Flight data transmission method, system and storage medium based on wireless communication

Abstract

The present application relates to the technical field of data transmission, and discloses a flight data transmission method, system and storage medium based on wireless communication. The method includes: edge computing noise reduction and data fusion processing on multi-source sensor flight data to obtain pre-processed transmission data; distributed cache node sharding and data synchronization processing on pre-processed transmission data to obtain multi-source communication data; bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources on multi-source communication data to obtain priority network data; layered network coding and transmission redundancy processing on priority network data to obtain coded transmission data; cloud-edge collaborative decoding and data recovery processing on coded transmission data to obtain transmission flight data. The present application realizes the reliable transmission of flight data in complex flight environments, and solves the technical problems that the existing single communication mode is susceptible to interference and data transmission is unstable.

Description

Technical Field

[0001] The present application relates to the field of data transmission, and in particular to a method, system and storage medium for in-flight data transmission based on wireless communication. Background Art

[0002] With the rapid development of the civil aviation industry, the demand for flight data recording and transmission is growing. Existing flight data transmission technology mainly relies on a single communication method, such as satellite communication or ground mobile communication network, and adopts a fixed sampling frequency and a unified data processing method during the flight data collection process. These systems usually adopt a centralized data processing architecture to uniformly transmit the collected data to the ground processing center for analysis. In the data transmission process, traditional data compression and encryption methods are mainly used to transmit data through a single channel.

[0003] However, the existing technology has the following shortcomings: in a complex flight environment, a single communication method is easily interfered with, resulting in unstable data transmission; the fixed sampling frequency cannot adapt to the data collection needs of different flight phases; the centralized data processing method is likely to cause communication bandwidth pressure and data transmission interruption when the network conditions are poor; in addition, traditional data compression and encryption methods are inefficient when processing large amounts of real-time flight data, and it is difficult to meet the real-time and reliability requirements of aviation data transmission. Summary of the invention

[0004] The present application provides a flight data transmission method, system and storage medium based on wireless communication, which are used to achieve reliable transmission of flight data in a complex flight environment, and solve the technical problems that the existing single communication mode is susceptible to interference and unstable data transmission.

[0005] In a first aspect, the present application provides a method for transmitting in-flight data based on wireless communication, the method for transmitting in-flight data based on wireless communication comprising:

[0006] Dynamic sampling frequency adjustment and data compensation processing are performed on the flight parameters collected by engine sensors, avionics systems and environmental monitors to obtain multi-source sensor flight data; edge computing noise reduction and data fusion processing are performed on the multi-source sensor flight data to obtain pre-processed transmission data; distributed cache node sharding and data synchronization processing are performed on the pre-processed transmission data to obtain multi-source communication data; bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources are performed on the multi-source communication data to obtain priority network data; layered network coding and transmission redundancy processing are performed on the priority network data to obtain coded transmission data; cloud-edge collaborative decoding and data recovery processing are performed on the coded transmission data to obtain transmission flight data.

[0007] In a second aspect, the present application provides a flight data transmission system based on wireless communication, and the flight data transmission system based on wireless communication includes:

[0008] The acquisition module is used to dynamically adjust the sampling frequency and perform data compensation processing on the flight parameters collected by the engine sensor, avionics system and environmental monitor to obtain multi-source sensing flight data;

[0009] A fusion module, used for performing edge computing noise reduction and data fusion processing on the multi-source sensor flight data to obtain pre-processed transmission data;

[0010] A synchronization module, used for performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data;

[0011] An allocation module is used to perform bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources on the multi-source communication data to obtain priority network data;

[0012] A redundancy module, used for performing hierarchical network coding and transmission redundancy processing on the priority network data to obtain coded transmission data;

[0013] The recovery module is used to perform cloud-edge collaborative decoding and data recovery processing on the encoded transmission data to obtain transmission flight data.

[0014] A third aspect of the present application provides a computer-readable storage medium, wherein instructions are stored in the computer-readable storage medium, and when the computer-readable storage medium is executed on a computer, the computer executes the above-mentioned flight data transmission method based on wireless communication.

[0015] In the technical solution provided by the present application, by dynamically adjusting the sampling frequency and performing data compensation processing on the flight parameters collected by the engine sensors, avionics systems and environmental monitors, the sampling frequency can be adaptively adjusted according to the importance of different flight stages and data types, effectively avoiding the data redundancy or information loss problems caused by the traditional fixed sampling method, while ensuring the continuity and integrity of the data through data compensation processing; by performing edge computing noise reduction and data fusion processing on multi-source sensor flight data, effective noise filtering and intelligent fusion of multi-source data are achieved at the data source, significantly improving data quality and processing efficiency, and reducing the burden of subsequent transmission and storage; the distributed cache node sharding and data synchronization processing mechanism are adopted to realize the distributed storage and synchronous update of data, which effectively solves the problem of The single point failure and performance bottleneck problems faced by traditional centralized storage are solved, and the reliability and scalability of the system are improved; by bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources for multi-source communication data, dynamic switching and resource optimization configuration of multiple communication modes are realized, overcoming the defect that a single communication mode is susceptible to interference, and significantly improving the stability and reliability of data transmission; hierarchical network coding and transmission redundancy processing are performed on priority network data. Through hierarchical coding strategies and intelligent redundancy mechanisms, the anti-interference and fault tolerance capabilities of data transmission are improved, ensuring the priority transmission and reliable arrival of important data; finally, through cloud-edge collaborative decoding and data recovery processing mechanisms, efficient decoding and data recovery in a distributed environment are realized, effectively improving the real-time and availability of the system. Through multi-level technical innovations, this solution builds a complete flight data collection, processing, transmission and recovery system, which not only solves the problems of inaccurate data collection, unstable transmission, and low processing efficiency in traditional solutions, but also realizes efficient, reliable and real-time transmission of flight data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a method for transmitting in-flight data based on wireless communication in an embodiment of the present application;

[0018] Figure 2 It is a schematic diagram of an embodiment of a flight data transmission system based on wireless communication in an embodiment of the present application. DETAILED DESCRIPTION

[0019] Embodiments of the present application provide a method, system and storage medium for transmitting in-flight data based on wireless communication. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for transmitting flight data based on wireless communication in the embodiment of the present application includes:

[0021] Step S101, dynamically adjusting the sampling frequency and performing data compensation processing on the flight parameters collected by the engine sensor, the avionics system and the environmental monitor to obtain multi-source sensing flight data;

[0022] Step S102, performing edge computing noise reduction and data fusion processing on multi-source sensor flight data to obtain pre-processed transmission data;

[0023] Step S103, performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data;

[0024] Step S104: Perform bidding and bandwidth allocation processing on WiFi, 4G / 5G and satellite communication resources on multi-source communication data to obtain priority network data;

[0025] Step S105, performing hierarchical network coding and transmission redundancy processing on the priority network data to obtain coded transmission data;

[0026] Step S106: Perform cloud-edge collaborative decoding and data recovery processing on the encoded transmission data to obtain transmission flight data.

[0027] It is understandable that the execution subject of the present application may be a flight data transmission system based on wireless communication, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0028] Specifically, the flight parameter data collected by the engine sensor, avionics system and environmental monitor are processed. During the flight, the engine sensor collects key parameters such as engine temperature, pressure, and speed; the avionics system collects flight status parameters such as aircraft attitude, heading, and speed; and the environmental monitor collects environmental parameters such as air pressure, temperature, and humidity. According to the importance of data in different flight stages, the sampling frequency is dynamically adjusted. For example, in key flight stages such as takeoff and landing, the sampling frequency of engine parameters is increased from the standard 10Hz to 50Hz to capture more key data. When abnormal fluctuations in engine parameters are detected, the system will start the data compensation mechanism and supplement the missing data through linear interpolation to ensure data continuity. When edge computing denoising and data fusion processing are performed on the collected multi-source sensor flight data, the edge computing node first performs denoising on the data. Specifically, for the periodic noise generated by engine vibration, a notch filter is used for processing; for random environmental noise, a Kalman filter is used for smoothing. In the data fusion stage, the GPS position data is fused with the inertial navigation data, and the position accuracy is improved through complementary filtering. For example, when the GPS signal is temporarily lost, inertial navigation data is used to supplement it and maintain the continuity of position data.

[0029] In the distributed cache node sharding and data synchronization processing stage, the preprocessed data is sharded and stored according to the time window. The size of each shard is 1MB, and it is allocated to different cache nodes according to the data type. The consistent hashing algorithm is used to ensure that the data is evenly distributed among the nodes, and each data shard saves multiple copies on different nodes. When a node detects a change in important data, the data synchronization mechanism between nodes is triggered to ensure data consistency. When bidding for communication resources and allocating bandwidth, the signal quality and available bandwidth of WiFi, 4G / 5G and satellite communications are first evaluated. Each communication mode obtains a corresponding bidding weight according to the current service quality index. For example, WiFi links with a signal strength of more than -75dBm obtain a higher weight, while 4G links with a signal strength of less than -95dBm have a lower bidding weight. Through the bandwidth reservation mechanism, 30% of the bandwidth resources are reserved for critical flight data to ensure the transmission priority of important data. In the hierarchical network coding and transmission redundancy processing stage, priority network data is divided into different levels of importance. Strong coding protection is used for critical flight data, and the redundancy is set to 1.5; lightweight coding is used for ordinary data, and the redundancy is set to 1.2. Through the network coding algorithm, the data to be transmitted is encoded into several coding blocks, each of which contains information from different data sources. The coding parameters are dynamically adjusted according to the network conditions, and the redundancy of the coding is increased when the network quality is detected to be degraded.

[0030] Finally, in the cloud-edge collaborative decoding and data recovery phase, the edge node first performs an integrity check on the received encoded data. For complete data blocks, it directly decodes them, and for incomplete data blocks, it requests the cloud to assist in recovery. The cloud repairs the data through historical data and performs a quality assessment on the recovered data. For example, during a certain flight, the engine temperature sensor data had a 3-second transmission interruption. The edge node repaired the data during the interruption by analyzing the data patterns of the previous and subsequent time periods and combining the engine operating characteristics. Through data normalization processing, the repaired data is adjusted to a reasonable range, and finally the complete transmission flight data is obtained.

[0031] For example, when the engine temperature changes suddenly, the sampling frequency automatically increases from 10Hz to 50Hz, and the amount of raw data collected within 5 minutes reaches 15MB. After edge computing noise reduction processing, 90% of the noise data is removed. In the distributed storage stage, the data is divided into 12 1MB shards and distributed on 6 cache nodes. When the 4G network signal strength is -85dBm and the WiFi signal strength is -70dBm, the system allocates 75% of the bandwidth to the WiFi link. When network coding the temperature data, a redundancy of 1.5 is set to generate 18 coding blocks. Finally, through cloud-edge collaborative decoding, the two data blocks lost due to network fluctuations were successfully restored, ensuring the integrity of the flight data.

[0032] In the embodiment of the present application, by dynamically adjusting the sampling frequency and performing data compensation processing on the flight parameters collected by the engine sensors, avionics systems and environmental monitors, the sampling frequency can be adaptively adjusted according to the importance of different flight stages and data types, effectively avoiding data redundancy or information loss problems caused by traditional fixed sampling methods, and ensuring the continuity and integrity of the data through data compensation processing; by performing edge computing noise reduction and data fusion processing on multi-source sensor flight data, effective noise filtering and intelligent fusion of multi-source data are achieved at the data source, significantly improving data quality and processing efficiency, and reducing the burden of subsequent transmission and storage; the distributed cache node sharding and data synchronization processing mechanism are adopted to realize distributed storage and synchronous update of data, effectively solving the traditional The single point failure and performance bottleneck problems faced by centralized storage are solved, and the reliability and scalability of the system are improved; by bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources for multi-source communication data, dynamic switching and resource optimization configuration of multiple communication modes are realized, overcoming the defect that a single communication mode is susceptible to interference, and significantly improving the stability and reliability of data transmission; hierarchical network coding and transmission redundancy processing are performed on priority network data. Through hierarchical coding strategies and intelligent redundancy mechanisms, the anti-interference ability and fault tolerance of data transmission are improved, ensuring the priority transmission and reliable arrival of important data; finally, through cloud-edge collaborative decoding and data recovery processing mechanisms, efficient decoding and data recovery in a distributed environment are realized, effectively improving the real-time and availability of the system. Through multi-level technical innovations, this solution builds a complete flight data collection, processing, transmission and recovery system, which not only solves the problems of inaccurate data collection, unstable transmission, and low processing efficiency in traditional solutions, but also realizes efficient, reliable and real-time transmission of flight data.

[0033] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0034] (1) Perform frequency analysis on the temperature, pressure, and speed parameters collected by the engine sensor to obtain the engine sampling threshold, and perform data classification on the engine sampling threshold to obtain the engine dynamic sampling rate;

[0035] (2) Perform time series analysis on the attitude, heading, and speed parameters collected by the avionics system to obtain the avionics sampling threshold, and divide the avionics sampling threshold into stages to obtain the avionics dynamic sampling rate;

[0036] (3) Perform fluctuation detection on the air pressure, temperature, and humidity parameters collected by the environmental monitor to obtain the environmental sampling threshold, and perform interval statistical processing on the environmental sampling threshold to obtain the environmental dynamic sampling rate;

[0037] (4) Perform noise filtering on the engine dynamic sampling rate, avionics dynamic sampling rate, and environmental dynamic sampling rate through the Kalman filter algorithm to obtain a sampling rate sequence, and perform timestamp processing on the sampling rate sequence to obtain a calibration sampling sequence;

[0038] (5) Performing data compensation window division processing on the calibration sampling sequence to obtain a compensation time window, and performing linear interpolation processing on the missing data within the compensation time window to obtain a compensation data sequence;

[0039] (6) The compensated data sequence is fused by a weighted average algorithm to obtain a fused data set, and the fused data set is verified for consistency to obtain multi-source sensing flight data.

[0040] Specifically, the three types of parameters collected by the engine sensor, namely temperature, pressure and speed, are subjected to frequency analysis. The frequency analysis includes the calculation of the parameter change rate and the analysis of the change trend. When the temperature parameter change rate exceeds 50°C / minute, the pressure parameter change rate exceeds 2MPa / minute or the speed parameter change rate exceeds 1000rpm / minute, the corresponding parameter is marked as a rapid change state to obtain the engine sampling threshold. These sampling thresholds are classified, and the parameter sampling rate of the rapid change state is set to 50Hz, and the parameter sampling rate of the stable state is set to 10Hz, so as to obtain the engine dynamic sampling rate. When the attitude, heading and speed parameters collected by the avionics system are subjected to time series analysis, the change amplitude of the parameters in different time windows is calculated. When the attitude angle changes by more than 5 degrees / second, the heading changes by more than 3 degrees / second or the speed changes by more than 10 meters / second, it is marked as a drastic change interval to obtain the avionics sampling threshold. Subsequently, the flight process is divided into take-off, climb, cruise, descent, landing and other stages. The sampling rate is set to 40Hz in the take-off and landing stages and 20Hz in the cruise stage to form the avionics dynamic sampling rate.

[0041] When the air pressure, temperature, and humidity parameters collected by the environmental monitor are subjected to fluctuation detection processing, the fluctuation range and frequency of the parameters are analyzed. When the air pressure change rate exceeds 2kPa / minute, the temperature change rate exceeds 2℃ / minute, or the humidity change rate exceeds 5% / minute, it is determined to be an abnormal fluctuation, and the environmental sampling threshold is obtained. By performing interval statistics on these thresholds, the sampling rate of the abnormal fluctuation interval is set to 30Hz, and the normal interval is set to 15Hz, and the environmental dynamic sampling rate is obtained. The Kalman filter algorithm is used to perform noise filtering on the above three types of dynamic sampling rates. The Kalman filter filters the random noise in the sampling rate sequence through two steps: prediction and update. The prediction step predicts the sampling rate at the current moment based on the state at the previous moment, and the update step corrects the predicted value based on the actual measured value. A unified timestamp is added to the processed sampling rate sequence, and the time accuracy is at the millisecond level to form a calibration sampling sequence.

[0042] The calibration sampling sequence is divided into windows according to fixed time intervals (such as 1 second) to obtain compensation time windows. In each time window, possible data missing points are detected. When data missing is found, linear interpolation method is used for compensation. Linear interpolation calculates the interpolation slope through the valid data points before and after the missing point, calculates the value of the missing point according to the time difference, and generates a compensated data sequence. Finally, the weighted average algorithm is used to fuse the compensated data sequence. The algorithm assigns weights according to the importance of different parameters. The weight of engine key parameters is 0.5, the weight of avionics system parameters is 0.3, and the weight of environmental parameters is 0.2. The consistency of the fused data is verified to check the correlation and physical constraint relationship between the parameters. For example, the engine speed and temperature should be positively correlated, and they should be marked when there is a significant deviation. The verified data forms the final multi-source sensing flight data.

[0043] For example, during the takeoff phase of a passenger plane, when the engine speed quickly increases from idle to takeoff power, the speed parameter change rate reaches 1500rpm / minute, triggering the rapid change state mark, and the sampling rate is automatically increased to 50Hz. At the same time, the aircraft attitude angle changes by 6 degrees / second, and the avionics system sampling rate is adjusted to 40Hz accordingly. At this time, the measured external temperature change rate is 1.5℃ / minute, which is within the normal range, and the environmental parameters maintain a sampling rate of 15Hz. After Kalman filtering, 95% of the random fluctuations in the sampling rate sequence are removed. In a 1-second compensation time window, it is found that 2 data points are missing, and the values ​​of the missing points are calculated by linear interpolation. Finally, the data of the three systems are fused to form a complete sensor data stream through the weighted average algorithm, and the reliability of the data is confirmed through consistency verification.

[0044] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0045] (1) Perform data grouping processing on multi-source sensor flight data to obtain sensor data groups, and perform edge node distribution processing on the sensor data groups to obtain edge computing sequences;

[0046] (2) Perform notch filtering on the periodic noise in the edge computing sequence to obtain primary filtered data, and perform random noise elimination on the primary filtered data to obtain a denoised data group;

[0047] (3) Perform GPS positioning data extraction on the noise reduction data group to obtain a position data set, and perform inertial navigation data association processing on the position data set to obtain fused position data;

[0048] (4) Performing time synchronization processing on the fused position data to obtain a synchronized data stream, and performing feature extraction processing on the synchronized data stream through a wavelet transform algorithm to obtain a feature data set;

[0049] (5) Perform data type labeling on the feature data set to obtain a labeled data stream, and perform cross-validation on the labeled data stream to obtain candidate transmission data;

[0050] (6) Perform edge node consistency processing on the candidate transmission data to obtain pre-processed transmission data.

[0051] Specifically, the multi-source sensor flight data is grouped according to data type and acquisition time. The data types include engine parameter group (temperature, pressure, speed), avionics parameter group (attitude, heading, speed) and environmental parameter group (air pressure, temperature, humidity). Each group of data is divided by a 500ms time window to form a sensor data group. These data groups are then distributed to edge computing nodes distributed at different locations on the aircraft. Each node is responsible for processing data in the adjacent area to generate an edge computing sequence. For the periodic noise in the edge computing sequence, a notch filter is used to process it. The center frequency of the notch filter is set to an integer multiple of the engine operating frequency. For example, when the engine main frequency is 50Hz, the center frequency of the notch filter is set to 50Hz, 100Hz, and 150Hz, and the periodic interference near these frequencies is filtered out to obtain the primary filtered data. For the random noise in the primary filtered data, Gaussian filtering is used to eliminate it, and the filter window size is set to 5 data points to generate a noise reduction data group.

[0052] Extract GPS positioning data from the noise reduction data set, including longitude, latitude, altitude and other information. The sampling rate of GPS data is 1Hz, and the accuracy is within 10 meters. At the same time, extract inertial navigation data from the avionics system, including acceleration, angular velocity and other parameters, with a sampling rate of 100Hz. Through data association processing, the GPS position data and inertial navigation data are complemented. GPS data provides an absolute position reference, and inertial navigation data provides high-frequency relative position changes to form fused position data. Perform time synchronization processing on the fused position data to unify data from different sources to the same time base. Use the timestamp alignment method, with GPS time as the base, to interpolate and align other data to generate a synchronous data stream. Use the wavelet transform algorithm to extract features from the synchronous data stream, select the db4 wavelet basis function, perform a three-layer decomposition, extract the mutation features, trend features and periodic features in the data, and form a feature data set.

[0053] The data in the feature data set is type-labeled, including data source, data type, timestamp, and reliability level. For example, the engine temperature data is labeled "ENG_TEMP_001", and the reliability level is evaluated based on the noise level and integrity of the data, and is divided into three levels: A / B / C. The labeled data stream is cross-validated to check the logical relationship between different types of data, such as the correspondence between engine power and fuel flow, and generate candidate transmission data. Finally, the candidate transmission data is verified for consistency between different edge nodes. Through the voting mechanism, when more than 2 / 3 of the nodes confirm that the data is valid, the data is marked as reliable data. All verified data constitute the preprocessed transmission data.

[0054] For example, during a certain flight, the raw data collected by the engine temperature sensor contained a 50Hz periodic interference with an amplitude of ±2°C. Through notch filtering, the amplitude of this periodic interference was reduced to ±0.2°C. At the same time, there was random noise with a standard deviation of 1°C in the data. After Gaussian filtering, the random noise was reduced to 0.2°C. During the position data fusion process, when the aircraft turned quickly, the update delay of the GPS position data reached 1 second. At this time, it was supplemented by inertial navigation data, and the calculated position error was controlled within 5 meters. After wavelet transform feature extraction, the abnormal event of the engine temperature rising by 10°C in 5 seconds was successfully captured. These data were verified for consistency by three edge nodes, and finally formed reliable pre-processed transmission data.

[0055] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0056] (1) Perform time window division processing on the pre-processed transmission data to obtain time series data segments, and classify the time series data segments according to data types to obtain classified data segments;

[0057] (2) Perform cache node distribution processing on the classified data slices to obtain a node distribution map, and perform data shard mapping processing on the node distribution map to obtain a shard mapping table;

[0058] (3) Perform node load calculation on the shard mapping table to obtain a load data set, and perform shard allocation on the load data set to obtain a shard storage solution;

[0059] (4) Perform consistency protocol processing on the data in the shard storage solution to obtain a synchronization rule set, and perform inter-node communication processing on the synchronization rule set to obtain a communication data stream;

[0060] (5) Performing data synchronization processing on the communication data stream through a consistent hashing algorithm to obtain a synchronized data set, and performing copy creation processing on the synchronized data set to obtain a backup data set;

[0061] (6) Perform distributed node integration processing on the backup data group to obtain multi-source communication data.

[0062] Specifically, the pre-processed transmission data is divided into windows according to fixed time intervals. The time window size is set to 1 second. Each window contains engine data (50Hz sampling), avionics data (40Hz sampling) and environmental data (30Hz sampling). The data in these time windows are formed into time series data fragments, each of which is about 500KB in size. The time series data fragments are classified according to data sources and characteristics, and are divided into key flight data (engine temperature, pressure, etc.), status monitoring data (attitude, heading, etc.) and environmental parameter data (air pressure, temperature, etc.), and classified data slices are generated. The cache node distribution planning is carried out for the classified data slices, and 6 cache nodes are deployed on the aircraft, located at the nose, tail, left and right wings and both sides of the middle of the fuselage. Each node is equipped with 4GB cache space and an independent processor. By analyzing the physical location and network connection status of each node, a node distribution map is drawn, and the network delay and bandwidth information between nodes are recorded. Then, data shard mapping is performed to map different types of data slices to appropriate cache nodes to generate a shard mapping table. Key flight data is preferentially mapped to the node in the middle of the fuselage to ensure low latency of data access.

[0063] The node load calculation adopts a comprehensive evaluation method, and the calculation formula is:

[0064]

[0065] in: represents the load index of the i-th node, Indicates the amount of data currently stored in the node. Represents the storage capacity of the node, Indicates the processor usage of the node. Indicates the maximum processing capability of the processor. Indicates the bandwidth occupancy of the node, Indicates the maximum bandwidth, , , are weight coefficients, which are 0.4, 0.3, and 0.3 respectively.

[0066] According to the calculated load data set, a shard storage plan is formulated. The load balancing principle is adopted. When the load index of a node exceeds 0.8, the new data shard is allocated to the node with lower load. For critical data, multiple copies are saved on different nodes to improve data reliability. Data synchronization adopts a two-phase commit protocol. First, the master node sends a synchronization preparation request to other nodes, including the data version number and checksum. When more than 2 / 3 of the nodes respond that they are ready, the master node sends a commit instruction to start the formal synchronization process. Data is transmitted between nodes through the TCP protocol to ensure the reliability of data transmission and generate communication data streams.

[0067] The consistent hashing algorithm is used to ensure the stability of data when nodes are added or deleted. The hash ring is divided into 2^32 points, and each node is responsible for a section of the ring. The data is mapped to a point on the ring through a hash function and stored by the node responsible for that section. When a node changes, only the section that the changed node is responsible for needs to be reallocated, and other data remains unchanged. The synchronized data is copied between different nodes, with 3 copies of each critical data and 2 copies of non-critical data to form a backup data group. Finally, the backup data group is integrated, and each node exchanges data status information regularly (every 5 seconds) to ensure data consistency. The data of all nodes is integrated into a unified data view to form multi-source communication data.

[0068] For example, during the cruise of a passenger plane, the data generated in a 1-second time window include: 50 engine parameter sampling points (each sample contains temperature, pressure, and speed data, a total of 150 values), 40 avionics system sampling points (each sample contains attitude, heading, and speed data, a total of 120 values), and 30 environmental parameter sampling points (each sample contains air pressure, temperature, and humidity data, a total of 90 values). These data are classified and distributed to 6 cache nodes. Through load calculation, the storage utilization rate of the right node in the middle of the fuselage is 75% (3GB / 4GB), the processor utilization rate is 60%, and the bandwidth occupancy rate is 50%. The load index calculated by the formula is 0.63. Since the load index does not exceed the threshold, new data shards continue to be allocated to this node. During the data synchronization process, the consistent hashing algorithm is used to assign the node to the interval [0, 2^30] on the hash ring. When the hash value of new data falls in this interval, the node is responsible for storage. Finally, a copy of the data is saved on 3 nodes, and data consistency is ensured through regular state exchange.

[0069] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0070] (1) Perform communication quality detection on multi-source communication data to obtain WiFi signal strength, 4G / 5G signal strength, and satellite signal strength data, and perform communication resource evaluation on the signal strength data to obtain a resource status table;

[0071] (2) Calculate the bandwidth capacity of the resource status table to obtain available bandwidth data, and perform delay measurement on the available bandwidth data to obtain a communication delay table;

[0072] (3) Calculate the resource bidding weights on the communication delay table to obtain an initial bidding table, and calculate the communication cost of the initial bidding table to obtain a bidding plan;

[0073] (4) Perform communication resource reservation processing on the bidding scheme to obtain a bandwidth reservation table, and perform emergency channel allocation processing on the bandwidth reservation table to obtain an emergency resource pool;

[0074] (5) Perform multi-channel combination processing on the emergency resource pool to obtain a channel combination plan, and perform redundant path planning processing on the channel combination plan to obtain a backup channel table;

[0075] (6) Perform resource scheduling processing on the standby channel table to obtain a scheduling sequence, and perform bandwidth allocation processing on the scheduling sequence to obtain a bandwidth allocation table;

[0076] (7) Performing communication quality assurance processing on the bandwidth allocation table to obtain service quality parameters, and dynamically adjusting resources on the service quality parameters to obtain an adjustment plan;

[0077] (8) Prioritize the adjustment plan through the resource allocation algorithm to obtain the data transmission queue, and then perform resource binding on the data transmission queue to obtain priority network data.

[0078] Specifically, in the process of communication resource bidding and bandwidth allocation, it is first necessary to detect the communication quality of multi-source communication data. The WiFi signal strength (in dBm) is measured by the received signal strength indicator to obtain the reference signal received power (RSRP) of the 4G / 5G base station and the carrier-to-noise ratio (C / N) of the satellite communication system. Through regular scanning, the signal quality parameters of each communication link are collected every 100 milliseconds and recorded in the signal strength data table. The signal strength data is evaluated for communication resources, and the WiFi signal strength threshold is set to -75dBm, the RSRP threshold of 4G / 5G is set to -95dBm, and the satellite communication C / N threshold is set to 8dB to generate a resource status table. The bandwidth capacity calculation adopts a comprehensive evaluation method, and the calculation formula is:

[0079]

[0080] in: Represents the total available bandwidth, represents the theoretical bandwidth of the ith communication link, represents the link utilization coefficient, Indicates signal strength. Indicates the baseline signal strength, represents the link quality factor, and n represents the total number of communication links.

[0081] The delay of each communication link is measured, and the ICMP protocol is used to send a detection packet, record the round-trip delay (RTT), and generate a communication delay table. The data in the communication delay table is weighted, and the bidding weight is calculated for each link. The weight value is inversely proportional to the signal quality and delay. Links with high quality and low latency obtain higher bidding weights, and the initial bidding table is generated. Considering the communication cost factor, the cost coefficient is set for different links. The cost coefficient of the WiFi link is 1, the 4G / 5G link is 2, and the satellite communication link is 5. The final bidding plan is calculated. According to the bidding plan, communication resources are reserved, and 30% of the bandwidth resources are reserved for critical flight data. A bandwidth reservation table is generated. On the basis of the bandwidth reservation table, an emergency communication channel is set, and 10% of the bandwidth resources are reserved for data transmission in emergency situations to form an emergency resource pool. The bandwidth in the emergency resource pool can be used by ordinary data under normal circumstances, but it is immediately released to emergency data when emergency data is detected.

[0082] The emergency resource pool is combined with multiple channels to combine different communication links into transmission channels. Channel aggregation technology is used to combine WiFi and 4G links into high-speed transmission channels, and 4G and satellite links into reliable transmission channels to generate a channel combination plan. Redundant path planning is performed on the channel combination plan, and the main path and backup path are designed for each data transmission task to generate a backup channel table. The resources in the backup channel table are scheduled, and the priority queue scheduling algorithm is used to sort the data transmission tasks by priority to generate a scheduling sequence. Bandwidth resources are allocated to each data stream according to the scheduling sequence, and high-priority data obtains more bandwidth shares to generate a bandwidth allocation table. The bandwidth allocation table is processed for service quality assurance, and the actual transmission performance indicators are monitored, including bandwidth utilization, packet loss rate, delay jitter, etc., to obtain service quality parameters. The resource allocation strategy is dynamically adjusted according to the service quality parameters to generate an adjustment plan.

[0083] Finally, the resource allocation algorithm is used to prioritize the adjustment plan, and the data is divided into four levels: emergency, high priority, medium priority, and low priority, to generate a data transmission queue. The data in the data transmission queue is bound to the communication resources, and the transmission path and resource configuration of each data packet are determined, and finally the priority network data is obtained.

[0084] For example, during the cruising phase of a passenger aircraft, the WiFi signal strength is -65dBm, the 4G signal RSRP is -85dBm, and the satellite communication C / N is 12dB. The theoretical bandwidth of the WiFi link is 54Mbps, the measured link utilization is 60%, and the delay is 15ms; the theoretical bandwidth of the 4G link is 20Mbps, the utilization is 70%, and the delay is 50ms; the satellite link bandwidth is 2Mbps, the utilization is 90%, and the delay is 500ms. According to the bandwidth capacity calculation formula, combined with the signal strength and quality factor of each link, the total available bandwidth is calculated to be 45Mbps. Among them, 13.5Mbps (30%) bandwidth is reserved for critical flight data, and 4.5Mbps (10%) bandwidth is reserved for emergency channels. Through channel aggregation, WiFi and 4G form a high-speed transmission channel with a total bandwidth of 50Mbps, and 4G and satellite form a reliable transmission channel with a total bandwidth of 22Mbps. In actual operation, engine parameter data is transmitted at the emergency level first, generating 500KB of data per second, which is transmitted through the main path of the high-speed transmission channel, while maintaining backup transmission on the reliable transmission channel.

[0085] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0086] (1) Priority network data is graded according to their importance to obtain a graded data set, and the graded data set is divided into coding blocks to obtain coded data blocks;

[0087] (2) Performing strong coding protection processing on the coded data block to obtain a protected data group, and performing lightweight coding mapping processing on the protected data group to obtain a mapped data set;

[0088] (3) Performing hierarchical coding processing on the mapping data set through a network coding algorithm to obtain a layered coding stream, and performing redundancy calculation processing on the layered coding stream to obtain a redundant parameter table;

[0089] (4) performing transmission status detection processing on the redundant parameter table to obtain a network status set, and performing redundancy adjustment processing on the network status set to obtain an adjustment parameter group;

[0090] (5) Performing network feedback processing on the adjustment parameter group to obtain a feedback data stream, and performing redundancy strategy adjustment processing on the feedback data stream to obtain a redundancy solution;

[0091] (6) Performing encoding parameter setting processing on the redundancy scheme to obtain an encoding strategy set, and performing transmission encoding processing on the encoding strategy set to obtain encoded transmission data.

[0092] Specifically, the priority network data is classified according to the importance. The classification standard includes three dimensions: data type, timeliness and security level. The data type is divided into engine parameters, avionics data and environmental data; timeliness is divided into real-time (needs to be transmitted immediately), quasi-real-time (allows 1 second delay) and non-real-time (allows 10 seconds delay); security level is divided into critical level, important level and ordinary level. Through comprehensive evaluation, the data is divided into three levels: A, B and C to form a hierarchical data set. Class A data includes engine key parameters and real-time avionics data, Class B data includes engine status data and quasi-real-time avionics data, and Class C data includes environmental parameters and non-real-time data. The hierarchical data set is divided into coding blocks. The size of Class A data blocks is set to 256KB, the size of Class B data blocks is 512KB, and the size of Class C data blocks is 1MB to generate coded data blocks. The coded data blocks are strongly coded and protected using the Reed-Solomon coding method. For Class A data, (128,64) encoding is used, that is, 64 original data symbols are encoded into 128 symbols, which has a strong error correction capability; for Class B data, (96,64) encoding is used; for Class C data, (80,64) encoding is used. After encoding, the protected data group is obtained. The protected data group is mapped using lightweight Fountain encoding, converting the data block into a series of encoded symbols, each symbol size is 1KB, and generating a mapping data set.

[0093] The mapping data set is hierarchically processed through the network coding algorithm, and a hierarchical network coding scheme is adopted. Class A data is encoded as the base layer, class B data is encoded as enhancement layer 1, and class C data is encoded as enhancement layer 2 to form a hierarchical coding stream. The redundancy of the hierarchical coding stream is calculated, and the redundancy coefficient of class A data is set to 1.5 (that is, 50% redundant data is generated), the redundancy coefficient of class B data is 1.3, and the redundancy coefficient of class C data is 1.1, and a redundant parameter table is generated. The transmission status of the redundant parameter table is detected, and the detection content includes network packet loss rate, link bandwidth, and transmission delay. The status sampling is performed every 500ms, and various network performance indicators are recorded to generate a network status set. The redundant parameters are adjusted according to the network status set. When the network condition is good (packet loss rate <1%), the redundancy is reduced, and when the network condition deteriorates (packet loss rate >5%), the redundancy is increased, and an adjustment parameter group is generated.

[0094] Perform network feedback analysis on the adjustment parameter group, collect the data recovery status of the receiving end, including the number of lost symbols, decoding success rate and other information, and generate feedback data stream. Adjust the redundancy strategy according to the feedback data stream, increase redundancy when the decoding success rate is lower than 95%, and reduce redundancy when the decoding success rate is higher than 99%, and form a redundancy scheme. Set specific encoding parameters for the redundancy scheme, including encoding symbol size, encoding rate and redundancy ratio, and generate an encoding strategy set. Perform final transmission encoding processing on the data according to the encoding strategy set to generate encoded transmission data.

[0095] For example, during the flight of a certain passenger plane, key parameters such as engine temperature and pressure generate 100KB of raw data, which is processed as Class A data. These data are divided into 25 4KB data blocks, and each data block generates 8KB of encoded data through Reed-Solomon (128, 64) encoding. Then, these data blocks are mapped into encoding symbols through Fountain encoding, each symbol is 1KB, and the redundancy is set to 1.5, generating a total of 150 encoding symbols. During the transmission process, the network status detection shows that the current packet loss rate is 3%, and the redundancy of 1.5 is kept unchanged based on this state. The feedback from the receiving end shows that the decoding success rate is 98%, which meets the expected requirements, and the current encoding strategy continues to be implemented. Finally, all encoding symbols are sent according to the set transmission rules to ensure the reliable transmission of key engine parameters. Throughout the process, through hierarchical encoding and dynamic adjustment of redundancy, the reliability of data transmission is ensured, and the bandwidth waste caused by excessive redundancy is avoided.

[0096] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0097] (1) The edge node receives and processes the encoded transmission data to obtain a received data stream, and performs a data integrity check on the received data stream to obtain an integrity report;

[0098] (2) Perform edge decoding on the complete data block in the integrity report to obtain edge decoded data, and perform cloud collaborative verification on the edge decoded data to obtain a verification data set;

[0099] (3) Perform cloud-based repair request processing on the incomplete data in the verification data set to obtain a repair task table, and perform historical data matching processing on the repair task table to obtain a matching data group;

[0100] (4) Performing data recovery rule analysis on the matching data group to obtain a recovery rule set, and completing the incomplete data on the recovery rule set to obtain a completed data stream;

[0101] (5) Performing data quality assessment on the completed data stream through a collaborative decoding algorithm to obtain a quality assessment table, and performing data normalization on the quality assessment table to obtain a standard data set;

[0102] (6) Perform cloud-edge data integration processing on the standard data set to obtain transmission flight data.

[0103] Specifically, the edge node receives the encoded transmission data scattered on multiple transmission channels. The edge node detects the arrival time and sequence number of each data packet, and reorganizes the received data into a continuous data stream to form a received data stream. The received data stream is checked and checked, the sequence integrity is verified, and the timestamp is analyzed to generate an integrity report, which records the loss, disorder, and delay of the data packet. The data blocks marked as complete in the integrity report are immediately decoded at the edge node. The decoding process first performs Reed-Solomon decoding to restore the original data symbols, and then reconstructs the original data packet through Fountain decoding to generate edge decoded data. The edge decoded data is uploaded to the cloud for collaborative verification, and the cloud checks the logical relationship, physical constraints, and timing consistency of the data. For example, check whether the engine temperature changes conform to the laws of thermodynamics and whether the flight speed changes conform to the aerodynamic characteristics to form a verification data set.

[0104] Generate a repair request for the data segments marked as incomplete or unreasonable in the verification data set, including information such as data type, time period, and reason for missing, to form a repair task list. The cloud searches the historical flight database based on the repair task list to find data patterns in similar flight phases and similar working conditions. Through pattern matching, the closest historical data is found as a reference to generate a matching data group. Perform data law analysis on the matching data group, extract data change trends, parameter correlations, and constraints, and establish a data recovery rule set. Based on the recovery rule set, complete the incomplete data, use interpolation, approximation, and other methods to calculate the values ​​of missing data points, and generate a completed data stream to ensure that the completed data conforms to physical laws and system constraints.

[0105] The quality of the completed data stream is evaluated through a collaborative decoding algorithm, and the evaluation indicators include data continuity, physical rationality, and time consistency. The evaluation results are recorded in a quality evaluation table, which includes the recovery quality score and credibility level of each data segment. The data in the quality evaluation table is normalized to unify different types of data to a standard scale for comprehensive analysis and comparison, and to generate a standard data set. Finally, the complete decoded data of the edge node and the repaired data in the cloud are integrated and reorganized in chronological order to form complete transmission flight data.

[0106] For example, during a flight data transmission, the edge node received 500MB of encoded data, and the integrity check found that 10MB of the data had problems. After the edge node successfully decoded 480MB of complete data, the problematic data was uploaded to the cloud for processing. The cloud found that these data were mainly records of engine temperature during a period of drastic changes. By searching the historical database, 100 sets of temperature change patterns under similar working conditions were found. Analysis of these patterns revealed that under similar working conditions, temperature changes follow a specific law: when the engine power suddenly increases from 60% to 90%, the temperature shows a characteristic of first rising rapidly and then slowly stabilizing within 15 seconds. Based on this law, the missing temperature data is supplemented. The supplemented data is quality evaluated, and the evaluation indicators show that the data continuity reaches 98%, and the physical rationality verification pass rate is 99%. Finally, the repaired 10MB of data is integrated with the original 480MB of data to obtain a complete 500MB flight data record.

[0107] The above describes the flight data transmission method based on wireless communication in the embodiment of the present application. The following describes the flight data transmission system based on wireless communication in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the flight data transmission system based on wireless communication includes:

[0108] The acquisition module is used to dynamically adjust the sampling frequency and perform data compensation processing on the flight parameters collected by the engine sensor, avionics system and environmental monitor to obtain multi-source sensing flight data;

[0109] A fusion module, used for performing edge computing noise reduction and data fusion processing on the multi-source sensor flight data to obtain pre-processed transmission data;

[0110] A synchronization module, used for performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data;

[0111] An allocation module is used to perform bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources on the multi-source communication data to obtain priority network data;

[0112] A redundancy module, used for performing hierarchical network coding and transmission redundancy processing on the priority network data to obtain coded transmission data;

[0113] The recovery module is used to perform cloud-edge collaborative decoding and data recovery processing on the encoded transmission data to obtain transmission flight data.

[0114] Through the coordinated cooperation of the above-mentioned components, by dynamically adjusting the sampling frequency and processing the data compensation of the flight parameters collected by the engine sensors, avionics systems and environmental monitors, the sampling frequency can be adaptively adjusted according to the importance of different flight stages and data types, effectively avoiding the data redundancy or information loss caused by the traditional fixed sampling method, and ensuring the continuity and integrity of the data through data compensation processing; by performing edge computing noise reduction and data fusion processing on multi-source sensor flight data, effective noise filtering and intelligent fusion of multi-source data are achieved at the data source, significantly improving data quality and processing efficiency, and reducing the burden of subsequent transmission and storage; the distributed cache node sharding and data synchronization processing mechanism are adopted to realize the distributed storage and synchronous update of data, effectively solving the problem of data loss. It solves the single point failure and performance bottleneck problems faced by traditional centralized storage, and improves the reliability and scalability of the system; through the bidding and bandwidth allocation of WiFi, 4G / 5G and satellite communication resources for multi-source communication data, it realizes the dynamic switching of multiple communication modes and resource optimization configuration, overcomes the defect that a single communication mode is susceptible to interference, and significantly improves the stability and reliability of data transmission; hierarchical network coding and transmission redundancy processing are performed on priority network data. Through hierarchical coding strategies and intelligent redundancy mechanisms, the anti-interference ability and fault tolerance of data transmission are improved, ensuring the priority transmission and reliable arrival of important data; finally, through cloud-edge collaborative decoding and data recovery processing mechanisms, efficient decoding and data recovery in a distributed environment are realized, effectively improving the real-time and availability of the system. Through multi-level technical innovation, this solution builds a complete flight data collection, processing, transmission and recovery system, which not only solves the problems of inaccurate data collection, unstable transmission, and low processing efficiency in traditional solutions, but also realizes efficient, reliable and real-time transmission of flight data.

[0115] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for transmitting flight data based on wireless communication.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for transmitting flight data based on wireless communication, characterized in that: The in-flight data transmission method based on wireless communication comprises: Dynamically adjust the sampling frequency and perform data compensation processing on the flight parameters collected by engine sensors, avionics systems and environmental monitors to obtain multi-source sensing flight data; Performing edge computing noise reduction and data fusion processing on the multi-source sensor flight data to obtain pre-processed transmission data; Performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data; Perform bidding and bandwidth allocation processing on WiFi, 4G / 5G and satellite communication resources on the multi-source communication data to obtain priority network data; The priority network data is subjected to hierarchical network coding and transmission redundancy processing to obtain coded transmission data, including: performing importance classification processing on the priority network data to obtain a hierarchical data set, and performing coding block division processing on the hierarchical data set to obtain a coded data block; performing strong coding protection processing on the coded data block to obtain a protection data group, and performing lightweight coding mapping processing on the protection data group to obtain a mapping data set; performing hierarchical coding processing on the mapping data set through a network coding algorithm to obtain a hierarchical coding stream, and performing redundancy calculation processing on the hierarchical coding stream to obtain a redundant parameter table; performing transmission status detection processing on the redundant parameter table to obtain a network status set, and performing redundancy adjustment processing on the network status set to obtain an adjustment parameter group; performing network feedback processing on the adjustment parameter group to obtain a feedback data stream, and performing redundancy strategy adjustment processing on the feedback data stream to obtain a redundant scheme; performing coding parameter setting processing on the redundant scheme to obtain a coding strategy set, and performing transmission coding processing on the coding strategy set to obtain the coded transmission data; The encoded transmission data is subjected to cloud-edge collaborative decoding and data recovery processing to obtain transmission flight data.

2. The method for transmitting in-flight data based on wireless communication according to claim 1, characterized in that: The dynamic sampling frequency adjustment and data compensation processing of the flight parameters collected by the engine sensor, avionics system and environmental monitor to obtain multi-source sensor flight data includes: Performing frequency analysis on the temperature, pressure and speed parameters collected by the engine sensor to obtain an engine sampling threshold, and performing data classification on the engine sampling threshold to obtain an engine dynamic sampling rate; Performing time series analysis on the attitude, heading, and speed parameters collected by the avionics system to obtain an avionics sampling threshold, and performing stage division processing on the avionics sampling threshold to obtain an avionics dynamic sampling rate; Performing fluctuation detection processing on the air pressure, temperature, and humidity parameters collected by the environmental monitor to obtain an environmental sampling threshold, and performing interval statistical processing on the environmental sampling threshold to obtain an environmental dynamic sampling rate; Performing noise filtering processing on the engine dynamic sampling rate, the avionics dynamic sampling rate, and the environment dynamic sampling rate through a Kalman filter algorithm to obtain a sampling rate sequence, and performing timestamp marking processing on the sampling rate sequence to obtain a calibration sampling sequence; Performing data compensation window division processing on the calibration sampling sequence to obtain a compensation time window, and performing linear interpolation processing on the missing data in the compensation time window to obtain a compensation data sequence; The compensation data sequence is subjected to data fusion processing by means of a weighted average algorithm to obtain a fused data set, and the fused data set is subjected to consistency verification processing to obtain the multi-source sensing flight data.

3. The method for transmitting in-flight data based on wireless communication according to claim 1, characterized in that: The performing edge computing noise reduction and data fusion processing on the multi-source sensor flight data to obtain pre-processed transmission data includes: Performing data grouping processing on the multi-source sensor flight data to obtain a sensor data group, and performing edge node distribution processing on the sensor data group to obtain an edge computing sequence; Performing notch filtering on the periodic noise in the edge computing sequence to obtain primary filtering data, and performing random noise elimination on the primary filtering data to obtain a noise reduction data group; Performing GPS positioning data extraction processing on the noise reduction data group to obtain a position data set, and performing inertial navigation data association processing on the position data set to obtain fused position data; Performing time synchronization processing on the fused position data to obtain a synchronous data stream, and performing feature extraction processing on the synchronous data stream through a wavelet transform algorithm to obtain a feature data set; Performing data type labeling processing on the feature data set to obtain a labeled data stream, and performing cross-validation processing on the labeled data stream to obtain candidate transmission data; Perform edge node consistency processing on the candidate transmission data to obtain the pre-processed transmission data.

4. The method for transmitting in-flight data based on wireless communication according to claim 1, characterized in that: The performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data includes: Performing time window division processing on the preprocessed transmission data to obtain time series data segments, and performing data type classification processing on the time series data segments to obtain classified data segments; Performing cache node distribution processing on the classified data slices to obtain a node distribution map, and performing data shard mapping processing on the node distribution map to obtain a shard mapping table; Performing node load calculation processing on the shard mapping table to obtain a load data set, and performing shard allocation processing on the load data set to obtain a shard storage solution; Performing consistency protocol processing on the data in the shard storage solution to obtain a synchronization rule set, and performing inter-node communication processing on the synchronization rule set to obtain a communication data stream; Performing data synchronization processing on the communication data stream by using a consistent hashing algorithm to obtain a synchronized data set, and performing copy creation processing on the synchronized data set to obtain a backup data set; Distributed node integration processing is performed on the backup data group to obtain the multi-source communication data.

5. The method for transmitting in-flight data based on wireless communication according to claim 1, characterized in that: The bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources are performed on the multi-source communication data to obtain priority network data, including: Performing communication quality detection processing on the multi-source communication data to obtain WiFi signal strength, 4G / 5G signal strength and satellite signal strength data, and performing communication resource evaluation processing on the signal strength data to obtain a resource status table; Performing bandwidth capacity calculation processing on the resource status table to obtain available bandwidth data, and performing delay measurement processing on the available bandwidth data to obtain a communication delay table; Perform resource bidding weight calculation processing on the communication delay table to obtain an initial bidding table, and perform communication cost accounting processing on the initial bidding table to obtain a bidding plan; Performing communication resource reservation processing on the bidding scheme to obtain a bandwidth reservation table, and performing emergency channel allocation processing on the bandwidth reservation table to obtain an emergency resource pool; Performing multi-channel combination processing on the emergency resource pool to obtain a channel combination scheme, and performing redundant path planning processing on the channel combination scheme to obtain a standby channel table; Performing resource scheduling processing on the standby channel table to obtain a scheduling sequence, and performing bandwidth allocation processing on the scheduling sequence to obtain a bandwidth allocation table; Performing communication quality assurance processing on the bandwidth allocation table to obtain service quality parameters, and performing resource dynamic adjustment processing on the service quality parameters to obtain an adjustment plan; The adjustment scheme is prioritized by a resource allocation algorithm to obtain a data transmission queue, and resource binding is performed on the data transmission queue to obtain the priority network data.

6. The method for transmitting in-flight data based on wireless communication according to claim 1, characterized in that: The performing cloud-edge collaborative decoding and data recovery processing on the encoded transmission data to obtain transmission flight data includes: Performing edge node reception processing on the encoded transmission data to obtain a received data stream, and performing data integrity check processing on the received data stream to obtain an integrity report; Performing edge decoding processing on the complete data block in the integrity report to obtain edge decoded data, and performing cloud collaborative verification processing on the edge decoded data to obtain a verification data set; Performing cloud repair request processing on the incomplete data in the verification data set to obtain a repair task table, and performing historical data matching processing on the repair task table to obtain a matching data group; Performing data recovery rule analysis on the matching data group to obtain a recovery rule set, and performing incomplete data completion processing on the recovery rule set to obtain a completed data stream; Performing data quality assessment processing on the complementary data stream through a collaborative decoding algorithm to obtain a quality assessment table, and performing data normalization processing on the quality assessment table to obtain a standard data set; The standard data set is subjected to cloud-edge data integration processing to obtain the transmission flight data.

7. A flight data transmission system based on wireless communication, used to implement the flight data transmission method based on wireless communication as described in any one of claims 1 to 6, characterized in that: The flight data transmission system based on wireless communication includes: The acquisition module is used to dynamically adjust the sampling frequency and perform data compensation processing on the flight parameters collected by the engine sensor, avionics system and environmental monitor to obtain multi-source sensing flight data; A fusion module, used for performing edge computing noise reduction and data fusion processing on the multi-source sensor flight data to obtain pre-processed transmission data; A synchronization module, used for performing distributed cache node sharding and data synchronization processing on the pre-processed transmission data to obtain multi-source communication data; An allocation module is used to perform bidding and bandwidth allocation processing of WiFi, 4G / 5G and satellite communication resources on the multi-source communication data to obtain priority network data; A redundancy module is used to perform hierarchical network coding and transmission redundancy processing on the priority network data to obtain coded transmission data, including: performing importance classification processing on the priority network data to obtain a hierarchical data set, and performing coding block division processing on the hierarchical data set to obtain a coded data block; performing strong coding protection processing on the coded data block to obtain a protection data group, and performing lightweight coding mapping processing on the protection data group to obtain a mapping data set; performing hierarchical coding processing on the mapping data set through a network coding algorithm to obtain a hierarchical coding stream, and performing redundancy calculation processing on the hierarchical coding stream to obtain a redundant parameter table; performing transmission status detection processing on the redundant parameter table to obtain a network status set, and performing redundancy adjustment processing on the network status set to obtain an adjustment parameter group; performing network feedback processing on the adjustment parameter group to obtain a feedback data stream, and performing redundancy strategy adjustment processing on the feedback data stream to obtain a redundant scheme; performing coding parameter setting processing on the redundant scheme to obtain a coding strategy set, and performing transmission coding processing on the coding strategy set to obtain the coded transmission data; The recovery module is used to perform cloud-edge collaborative decoding and data recovery processing on the encoded transmission data to obtain transmission flight data.

8. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the flight data transmission method based on wireless communication is implemented as described in any one of claims 1-6.

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