each target device The value is 0 or 1, representing normal and abnormal respectively. The target device is a status monitoring device installed on the apron operating elements and is equipped with a GNSS sensor.
[0007] Furthermore, determining the risk level set for the target device area includes obtaining the most recent list of target device locations based on the target device list. At the same time, obtain the list of preset areas of the airport apron. Iterate through the target device location list P, for each target device location... The ray method is used to determine the results sequentially. The preset area where it is located, when When the device is located in multiple areas, the highest risk level in each area is taken as the risk reference for that area. When the device is not in any region, the region where the device is located is assumed to be of low risk level, which ultimately forms a set of regional risk levels. in, This refers to the location information most recently recorded by the i-th target device. Let m be the j-th preset region, and m be the total number of preset regions.
[0008] Furthermore, the method of sequentially judging by ray casting... The preset area includes the preset area adjusted by the average correction algorithm. The boundary point set is modified with coordinates to adjust the target device location. The latitude and longitude coordinates of the boundary points and the boundary points of the boundary point set are converted into Cartesian coordinates using the Kruger projection transformation algorithm. Draw a horizontal line to the left from the starting point, and calculate the intersection of the line segment and the horizontal line. The number of intersections; when the number of intersections is odd, set... exist Internally, when the number of intersections is even, set exist external.
[0009] Furthermore, the determination of the target device environment density by combining multi-threaded parallel processing includes: S31. Obtain the latest location dataset of all devices, the list of aircraft operating on the apron, the list of aircraft status, and the latest location dataset of aircraft. S32. Build a multi-threaded processing architecture, determine the total number of threads based on the number of server CPU cores and establish a thread set, sort the target device list by ID and divide it into thread subsets, and deploy a mutex lock synchronization mechanism. S33. Each thread traverses the subset of target devices it is responsible for. For each target device, the Haversine formula is used to calculate the spherical distance between the target device and the central point of the latest point data of all devices, and the spherical distance between the target device and the central point of the latest point data of the aircraft. The distance count is completed based on the distance threshold and the aircraft status. S34. Based on the counting results, the cumulative density value is calculated using a weighted formula. The density level of the target device is determined by combining the density threshold. After all threads have completed the calculation, the main thread summarizes the results to obtain the density level set.
[0010] Furthermore, the fusion of multiple factors yields a set of environmental risk levels for the target device, including: S41. Obtain multi-source risk correlation data, which includes meteorological data, device anomaly data, and target device location data. S42. Calculate the location change level based on the target device location data. Calculate the distance between adjacent locations of the target device within a preset time period using the Haversine formula to obtain the total length of the location set. Combine this with a location length threshold to classify the location change level. The expression for the change level is: ; in, Indicates the total length of the point set; S43. A decision tree algorithm is constructed to convert meteorological data and location change levels into meteorological correction values and location change correction values, respectively. Finally, the device anomaly data is combined with the density level to convert it into device anomaly correction values. The expression for the meteorological correction value is as follows: ; in, This indicates rainfall level data. This indicates snow level data. Indicates fog level data, This represents day / night status data, with values of 1 or 2, where 1 corresponds to daytime and 2 corresponds to nighttime. It represents the "OR" relationship in logical operations; S44. By integrating density level with meteorological correction value, device anomaly correction value and location change correction value through decision tree algorithm, the environmental risk level of the target device is calculated, and an environmental risk level set is formed after traversing all target devices.
[0011] Furthermore, the calculation of the target device priority set includes setting standardized environmental risk level, regional risk level, device type, device accuracy, and target device baseline value as weight parameters based on airport apron operation safety requirements and monitoring priority. A weighted calculation model is constructed, multiplying the standardized environmental risk level, regional risk level, device type, and device accuracy by their corresponding weight parameters, and then adding the product of the target device baseline value and its weight to obtain the priority of each individual target device. This process iterates through all target devices in the target device list, obtaining the priority value for each device, and sorting them according to the target device list to form the target device priority set. The formula for calculating the priority of a single target device is as follows: ; in, This represents the standardized value of the environmental risk level of the i-th target device. This represents the standardized value of the regional risk level for the i-th target device. This represents the type-normalized value of the i-th target device. This represents the standardized accuracy value of the i-th target device. This represents the reference value of the i-th target device. , , , and These represent the weight parameters of each input parameter.
[0012] Furthermore, the calculation of the available bandwidth of each transmission network includes obtaining the set of transmission networks and network parameters of the target device, calculating the available bandwidth of the target device according to the type of each transmission network. When the transmission network is of static bandwidth type, the reference bandwidth is directly determined as the available bandwidth of the target device under that network. When the transmission network is of dynamic bandwidth type, the available bandwidth is calculated using a bandwidth calculation formula, which is expressed as follows: ; in, This indicates the total number of aircraft operating within the apron area. This function represents the minimum value and is used to ensure that the calculated available bandwidth does not exceed the total network bandwidth. Indicates the total bandwidth of the transmission network. This represents the reference bandwidth available to the target device within the transmission network. This indicates adjustable weights in the transmission network. This indicates the number of adjustable reference aircraft under the transmission network.
[0013] Furthermore, determining the transmission frequency of each target device includes filtering out a set of target devices with normal transmission status under the transmission network based on the target device mapping relationship of the transmission network and the target device transmission status data. ,based on Call the target device priority set, obtain the corresponding priority set, and iterate through it. ,right Each target device Calculate its available bandwidth, and calculate the transmission frequency based on the available bandwidth and the average data transmission size. The formula for calculating the available bandwidth is as follows: ; in, This represents the set of target devices in a normal state. This represents a specific target device within the constant set of target devices. This represents the available bandwidth of the target device under the a-th transmission network. This represents the b-th normal target device under the a-th transmission network. This represents the sum of priorities of all normally functioning target devices under the a-th transmission network.
[0014] Secondly, an airport equipment transmission frequency adjustment system based on multi-dimensional risk assessment includes: The target device acquisition module is configured to: in response to a transmission frequency recalculation event, acquire a list of target devices and acquire a basic parameter set of the target devices based on the list of target devices; The regional risk module is configured to: obtain a list of target device locations and a preset regional list based on the target device list; use the ray casting method to determine the region where each location in the target device location list is located; and determine the set of regional risk levels for the target device. The density calculation module is configured to: traverse the target device list, calculate the positional relationship between each target device and other devices and aircraft, determine the environmental density of the target device by combining multi-threaded parallel processing, and obtain a density level set; The environmental risk module is configured to: acquire meteorological data and device anomaly data, calculate the target device location change level, and use an empirical rule-based decision tree algorithm to integrate multiple factors to obtain the target device environmental risk level set; The priority module is configured to: standardize the environmental risk level set, regional risk level set, type set, and accuracy set, and construct a weighted model based on the standardization results to calculate the priority set of the target device; The update module is configured to: obtain the set of device transmission networks, calculate the available bandwidth of each transmission network, and determine the transmission frequency of each target device by combining the target device list, the target device priority set and the device transmission status, and issue an update command.
[0015] In summary, the present invention has the following beneficial technical effects: 1. This invention uses ray casting combined with coordinate correction and projection transformation to identify the preset area where the target device is located. For scenarios with overlapping areas, the highest risk level is assigned to ensure the monitoring priority of devices in the core sensitive areas of the apron. Simultaneously, a multi-threaded parallel processing architecture is employed to calculate the spatial distance between the device and other devices and aircraft in real time, quantifying the device's environmental density and avoiding operational interference and safety hazards caused by densely packed devices or nearby aircraft. Compared to traditional manual methods of dividing area risks, this technology achieves automated and precise assessment of regional and location risks, ensuring blind-spot-free monitoring coverage of devices in key areas, and significantly improving the speed of apron safety hazard identification and response.
[0016] 2. This invention constructs a decision tree algorithm based on empirical rules to convert meteorological conditions, abnormal device states, and location changes into quantified correction values. These values are then integrated with device density levels to form an environmental risk assessment system. Meteorological conditions are logically differentiated between severe and generally unfavorable scenarios, while location changes are reflected through distance calculations to track device dynamics. This ensures that the environmental risk assessment is updated in real time according to weather changes and device movement. Compared to traditional assessment methods using fixed risk thresholds, this technology is more sensitive to dynamic risks on the apron, significantly improving the accuracy and timeliness of identifying abnormal states of high-risk devices, and effectively avoiding monitoring failures caused by misjudgments of risk.
[0017] 3. This invention designs dedicated bandwidth calculation logic for both static and dynamic transmission networks: the static bandwidth network uses the baseline bandwidth to ensure stable supply of device resources for transmission needs; the dynamic bandwidth network dynamically expands bandwidth based on the level of apron traffic (such as the number of aircraft), while avoiding network overload through an upper limit constraint mechanism. This not only meets the stable needs of low-power, low-frequency transmission devices, but also provides elastic bandwidth support for devices in high-traffic scenarios. Compared with the traditional fixed bandwidth allocation method, the network resource utilization rate is significantly improved, and the packet loss rate of critical data transmission is greatly reduced.
[0018] 4. This invention allocates available bandwidth based on the priority ratio of target devices. Through the correlation mapping between priority and bandwidth, critical devices on the apron can obtain higher bandwidth support to meet the needs of high-frequency data transmission. Non-critical devices are allocated bandwidth as needed, avoiding resource idleness. This solves the contradiction of insufficient resources for critical devices and waste of resources for non-critical devices in traditional bandwidth allocation, realizes the tilt of network resources towards high-value monitoring needs, and improves the overall resource input-output ratio. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the overall process of an airport device transmission frequency adjustment method based on multi-dimensional risk assessment according to an embodiment of the present invention.
[0020] Figure 2 This is a flowchart illustrating the execution of the calculation of the environmental density of the target device list in an embodiment of the present invention. Detailed Implementation
[0021] The present invention will be further described in detail below with reference to the accompanying drawings. Example
[0022] Reference Figure 1 This embodiment of a method for adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment includes: S1. In response to the transmission frequency recalculation event, obtain the target device list, and obtain the reference value set, type set, accuracy set, and transmission status set of the target devices based on the target device list; S2. Based on the target device list, obtain the target device location list and the preset area list, and use the ray method to determine the area where each location in the target device location list is located, and determine the risk level set of the target device area. S3. Traverse the target device list, calculate the positional relationship between each target device and other devices and aircraft, and determine the environmental density of the target devices by combining multi-threaded parallel processing to obtain the density level set. S4. Acquire meteorological data and equipment anomaly data, calculate the target equipment location change level, and use an empirical rule-based decision tree algorithm to calculate the target equipment environmental risk level by integrating factors such as meteorology, location change level, and equipment anomaly status. S5. Standardize the environmental risk level set, regional risk level set, type set and accuracy set, and construct a weighted model by setting the weights of each parameter based on the standardization results, and calculate the target device priority set. S6. Obtain the set of device transmission networks, calculate the available bandwidth of each transmission network, and determine the transmission frequency of each target device by combining the target device list, target device priority set and device transmission status, and issue an update command.
[0023] like Figure 1 As shown, specifically, an airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment includes the following: A method for dynamically adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment includes the following steps: S100: The transmission frequency recalculation event is a prerequisite. This event is dynamically triggered by the system's preset rule engine to ensure that the timing of data acquisition accurately matches the actual needs of the apron. In this embodiment, it is automatically triggered at a fixed 30-minute cycle to adapt to the periodic frequency adjustment needs under normal apron operation, avoiding frequency and scenario disconnect due to long-term lack of updates. In addition, the number of aircraft on the apron is monitored in real time by the airport surface surveillance system. When the number increases by more than 50% compared to the average of the previous hour, it is judged as a high workload, triggering data acquisition to cope with dense scenarios and responding to the transmission frequency recalculation event. When the system captures any trigger signal, it immediately obtains the target device list through the dedicated data interface of the apron equipment management platform, such as using the MQTT protocol. in, The number of target devices. For the first One target device to be calculated; based on the device priority configuration table in the apron equipment parameter configuration database associated with the device's unique identifier, obtain the target device's baseline value set. , For the first Based on the baseline values of each target device, the power supply type information of each device is retrieved from the device hardware attribute database to generate a target device type set. , For the first The target device type is specified as either 1 or 2, representing low power consumption and continuous power supply, respectively; the target device accuracy set is obtained from the device performance database. , For the first The precision of the target device is specified as a value of 1, 2, or 3, representing low precision, medium precision, and high precision, respectively; the transmission status set of the target device is obtained. , For the first The target device transmits a status, which is a value of 0 or 1, representing normal and abnormal respectively; S200: According to Get the most recent list of target device locations Get the list of preset areas For the preset number of areas, Indicates the first A preset area is traversed. The ray method is used to determine the results sequentially. Location Ultimately, the risk level of the corresponding device in the area is obtained. ; Specifically, based on the target device list D, the target device location list P is obtained and preprocessed. Through the GNSS data interface of the apron equipment IoT platform, each target device is retrieved using its unique identifier in the target device list D as the search key. The most recent valid location record is used to form a location list. ,in, This provides the location information for the i-th target device, in latitude and longitude coordinates. The data includes a timestamp of the data collection location (accurate to the second) to ensure data timeliness. In this embodiment, only data collection records from the past 5 minutes are retained; records exceeding this timeframe are marked as invalid and require re-collection. The retrieved data is then processed accordingly. Perform two checks: the first is a coordinate range check, to determine... Whether it is within the geographical boundary of the airport apron can be determined by pre-setting the extreme values of apron latitude and longitude. The system performs two checks: first, it determines the location as abnormal if it exceeds the specified range; second, it verifies the data integrity by checking for missing latitude or longitude coordinates. If any are missing, the location is considered incomplete. For abnormal or incomplete locations, the system automatically triggers a retransmission command from the target device's GNSS sensor.
[0024] Retrieve data for a preset area from the airport apron geographic information system (GIS) to obtain a list of preset areas. For the preset number of areas, Indicates the first A preset region, composed of the latitude and longitude coordinates of the vertices of a closed polygon, denoted as . Based on the pre-defined risk levels according to the importance of regional security, this embodiment sets low risk, moderate risk, significant risk, and severe risk, corresponding to values of 1-4 respectively. Since GNSS positioning has minor errors, an averaging correction algorithm is required. The boundary point coordinates are calibrated to eliminate the influence of errors on region determination. The correction formula is as follows: ; ; in, and These are the corrected longitude and latitude of the boundary points, respectively, where K is the total number of boundary points. and This is the average of the coordinates collected from the three historical data points of this boundary point.
[0025] Next, a coordinate projection transformation is performed, unifying the coordinates from latitude and longitude to Cartesian coordinates. Since the ray casting method requires calculating the number of line segment intersections in a plane coordinate system, the target device locations need to be... With the preset region boundary point set The latitude and longitude coordinates are converted into Cartesian coordinates using the Gauss-Kruger projection transformation algorithm. Then, the ray casting method is used to determine the location of the target device. For each target device location... The transformed coordinates are Traverse all preset regions The coordinates of the boundary points after transformation are: Perform the following steps: by Draw a horizontal ray to the left from the starting point, that is, a straight line parallel to the negative x-axis. The equation of the line is: traversal The boundary edges are formed by adjacent boundary points, such as edge 1: Side 2: edge k is Determine whether each boundary edge intersects with the ray; count the number of intersections C. If C is odd, then... exist Internally; if C is even, then determine exist external.
[0026] To avoid misjudgments due to special cases such as overlapping boundary points or collinear rays with boundary edges, the following judgment logic is set: 1. If the y-coordinates of both endpoints of the boundary edge are greater than 1 / 2, then... or both are less than If the ray does not intersect the edge, then the ray will not intersect the edge. 2. If the y-coordinate of one endpoint of the boundary edge is equal to If the y-coordinate of the next adjacent endpoint is compared, then the double-counting is avoided. 3. If the ray is collinear with the boundary edge (i.e., the ray extends along the boundary edge), it is determined that they do not intersect, to prevent errors in the count of occurrences due to collinearity.
[0027] like If the device is determined to be located in multiple preset areas (such as the overlapping area between the aircraft stand and the cargo area), the risk level of these areas is extracted, and the maximum value is taken as the area risk reference value for the device. If the area is not within any preset area (such as an unmarked area at the edge of the apron), the default area risk level is 1, i.e., low risk.
[0028] Finally, iterate through all target devices. , each The regional risk assessment results serve as To form a regional risk level set ,in, The value of L is 1-4, consistent with the preset area risk level. L is stored in the apron risk assessment database, and a system is established... and Unique identifier The relationships between location information.
[0029] like Figure 2 As shown, S300: Traversal Calculate in sequence Determine the relationship with other devices and aircraft. The density of the surrounding environment was obtained. Corresponding density situation ,in for The environmental density is indicated by a value of 1, 2, or 3, with a higher value representing a higher environmental density. Specifically, S310: Acquires four types of core datasets through multi-interface collaboration, including: Get the latest location data of all devices in For the total number of devices, For the first The latest location of the device. Obtain a list of aircraft operating in the apron area. in For all aircraft operating on the apron, For the first One aircraft, Get the aircraft status list , for The operational status includes driving, parking, pushing out, and waiting. Obtain the latest aircraft location data , for Latest location data; S320: Reads the number of server CPU cores, and creates a thread set based on the number of cores minus 1. in The total number of threads, the size of which is determined by server configuration, is used to establish a synchronized thread lock. Divided into The target device list is divided into t thread subsets, which are then processed sequentially by different threads. The list is sorted lexicographically by the device's unique identifier ID and divided into t thread subsets using an average partitioning plus remainder distribution strategy. When n is divisible by t, each subset contains n / t devices; when n is not divisible by t, the first subset contains n / t devices. A subset contains The remaining subset contains [number] devices. To ensure a balanced workload across all threads, a mutex synchronization mechanism is implemented to prevent data conflicts caused by multiple threads simultaneously reading and writing shared data. This mechanism involves setting up a shared result array W (used to store the density levels of each device). Each thread can only write its own calculation results after acquiring the mutex, and releases the lock after writing. The main thread monitors the status of each child thread in real time, and triggers a result aggregation process after all child threads have completed their calculations, ensuring the safety and accuracy of multi-threaded computations.
[0030] S330: Each thread Iterate through the divided set of target devices to obtain the specific target devices. Get the latest location Since the apron locations are defined by latitude and longitude coordinates on the Earth's surface, the spherical distance between the two points needs to be calculated using the Haversine formula, which is expressed as: ; in, The average radius of the Earth For the target device location , For comparison points.
[0031] For each target device The thread iterates through the entire device location dataset B and calculates sequentially using the Haversine formula. and Distance from the midpoint Two preset distance thresholds are used as the near distance threshold. and long distance threshold In this embodiment, 50 meters and 100 meters are used respectively to establish close-range counting variables. With long-distance counting variables ,when hour, Perform an increment operation when At that time, Perform an increment operation, where and These are user-adjustable parameters, namely the near-distance threshold and the far-distance threshold. S340: Similarly, the thread iterates through the aircraft location dataset O, calculating sequentially using the Haversine formula. and Distance from the midpoint Establish aircraft motion state counting variables Aircraft stationary state count variable ,when At that time, if Corresponding aircraft status For driving or pushing, Increment by 1 if the aircraft is in a stopped or holding state. Perform an increment operation, where These are user-adjustable parameters.
[0032] S350: Based on the above distance counting results, the density accumulation value is calculated using a weighted formula. Combined with a preset threshold, the environmental density level of the target device is determined. To reflect the differences in the impact of different counting items on environmental density, weight parameters for each counting item are set, and a density accumulation calculation formula is established: ; in For user-adjustable vehicle proximity counting weights, User-adjustable vehicle long-distance counting weights For user-adjustable aircraft motion state counting weights, For user-adjustable aircraft stationary state counting weights, This is a user-adjustable interaction impact coefficient.
[0033] S360: Presets two density thresholds: medium density threshold and density threshold A density grade calculation formula is established, and the density grade is determined through a piecewise function. The density grade calculation formula is as follows: ; in A user-adjustable high-density threshold. For a user-adjustable medium density threshold, each thread Solve sequentially using the density formula. The density level corresponding to the target device is synchronized based on a synchronization thread lock. After all threads have completed their calculations, the results are aggregated to obtain... Corresponding target device level ,in For the corresponding Density level.
[0034] Each child thread After completing the density calculation of the subset of devices under its responsibility, it acquires a mutex lock and locks each device. density level Write the results to the shared result array (with each index corresponding to a device ID). After writing, release the lock and send a completion signal to the main thread. Once the main thread receives completion signals from all child threads, it reads the shared array W in the original order of the target device list. tidy Forming an environmental density level set ,in for Environmental density conditions.
[0035] S400: Obtain environmental data such as rain, snow, and fog; acquire device anomaly data; and iterate through... Calculation target device The site change level is determined using an empirical rule-based decision tree algorithm, which integrates factors such as meteorology, site change level, and abnormal device status to derive the environmental risk level of the target device. Corresponding environmental risk situation ,in for The environmental risk level is indicated by a value of 1, 2, 3, or 4, with higher values representing higher environmental risks. S410: Obtain risk-related data, including rainfall level data. Obtain fog level data Obtain snow level data Obtain day and night status data ,in An integer value of 0, 1, 2, or 3 represents no rain, light rain, moderate rain, and heavy rain, respectively. The value can be an integer of 0, 1, 2, or 3; the larger the value, the lower the visibility. An integer value of 0, 1, 2, or 3 represents no snow, light snow, moderate snow, and heavy snow, respectively. An integer value of 1 or 2, representing daytime and nighttime respectively; S420: Iterate through D and obtain... For the corresponding 30-second data set, the distances to the midpoints were calculated sequentially using the Haversine formula and then summed to obtain the length of the data set. ,Establish Point change level The specific calculation method is as follows: ,in, is the length of the point set.
[0036] S430: Retrieve target device from apron equipment condition monitoring system The self-test abnormal signal generates a set of device abnormal states. , Using 0-1 binary encoding, obtain Corresponding device abnormal status data , where is a value of 0 or 1, where 0 represents normal and 1 represents abnormal. A formula for calculating the final environmental risk level based on a decision tree is constructed.
[0037] In S430, the decision tree uses the degree of meteorological risk impact as the core rule, prioritizing the determination of high-impact meteorological factors (such as heavy rain and dense fog), then determining medium-impact factors (such as light rain plus nighttime), and finally outputting meteorological correction values. Weather correction value for: ; in, This indicates rainfall level data. This indicates snow level data. Indicates fog level data, This represents day / night status data, with values of 1 or 2, where 1 corresponds to daytime and 2 corresponds to nighttime. It represents the "OR" relationship in logical operations; The decision tree combines the S300's environmental density level with the device's abnormal status, triggering corrections only when the density environment and device abnormalities occur. The device abnormality correction value... for: , in, Indicates the environmental density level of the target device. This indicates abnormal status data of the device.
[0038] The decision tree uses the contribution of location change level to risk as a rule, and only triggers a correction when the change level reaches medium to high. The location change correction value is... for: ,in, This indicates the total length of the point set.
[0039] Finally, the decision tree uses the environmental density level as the root node and whether each correction value is triggered as the branch nodes, resulting in the final environmental risk level. for: .
[0040] in, For the risk upper limit constraint function, This indicates a weather correction value. Indicates the device's abnormal correction value. This indicates the correction value for changes in location.
[0041] S500: Traverse D, obtain corresponding , , , , Standardization is performed using a weighted model. Priority calculation, ultimately yielding the corresponding priority. ,in For the first The priority value of each target device; First, the basic parameters are retrieved, including the target device reference value set. , , For the first A target device baseline value, where n is the number of target devices; target device type set. , For the first One target device type, A value of 1 or 2 represents low power consumption and continuous power supply, respectively; the target device precision set , For the first The accuracy of the target device The values are 1, 2, or 3, representing low, medium, and high precision, respectively. Additionally, a regional risk level set for S200 is added. ,in, The value ranges from 1 to 4, consistent with the preset regional risk level, and is part of the S400 environmental risk level set. ,in for The environmental risk level is indicated by a value of 1, 2, 3, or 4, with higher values representing higher environmental risk. Complete the parameter categorization and retrieval.
[0042] because The original value range and units are different, and directly participating in the weighted calculation will lead to an imbalance in weights. It is necessary to standardize it and convert it to the [0,1] interval. The specific algorithm and implementation are as follows: S510: Yes Standardization process is performed to obtain The specific calculation method is as follows: ; right Standardization process is performed to obtain The specific calculation method is as follows: ; right Standardization process is performed to obtain The specific calculation method is as follows: ; right Standardization process is performed to obtain The specific calculation method is as follows: ; S520: Set user-adjustable environmental risk weights This reflects the impact of environmental risks on priorities and regional risk weights. Regional risk weights and device type weights This reflects the impact of power supply characteristics on priority and device accuracy weight. This reflects the impact of data accuracy on priority, with benchmark weights. This reflects the impact of user-preset importance on priority. , S530: Employing a multi-parameter linear superposition structure, the standardized parameters are multiplied by their corresponding weights and then summed to establish a comprehensive priority weighting formula. ; in, This represents the standardized value of the environmental risk level. This represents the standardized value of the regional risk level. Indicates the standardized value for device type. This indicates the standardized value of the device's accuracy. The reference value for the i-th target device is represented, and the process is performed sequentially. Priority calculation to obtain the corresponding priority. .
[0043] S600: Acquisition device transmission network set in The number of transmission networks for the target device. Representing the A transmission network, according to , , Calculate the transmission frequency of each target device, issue commands, and update the frequency.
[0044] The S600 includes the following steps: S610: Obtain the target device's transmission network set in The number of transmission networks for the target device. Representing the A transmission network, ,in represent Average data transmission size represent The reference bandwidth available to the target device represent Total bandwidth represent There are two types of bandwidth: static bandwidth and dynamic bandwidth. In a static bandwidth network, the target device uses a static base bandwidth, while in a dynamic bandwidth network, the target device uses additional network bandwidth beyond the base bandwidth, depending on the scale of apron traffic. represent Adjustable weights represent The number of adjustable baseline aircraft. represent The target device mapping relationship is as follows; S620: Traversal Obtain the specific transmission network ,judge The type, when it is static bandwidth type, then Available bandwidth for the target device When it is a dynamic bandwidth type, The calculation method is as follows: ; Where q represents the total number of aircraft operating within the apron area. represent The reference bandwidth available to the target device represent Total bandwidth represent Adjustable weights It is a minimum value function.
[0045] S630: According to In Target device mapping relationship, and simultaneously based on The target device transmits its status, and the data is obtained in the middle. Set of target devices with normal transmission status in for The number of target devices with normal transmission status varies. The following will be different. For the first One target device, according to and Obtain the corresponding target device priority set , for According to the corresponding priority, traverse To obtain the specific target device ,calculate Bandwidth available : ; Transmission frequency (seconds / time) : ; in, for Available bandwidth, Given the sum of priorities for all normally functioning target devices under the a-th transmission network, complete the following: All network calculations are performed to obtain the transmission frequency calculated by different target devices. The transmission frequency is then converted into a change command, which is issued to the target device to change the transmission frequency.
[0046] Example 2 The difference between this embodiment and Embodiment 1 is that this embodiment provides an airport equipment transmission frequency adjustment system based on multi-dimensional risk assessment, including: Target device acquisition unit: Recalculates events based on transmission frequency to obtain a list of target devices. in The number of target devices. For the first A target device to be calculated; obtain the target device reference value set based on D. , For the first Reference values for each target device; obtain the target device type set. , For the first Target device type; Obtain target device accuracy set , For the first Accuracy of the target device; acquisition of the target device's transmission status set. , For the first The target device transmits its status.
[0047] Regional risk unit: used to determine the risk level based on the risk level of the area. Get the most recent list of target device locations Get the list of preset areas For the preset number of areas, Indicates the first A preset area is traversed. The ray method is used to determine the results sequentially. Location Specifically Draw a horizontal line to the left from the starting point, and calculate the intersection of the line segment and the horizontal line. The number of intersections; if the number is odd, then it is considered... exist If the number is even, then it is considered... exist In addition, if If there are multiple regions, the device is assigned the highest risk level across all regions. If it is not in any of the regions, the device is considered to be at a low risk level. The final risk level of the device in each region is then determined. .
[0048] Density calculation unit: used for traversal Calculate in sequence Determine the relationship with other devices and aircraft. The density of the surrounding environment was obtained. Corresponding density situation ,in for Environmental density conditions.
[0049] Environmental risk unit: used to obtain environmental data such as rain, snow, and fog, acquire device anomaly data, and traverse... Calculation target device The site change level is determined using an empirical rule-based decision tree algorithm, which integrates factors such as meteorology, site change level, and abnormal device status to derive the environmental risk level of the target device. Corresponding environmental risk situation ,in for Environmental risk situation.
[0050] Priority unit: used to traverse D and obtain corresponding , , , , Standardization is performed using a weighted model. Priority calculation, ultimately yielding the corresponding priority. ,in For the first The priority value of each target device.
[0051] Update Unit: Acquisition Device Transmission Network Set in The number of transmission networks for the target device. Representing the A transmission network; according to , , Calculate the transmission frequency of each target device, issue commands, and update the frequency.
[0052] The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.
Claims
1. A method for adjusting the transmission frequency of airport equipment based on multi-dimensional risk assessment, characterized in that, include: S1. In response to the transmission frequency recalculation event, obtain the target device list, and obtain the reference value set, type set, accuracy set and transmission status set of the target devices based on the target device list; S2. Based on the target device list, obtain the target device location list and the preset area list, and use the ray method to determine the area where each location in the target device location list is located, and determine the risk level set of the target device area. S3. Traverse the target device list, calculate the positional relationship between each target device and other devices and aircraft, and determine the environmental density of the target devices by combining multi-threaded parallel processing to obtain the density level set. S4. Acquire meteorological data and equipment anomaly data, calculate the target equipment location change level, and use an empirical rule-based decision tree algorithm to integrate meteorological factors, location change level factors, and equipment anomaly state factors to calculate the target equipment environmental risk level. S5. Standardize the environmental risk level set, regional risk level set, type set and accuracy set, and construct a weighted model by setting the weights of each parameter based on the standardization results, and calculate the target device priority set. S6. Obtain the set of device transmission networks, calculate the available bandwidth of each transmission network, and determine the transmission frequency of each target device by combining the target device list, target device priority set and device transmission status, and issue an update command.
2. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, The acquisition of the reference value set, type set, precision set, and transmission status set of the target device specifically includes: S11. Obtain the target device reference value set based on the target device list. , , For the first The target device baseline value is n, where n is the number of target devices; S12. Obtain the target device type set , For the first One target device type, A value of 1 or 2 represents low power consumption and continuous power supply, respectively. S13. Obtain the target device accuracy set , For the first The accuracy of the target device The value is 1, 2, or 3, representing low precision, medium precision, and high precision, respectively. S14. Obtain the target device transmission status set , For the first Transmitting status of each target device The value is 0 or 1, representing normal and abnormal respectively. The target device is a status monitoring device installed on the apron operating elements and is equipped with a GNSS sensor.
3. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, The determination of the risk level set for the target device area includes obtaining the most recent list of target device locations based on the target device list. At the same time, obtain the list of preset areas of the airport apron. Iterate through the target device location list P, for each target device location... The ray method is used to determine the results sequentially. The preset area where it is located, when When the device is located in multiple areas, the highest risk level in each area is taken as the risk reference for that area. When the device is not in any region, the region where the device is located is assumed to be of low risk level, which ultimately forms a set of regional risk levels. in, This refers to the location information most recently recorded by the i-th target device. Let m be the j-th preset region, and m be the total number of preset regions.
4. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 3, characterized in that, The method of judging sequentially using rays The preset area includes the preset area adjusted by the average correction algorithm. The boundary point set is modified with coordinates to adjust the target device location. The latitude and longitude coordinates of the boundary points and the boundary points of the boundary point set are converted into Cartesian coordinates using the Kruger projection transformation algorithm. Draw a horizontal line to the left from the starting point, and calculate the intersection of the line segment and the horizontal line. The number of intersections; when the number of intersections is odd, set... exist Internally, when the number of intersections is even, set exist external.
5. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, The determination of the target device environment density by combining multi-threaded parallel processing includes: S31. Obtain the latest location dataset of all devices, the list of aircraft operating on the apron, the list of aircraft status, and the latest location dataset of aircraft. S32. Build a multi-threaded processing architecture, determine the total number of threads based on the number of server CPU cores and establish a thread set, sort the target device list by ID and divide it into thread subsets, and deploy a mutex lock synchronization mechanism. S33. Each thread traverses the subset of target devices it is responsible for. For each target device, the Haversine formula is used to calculate the spherical distance between the target device and the central point of the latest point data of all devices, and the spherical distance between the target device and the central point of the latest point data of the aircraft. The distance count is completed based on the distance threshold and the aircraft status. S34. Based on the counting results, the cumulative density value is calculated using a weighted formula. The density level of the target device is determined by combining the density threshold. After all threads have completed the calculation, the main thread summarizes the results to obtain the density level set.
6. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, Step S4 specifically includes the following sub-steps: S41. Obtain multi-source risk association data, which includes meteorological data, device anomaly data, and target device location data. S42. Calculate the location change level based on the target device location data. Calculate the distance between adjacent locations of the target device within a preset time period using the Haversine formula to obtain the total length of the location set. Combine this with a location length threshold to classify the location change level. The expression for the change level is: ； in, Indicates the total length of the point set; S43. A decision tree algorithm is constructed to convert meteorological data and location change levels into meteorological correction values and location change correction values, respectively. Finally, the device anomaly data is combined with the density level to convert it into device anomaly correction values. The expression for the meteorological correction value is as follows: ； in, This indicates rainfall level data. This indicates snow level data. Indicates fog level data, This represents day / night status data, with values of 1 or 2, where 1 corresponds to daytime and 2 corresponds to nighttime. It represents the "OR" relationship in logical operations; S44. By integrating density level with meteorological correction value, device anomaly correction value and location change correction value through decision tree algorithm, the environmental risk level of the target device is calculated, and an environmental risk level set is formed after traversing all target devices.
7. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, The calculation of the target device priority set includes setting standardized environmental risk level, regional risk level, device type, device accuracy, and target device baseline value as weight parameters based on airport apron operation safety requirements and monitoring priorities. A weighted calculation model is constructed by multiplying the standardized environmental risk level, regional risk level, device type, and device accuracy by their corresponding weight parameters, and then adding the product of the target device baseline value and its weight to obtain the priority of each individual target device. This process iterates through all target devices in the target device list, obtains the priority value for each device, and sorts and organizes them according to the target device list to form the target device priority set. The formula for calculating the priority of a single target device is as follows: ； in, This represents the standardized value of the environmental risk level of the i-th target device. This represents the standardized value of the regional risk level for the i-th target device. This represents the type-normalized value of the i-th target device. This represents the standardized accuracy value of the i-th target device. This represents the reference value of the i-th target device. , , , and These represent the weight parameters of each input parameter.
8. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, The calculation of the available bandwidth of each transmission network includes obtaining the target device's transmission network set and network parameters, calculating the target device's available bandwidth according to the type of each transmission network, and when the transmission network is of static bandwidth type, directly determining the reference bandwidth as the target device's available bandwidth under that network, and when the transmission network is of dynamic bandwidth type, using a bandwidth calculation formula to calculate the available bandwidth, the bandwidth calculation formula being expressed as follows: ； in, This indicates the total number of aircraft operating within the apron area. This function represents the minimum value and is used to ensure that the calculated available bandwidth does not exceed the total network bandwidth. Indicates the total bandwidth of the transmission network. This represents the reference bandwidth available to the target device within the transmission network. This indicates adjustable weights in the transmission network. This indicates the number of adjustable reference aircraft under the transmission network.
9. The airport equipment transmission frequency adjustment method based on multi-dimensional risk assessment according to claim 1, characterized in that, Determining the transmission frequency of each target device includes filtering out a set of target devices with normal transmission status under the transmission network based on the target device mapping relationship of the transmission network and the target device transmission status data. ,based on Call the target device priority set, obtain the corresponding priority set, and iterate through it. ,right Each target device Calculate its available bandwidth, and calculate the transmission frequency based on the available bandwidth and the average data transmission size. The formula for calculating the available bandwidth is as follows: ； in, This represents the set of target devices in a normal state. This represents a specific target device within the constant set of target devices. This represents the available bandwidth of the target device under the a-th transmission network. This represents the b-th normal target device under the a-th transmission network. This represents the sum of priorities of all normally functioning target devices under the a-th transmission network.
10. An airport facility transmission frequency adjustment system based on multi-dimensional risk assessment, comprising the method described in claim 1, characterized in that, include: The target device acquisition module is configured to: in response to a transmission frequency recalculation event, acquire a list of target devices and acquire a basic parameter set of the target devices based on the list of target devices; The regional risk module is configured to: obtain a list of target device locations and a preset regional list based on the target device list; use the ray casting method to determine the region where each location in the target device location list is located; and determine the set of regional risk levels for the target device. The density calculation module is configured to: traverse the target device list, calculate the positional relationship between each target device and other devices and aircraft, determine the environmental density of the target device by combining multi-threaded parallel processing, and obtain a density level set; The environmental risk module is configured to: acquire meteorological data and device anomaly data, calculate the target device location change level, and use an empirical rule-based decision tree algorithm to integrate multiple factors to obtain the target device environmental risk level set; The priority module is configured to: standardize the environmental risk level set, regional risk level set, type set, and accuracy set, and construct a weighted model based on the standardization results to calculate the priority set of the target device. The update module is configured to: obtain the set of device transmission networks, calculate the available bandwidth of each transmission network, and determine the transmission frequency of each target device by combining the target device list, the target device priority set and the device transmission status, and issue an update command.