An unmanned aerial vehicle system data processing method, device, equipment and medium

Abstract

The application discloses a kind of unmanned aerial vehicle system data processing method, device, equipment and medium, it is related to computer technical field.The application includes: obtaining XML protocol description file based on unmanned aerial vehicle system ICD interface file construction, wherein ICD special tag with byte offset, bit inner offset, bit width and conversion formula are defined;Filtering condition is disassembled into graphical node and is connected by directed link, and filtering rule model of hierarchical topology structure is obtained;According to the historical data cycle of each channel data and the real-time data flow change rate, the data record period is adaptively sliding adjusted, so that each channel data is synchronized and aligned;Recursive operation is started from termination node, and original bus data is solved according to ICD special tag to obtain input parameter value and operate layer by layer, and data processing result is obtained;Each chart is drawn according to the same time axis, and cursor and zoom linkage are realized.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a data processing method, apparatus, equipment and medium for an unmanned aerial vehicle (UAV) system. Background Technology

[0002] Unmanned aerial vehicle (UAV) data analysis systems undertake critical tasks such as flight status monitoring, testing and evaluation, and fault diagnosis. Their core value lies in transforming the massive amounts of bus data transmitted in real-time during flight into effective information that supports decision-making through preprocessing, fusion analysis, and visualization. As UAVs are increasingly used in both military and civilian fields, system reliability and mission safety increasingly depend on the timeliness and accuracy of data analysis. Especially for complex mission scenarios such as long-endurance and high-maneuverability missions, the ability to quickly locate anomalies and accurately diagnose faults from multi-source heterogeneous data has become a crucial factor restricting the practical application of UAVs. Currently, the industry's commonly used data analysis process still relies primarily on manual interpretation combined with basic charting tools. That is, after data is transmitted and recorded via a link, professional analysts convert it according to pre-written analysis scripts, and then manually compare it using static charts. This approach has significant limitations: the analysis rules are fixed in the software code during the development phase, requiring reprogramming for different drone models or different test subjects, leading to a repetitive development dilemma of "one system for one model"; at the same time, manual interpretation relies on analysts' extensive experience in drone signal definition, bus protocols, and flight mechanisms, which is not only difficult and time-consuming, but also requires analysts to manually align timestamps and repeatedly switch charts when cross-comparing multi-source data due to the different data stream cycles, making it easy to miss key related information and resulting in a high misjudgment rate.

[0003] To address these issues, several automated analysis and assisted modeling technologies have emerged in recent years. For example, some solutions introduce streaming computing platforms, defining rules and generating physical operator trees using event processing languages ​​to improve computational flexibility; others employ graphical modeling to convert discrete event simulation rules into executable code, reducing programming complexity; still others achieve timestamp alignment by calculating the greatest common divisor of sampling periods from multiple data sources, improving synchronization. However, these solutions are all designed for general data processing scenarios and are not customized for the core characteristics of UAV systems. Specifically, streaming computing platforms only use XML for general stream structure definitions, failing to design dedicated parsing tags and signal processing logic based on UAV interface control files. This makes them unable to directly identify byte offsets, bit field definitions, and conversion formulas for different UAV bus models. Rules generated by graphical modeling schemes need to be converted to SimScript or C / C++ code before execution, resulting in additional latency. Furthermore, their node logic is geared towards discrete event simulation, lacking lightweight operators and nesting mechanisms suitable for real-time UAV data verification. Data synchronization methods employ static periodic configuration, failing to adaptively adjust to drastic changes in data flow during UAV flight. Under high-speed flight or maneuvering conditions, data acquisition loss or synchronization deviations are prone to occur, and accuracy is insufficient for fault diagnosis. Moreover, most of the above solutions are "single-function" designs, addressing only one aspect of computation, modeling, or synchronization, without integrating protocol parsing, rule configuration, automated analysis, and dynamic synchronization across the entire process. Users must deploy multiple independent systems and manually connect data streams, resulting in high integration costs and further reduced analysis efficiency due to interaction latency.

[0004] As can be seen from the above, how to achieve efficient, accurate, and real-time synchronous analysis of multi-source heterogeneous data from UAVs is an urgent problem to be solved. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a data processing method, apparatus, device, and medium for unmanned aerial vehicle (UAV) systems, capable of achieving efficient, accurate, and real-time synchronous analysis of multi-source heterogeneous data from UAVs. The specific solution is as follows: Firstly, this application provides a data processing method for an unmanned aerial vehicle (UAV) system, including: Obtain a protocol description file that matches the UAV system to be analyzed; the protocol description file is an XML file built based on the UAV system's ICD interface file, and it uniquely corresponds to the UAV model and ICD version; the protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset and bit width for locating the position of the signal in the raw bus data of the UAV system, and a conversion formula for converting the raw value of the signal into a physical quantity; The filtering conditions to be analyzed are decomposed into multiple graphical nodes, and each graphical node is connected by directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. The system acquires multi-channel data transmitted via multiple data links from the UAV system. The data recording period is adaptively adjusted based on the historical data period and real-time data flow rate of each channel data. Each channel data is recorded according to the adjusted data recording period to ensure that the channel data recorded within each data recording period is synchronized. Each channel data includes the original bus data. The recursive operation starts from the termination node of the filtering rule model and traces backward layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the original bus data after synchronization and alignment is calculated according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter; the operators configured in each graphical node are used to calculate the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; the calculation results of each level are returned layer by layer, and the calculation result of the termination node is used as the data processing result; Charts are drawn and displayed for the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement or scaling operation on any chart, the cursor position or scaling range of the remaining charts on the same time axis is adjusted to be consistent with the chart being operated on.

[0006] Optionally, the UAV ICD dedicated tag may also include ICD version, signal type, aircraft model, aircraft number, frame count, frame check, and bus type; Accordingly, obtaining the protocol description file matching the unmanned aerial vehicle system to be analyzed includes: Using the drone model and the ICD version as keys, multiple protocol description files are stored in a hash table structure; In response to a model switching operation, the protocol description file matching the model and ICD version of the UAV system to be analyzed is retrieved from the hash table structure and loaded.

[0007] Optionally, the data processing method for the unmanned aerial vehicle system further includes: The interface data in the protocol description file is broken down into bus, frame, parameter and signal levels to determine the hierarchical relationship and data boundary between each level. Based on the attribution relationship and the data boundary, frame verification rules, numerical range rules, bus format rules and configuration consistency rules are generated, and the legality of the protocol description file is judged using the generated rules. When the result of the legality judgment is that there is a data anomaly or configuration error, the system traces upwards level by level according to the attribution relationship to locate the level where the data anomaly or configuration error is located, and generates information including the error location, anomaly type and correction prompt.

[0008] Optionally, the step of decomposing the screening conditions to be analyzed into multiple graphical nodes and connecting each graphical node with directed lines to obtain a screening rule model with a hierarchical topology structure for expressing the logical nesting relationship between the graphical nodes includes: In response to selection and drag-and-drop operations on the built-in operator library, the selected operator is bound to a preset data source input box to form the graphical node; the operator library includes threshold comparison operators, data verification operators, multi-signal logic combination operators, difference calculation operators, and flow dynamic judgment operators; The logical nesting relationship between each graphical node is established through the directed connection; the logical nesting relationship includes AND nesting and OR nesting, and the nesting level is dynamically adjusted according to the amount of data; Syntax validation is performed in real time during the establishment of the logical nesting relationship, and graphical annotations are applied to graphical nodes that fail the syntax validation. The filtering rule model is converted into structured data retrieval logic and sent to the analysis engine for execution.

[0009] Optionally, initiating recursive computation from the termination node of the filtering rule model includes: The termination node is automatically identified based on preset termination node characteristics, and the recursive operation is initiated from the identified termination node; the termination node characteristics include fault alarm node characteristics and flight parameter threshold node characteristics; Accordingly, the forward tracing layer by layer to obtain the input parameters of each of the graphical nodes includes: When the operation of the current graphical node depends on the output of the previous graphical node, trace back layer by layer until the input parameter is obtained, and cache the data obtained during the tracing process so that the cached data can be directly called in subsequent tracing. Accordingly, returning the calculation results at each level layer by layer includes: The calculation results at each level are returned along the recursive call stack to the graphical node that initiated the call; the recursive call stack is equipped with a cache area for caching the calculated physical quantity values.

[0010] Optionally, the adaptive sliding adjustment of the data recording period based on the historical data period and real-time data flow rate of each channel includes: The maximum data recording period threshold of the UAV system is preset, and the data recording period is adaptively adjusted by sliding according to the proportional relationship between the historical data period of each channel data, the real-time data flow change rate and the maximum data recording period threshold.

[0011] Optionally, the adaptive sliding adjustment of the data recording period includes: Data is collected from multiple data sources corresponding to each of the aforementioned channels, the data period of each of the aforementioned data sources is determined, and the minimum value among all the aforementioned data periods is determined as the global minimum synchronization period for this round; the data period is the time difference between the timestamps of two adjacent data sources corresponding to the same data source. The smaller of the reference time and the global minimum synchronization period of this round is determined as the adjusted data recording period of this round; the initial value of the reference time is a preset value; For each data source, the number of cyclic recordings corresponding to the data source is determined based on the multiple relationship between the data period of the data source and the adjusted data recording period, and the data points of the data source are cyclically recorded according to the number of cyclic recordings, with the adjusted data recording period as the time interval; After completing this round of recording, the reference time is reset to the global minimum synchronization period of this round; if new data exists, the process jumps to the step of collecting data from multiple data sources corresponding to each channel data, otherwise it ends.

[0012] Secondly, this application provides a data processing device for an unmanned aerial vehicle (UAV) system, comprising: The protocol file acquisition module is used to acquire a protocol description file that matches the UAV system to be analyzed. The protocol description file is an XML file built based on the UAV system's ICD interface file and uniquely corresponds to the UAV model and ICD version. The protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset, and bit width for locating the position of the signal in the original bus data of the UAV system, as well as a conversion formula for converting the original value of the signal into a physical quantity. The rule modeling module is used to decompose the filtering conditions to be analyzed into multiple graphical nodes, and connect each graphical node with directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. The data recording module is used to acquire multi-channel data transmitted by the UAV system via multiple data links, adaptively adjust the data recording period according to the historical data period and real-time data flow rate of each channel data, and record each channel data according to the adjusted data recording period so that the channel data recorded in each data recording period are synchronously aligned; each channel data includes the original bus data; The data processing module is used to initiate recursive calculations from the termination node of the filtering rule model and trace back layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the original bus data after synchronization and alignment is calculated according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter; the operators configured in each graphical node are used to calculate the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; the calculation results of each level are returned layer by layer, and the calculation result of the termination node is used as the data processing result; The data display module is used to draw and display charts of the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement or zoom operation on any of the charts, the cursor positions or zoom ranges of the remaining charts on the same time axis are adjusted to be consistent with the operated chart.

[0013] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is used to execute the computer program to implement the aforementioned data processing method for an unmanned aerial vehicle system.

[0014] Fourthly, this application provides a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned unmanned aerial vehicle system data processing method.

[0015] This application provides a data processing method for an unmanned aerial vehicle (UAV) system, comprising: acquiring a protocol description file matching the UAV system to be analyzed; the protocol description file is an XML file constructed based on the UAV system's ICD interface file, and uniquely corresponds to the UAV model and ICD version; the protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset, and bit width for locating the position of a signal in the original bus data of the UAV system, and a conversion formula for converting the original value of the signal into a physical quantity; decomposing the filtering conditions to be analyzed into multiple graphical nodes, and connecting each graphical node with directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein each graphical node corresponds to a data filtering expression and is configured with operators and input parameters; acquiring multi-channel data formed by the UAV system transmitted through multiple data links, adaptively sliding and adjusting the data recording period according to the historical data period and real-time data flow rate of each channel data, and recording each channel data according to the adjusted data recording period. Channel data is used to synchronize and align the channel data recorded in each data recording period; each channel data includes the original bus data; recursive operation is started from the termination node of the filtering rule model and traced backward layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the synchronized and aligned original bus data is solved according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter. Using the operators configured in each graphical node, the values ​​of the input parameters of the corresponding graphical node are calculated to obtain the calculation results of each graphical node; the calculation results of each level are returned layer by layer, so that the calculation result of the terminal node is used as the data processing result; charts are drawn and displayed on the synchronized and aligned channel data and the data processing results according to the same time axis; in response to the cursor movement operation or scaling operation for any chart, the cursor position or scaling range of the remaining charts on the same time axis is adjusted to be consistent with the chart being operated on.

[0016] As can be seen from the above, this application achieves universal parsing configuration for heterogeneous data from different UAV models by customizing a protocol description file based on the ICD interface for the UAV system, eliminating the need to develop separate parsing interfaces for each model. By constructing a filtering rule model using graphical nodes and hierarchical topology, the configuration of analysis logic no longer relies on programming, lowering the operational threshold. Simultaneously, the model can be directly executed, ensuring real-time analysis response. By sensing dynamic changes in data flow and adaptively adjusting the recording period, it effectively solves the asynchrony problem caused by differences in transmission periods among multi-channel data, ensuring precise alignment of data from each channel within the recording period. Furthermore, by employing a reverse recursive operation mechanism from the termination node combined with the ICD protocol to solve the original bus data, it can automatically complete the matching of filtering conditions and output results, avoiding the tediousness and errors of manual interpretation. Finally, the data from each channel and the analysis results are displayed in a linked chart format along a unified timeline, supporting cross-chart cursor and zoom synchronization operations, facilitating multi-dimensional correlation analysis of data. This enables efficient, accurate, and real-time synchronous analysis of multi-source heterogeneous data from UAVs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This is a flowchart of a data processing method for an unmanned aerial vehicle (UAV) system disclosed in this invention; Figure 2 This is a schematic diagram of the hierarchical structure of a protocol description file disclosed in this invention; Figure 3 This is a graphical editing flowchart of a screening rule model disclosed in this invention; Figure 4 This invention discloses a flowchart of a data recording cycle sliding adjustment process; Figure 5 This invention discloses an automatic data analysis flowchart; Figure 6 This is a schematic diagram of a data processing device for an unmanned aerial vehicle (UAV) system disclosed in this invention. Figure 7 This is a structural diagram of an electronic device disclosed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Unmanned aerial vehicle (UAV) data analysis systems undertake critical tasks such as flight status monitoring, testing and evaluation, and fault diagnosis. Their core value lies in transforming the massive amounts of bus data transmitted in real-time during flight into effective information that supports decision-making through preprocessing, fusion analysis, and visualization. As UAVs are increasingly used in both military and civilian fields, system reliability and mission safety increasingly depend on the timeliness and accuracy of data analysis. Especially for complex mission scenarios such as long-endurance and high-maneuverability missions, the ability to quickly locate anomalies and accurately diagnose faults from multi-source heterogeneous data has become a crucial factor restricting the practical application of UAVs. Currently, the industry's commonly used data analysis process still relies primarily on manual interpretation combined with basic charting tools. That is, after data is transmitted and recorded via a link, professional analysts convert it according to pre-written analysis scripts, and then manually compare it using static charts. This approach has significant limitations: the analysis rules are embedded in the software code during the development phase, requiring reprogramming for different UAV models or test subjects, leading to a repetitive development dilemma of "one system for one model." Furthermore, manual interpretation relies on analysts' extensive experience in UAV signal definition, bus protocols, and flight mechanisms, which is not only time-consuming and has a high barrier to entry, but also presents challenges when cross-referencing multi-source data. Because different data streams have varying periods, analysts must manually align timestamps and repeatedly switch charts, easily overlooking crucial correlation information and resulting in a high misjudgment rate. To address these issues, several automated analysis or assisted modeling solutions have emerged in recent years. For example, some solutions introduce streaming computing platforms, defining rules and generating physical operator trees through event processing languages ​​to improve computational flexibility; others use graphical modeling to convert discrete event simulation rules into executable code to reduce programming difficulty; still others achieve timestamp alignment by calculating the greatest common divisor of multi-source data sampling periods to improve synchronization. However, these solutions are all designed for general data processing scenarios and are not customized for the core characteristics of UAV systems. Specifically, the streaming computing platform only uses XML for general stream structure definition, without designing dedicated parsing tags and signal processing logic based on the UAV's interface control files. It cannot directly identify the byte offsets, bit field definitions, and conversion formulas of different UAV models' buses. The rules generated by the graphical modeling scheme need to be converted into SIMSCRIPT or C / C++ code before they can be executed, resulting in additional latency. Furthermore, its node logic is geared towards discrete event simulation and lacks lightweight operators and nesting mechanisms suitable for real-time verification of UAV data. The data synchronization method uses static periodic configuration and fails to adaptively adjust to the drastic changes in data flow during UAV flight. Under high-speed flight or maneuvering conditions, data acquisition loss or synchronization deviation is likely to occur, and the accuracy is insufficient to meet the requirements of fault diagnosis.Furthermore, most of the aforementioned solutions are "single-function" designs, addressing only one aspect of computation, modeling, or synchronization. They fail to integrate protocol parsing, rule configuration, automated analysis, and dynamic synchronization across the entire process. Users need to deploy multiple independent systems and manually connect data streams, resulting in high integration costs and further reduced analysis efficiency due to interaction latency. Therefore, this application provides a UAV system data processing solution that enables efficient, accurate, and real-time synchronous analysis of multi-source heterogeneous UAV data.

[0021] See Figure 1 As shown in the figure, this application discloses a data processing method for an unmanned aerial vehicle (UAV) system, including: Step S11: Obtain a protocol description file that matches the UAV system to be analyzed; the protocol description file is an XML file built based on the UAV system's ICD interface file, and it uniquely corresponds to the UAV model and ICD version; the protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset and bit width for locating the position of the signal in the original bus data of the UAV system, and a conversion formula for converting the original value of the signal into a physical quantity.

[0022] In this embodiment, the ICD (Interface Control Document) is an interface control file used in the UAV system to define the data interaction format between various buses and devices; the protocol description file uses XML (Extensible Markup Language) as a carrier and is designed for UAV data construction based on TCP / IP (Transmission Control Protocol / Internet Protocol) communication, so as to support the reuse of the same set of data analysis functions among different UAV products and models.

[0023] Unlike XML format, which is only used to define general streaming data structures, the protocol description file in this embodiment, in addition to basic fields, also specifically divides dedicated tags and elements for the characteristics of UAV data, namely the UAV ICD dedicated tags. These UAV ICD dedicated tags include byte offset, bit offset, bit width, and conversion formula, as well as ICD version, signal type, aircraft model, aircraft number, frame count, frame checksum, and bus type, and support adding, deleting, and modifying the above information. The byte offset, bit offset, and bit width are used together to locate the specific position of a signal in the original bus data, while the conversion formula is used to convert the located original signal value into a physical quantity with engineering meaning. Since different models and states of UAV systems can be defined as independent protocol description files, when facing new models or state changes, adaptation can be completed simply by adding or replacing the corresponding protocol description file, without needing to develop a separate parsing interface for each model, greatly enriching the flexibility of adapting to heterogeneous data from multiple models.

[0024] Furthermore, obtaining the protocol description file matching the UAV system to be analyzed may include: storing multiple protocol description files using a hash table structure with the UAV model and ICD version as keys; and retrieving and loading the protocol description file matching the UAV model and ICD version from the hash table structure in response to a model switching operation. That is, with the help of the hash table structure, loading and retrieving content from multiple ICD models can be completed in approximately constant time, thereby supporting one-click switching between multiple protocol configurations and providing stable and quickly switchable data source support for upper-layer data processing.

[0025] See Figure 2 As shown, this embodiment further follows the principles of data, model, and view. Figure 3Each layer organizes and manages the protocol description file, and each layer is designed in conjunction with the characteristics of the UAV ICD interface. The data layer is used to aggregate and manage the underlying interface information of the UAV system. Based on the ICD interface file, it uniformly organizes the raw configuration information of various signals. Each signal node records key information such as its corresponding ICD version, signal attributes, name, physical quantity value, conversion formula, byte offset, bit offset, start address, bit width, and bus type, thereby integrating the raw bus data with the parsing rules. The configuration information is stored in the aforementioned hash table structure to adapt to the heterogeneous data characteristics of multiple UAV models. The model layer is built around the hierarchical relationships and inherent connections between UAV data, and combines actual usage scenarios such as flight testing and fault diagnosis to perform structured organization and rule-based abstraction of the data. Specifically, the UAV system data processing method may further include: decomposing the data in the protocol description file level by level according to bus, frame, parameter, and signal to determine the attribution relationship and data boundary between each level; generating frame verification rules, numerical range rules, bus format rules, and configuration consistency rules based on the attribution relationship and the data boundary, and using the generated rules to perform a legality judgment on the protocol description file; when the result of the legality judgment is that there is a data anomaly or configuration error, tracing upwards level by level according to the attribution relationship to locate the level where the data anomaly or configuration error is located, and generating information including the error location, anomaly type, and correction prompt. That is, based on the ICD definition, the model layer, on the one hand, establishes the temporal association, business association, and constraint association between signals according to the actual working logic of the UAV, and integrates the originally scattered ICD signals into a hierarchical data structure according to the dimensions of frame, parameter group, and channel; on the other hand, it forms the above four types of inspection rules, which are used to perform legality judgment during configuration and parsing, and provide judgment basis and anomaly location capability for upper-layer applications when anomalies occur. The view layer provides operators with a graphical interactive interface for intuitive display and convenient operation of the data model. Each display unit in the interface corresponds one-to-one with the underlying signals, parameters, and frame structures. The four-level structure of bus, data frame, parameter, and signal can be visualized using tree structures, forms, and custom graphics, and supports hierarchical expansion, parameter viewing, and online editing. Modifications made by operators in the view are directly synchronized to the corresponding data items, and the model layer performs verification and updates. Furthermore, the view layer provides a quick switching function between model and ICD versions, supports loading, comparing, and previewing protocol description files for different models, and facilitates operators' intuitive identification of configuration differences between models, thereby reducing the difficulty of multi-model protocol adaptation and maintenance.

[0026] Step S12: Decompose the screening conditions to be analyzed into multiple graphical nodes, and connect each graphical node with directed lines to obtain a screening rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node.

[0027] In this embodiment, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. See also Figure 3 As shown, by constructing a UML (Unified Modeling Language)-like graphical event modeling framework adapted for UAV data analysis, the event rules of data analysis are deconstructed into lightweight, real-time executable visual models. Unlike the complex modeling logic of general discrete event simulation, the nodes and logic of this framework are designed specifically for real-time analysis needs, and can be executed directly after modeling without generating dedicated code. Specifically, firstly, the original event conditions are decomposed into independent lightweight graphical nodes, with each data filtering expression corresponding to a specific type of node; secondly, the nodes are connected through a hierarchical topology, using directed connections to clarify the logical nesting relationships between expressions; finally, an association mapping of numerical operators, logical operators, and relational operators is established to generate a filtering expression model with complete semantics. This model achieves pattern matching capabilities similar to regular expressions through its visual topology, and can dynamically generate structured query statements.

[0028] Specifically, the step of decomposing the filtering conditions to be analyzed into multiple graphical nodes and connecting each graphical node with directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between the graphical nodes may include: in response to selection and drag operations of the built-in operator library, binding the selected operator to a preset data source input box to form the graphical node; the operator library includes threshold comparison operators, data validation operators, multi-signal logic combination operators, difference calculation operators, and flow dynamic judgment operators; establishing the logical nesting relationship between each graphical node through the directed lines; the logical nesting relationship includes AND (logical AND) nesting and OR (logical OR) nesting, and the number of nesting levels is dynamically adjusted according to the data volume; performing syntax validation in real time during the establishment of the logical nesting relationship, and graphically labeling graphical nodes that fail the syntax validation. Correspondingly, after obtaining the filtering rule model, the filtering rule model can also be converted into structured data retrieval logic and sent to the analysis engine for execution. That is, the above modeling process can be specifically divided into the following four steps: first, event operator selection and binding. The system has a built-in operator library for UAV data analysis. Users can select basic filtering operators or UAV system-related operators from the library and then drag and drop the selected operators to the corresponding data source input boxes, such as flight control bus, navigation data, and power system data. The entire process requires no coding. Secondly, it supports multi-condition combination configuration. When constructing composite filtering conditions, users can establish logical relationships between operator nodes through directed connections. These logical relationships support recursive nesting of AND / OR. The system also provides nesting level prompts to avoid excessive nesting that could lead to execution delays. During the relationship establishment process, millisecond-level syntax checks are performed in real time, and configurations that do not conform to data logic are graphically marked in red to ensure correctness. Thirdly, it generates and verifies topology diagrams. After all nodes are configured, the system automatically generates a structured filtering relationship topology diagram, visually displaying the condition combination logic and data flow, while triggering dynamic verification. This dynamic verification includes data source validity verification, operator and data type matching verification, and logical nesting rationality verification to ensure the integrity of expressions and data adaptability. Fourthly, it features a code-free execution model. The resulting visualization model can be converted into a standard-format structured data retrieval logic with a single click and directly deployed to the analysis engine for execution, achieving a "what you see is what you get" modeling-to-execution process without any additional delays caused by code generation. As a result, operators can dynamically define different analysis events graphically during data analysis, without needing professional programming or simulation knowledge; a basic understanding of UAV data characteristics is sufficient to complete the modeling.

[0029] Step S13: Obtain multi-channel data transmitted by the UAV system via multiple data links, adaptively adjust the data recording period according to the historical data period and real-time data flow change rate of each channel data, and record each channel data according to the adjusted data recording period so that the channel data recorded in each data recording period are synchronously aligned.

[0030] In this embodiment, the data in each channel includes the original bus data. UAV platforms typically transmit their data through multiple data chains such as flight control chains, telemetry and control chains, and image chains. Due to differences in redundancy, importance, and transmission rates, these data streams can form multiple data streams with varying transmission cycles. Furthermore, the data flow of a UAV can change drastically under different flight conditions, such as cruising, high-speed flight, maneuvering, and hovering, further complicating data synchronization. This results in asynchrony between different data recorded by various data analysis platforms at the same time, which is particularly significant for scenarios requiring highly accurate fault diagnosis and flight test data analysis.

[0031] To address the aforementioned issues, the adaptive sliding adjustment of the data recording period based on the historical data period and real-time data flow rate of each channel can include: pre-setting a maximum data recording period threshold for the UAV system, and adaptively sliding the data recording period according to the proportional relationship between the historical data period, the real-time data flow rate, and the maximum data recording period threshold for each channel. For example, the default value of the maximum data recording period threshold can be set to 50ms, and customization based on the UAV model is supported; based on this, the current flight condition is determined through dynamic flow perception, causing the recording period to slide and converge with changes in flow, thereby ensuring synchronous alignment of data from each channel within each effective recording period.

[0032] See Figure 4As shown, the adaptive sliding adjustment of the data recording period specifically includes: collecting data from multiple data sources corresponding to each channel data, determining the data period of each data source, and determining the minimum value among all data periods as the global minimum synchronization period for this round; the data period is the time difference between the timestamps of two adjacent data from the corresponding data source; determining the smaller of the reference time and the global minimum synchronization period for this round as the adjusted data recording period for this round; the initial value of the reference time is a preset value; for each data source, determining the number of loop recordings corresponding to the data source based on the multiple relationship between the data period of the data source and the adjusted data recording period, and cyclically recording the data points of the data source at time intervals of the adjusted data recording period according to the number of loop recordings; after completing this round of recording, resetting the reference time to the global minimum synchronization period for this round; if there is new data, jumping to the step of collecting data from multiple data sources corresponding to each channel data, otherwise ending. That is, the above sliding adjustment process executes the following six steps in each round of recording: First, data acquisition and data period calculation. Data is acquired from multiple data sources corresponding to each channel, and the timestamps Tx1 and Tx2 of two adjacent data points from the x-th data source and their corresponding data Dx1 and Dx2 are extracted. The timestamps and data are stored in the queue corresponding to the data source, and the data period ΔTx of the data source is calculated using the following formula: ΔTx = Tx1 - Tx2; Where x is the sequence number of the data source, Tx1 and Tx2 are the timestamps of two adjacent data from the x-th data source, Dx1 and Dx2 are the data corresponding to the two timestamps, and ΔTx is the data period of the x-th data source. Its value is the current historical data period of the data source, and its change between adjacent rounds represents the real-time data flow rate of the channel.

[0033] Secondly, the determination of the global minimum synchronization period and the initialization of the reference time. Iterate through the data periods ΔT1 to ΔTn of all n data sources, and take the minimum value among them as the global minimum synchronization period ΔTmin for this round: ΔTmin = min(ΔT1, ΔT2, …, ΔTn); Where n is the total number of data sources, and ΔTmin is the global minimum synchronization period for this round. Simultaneously, the reference time T0 is initialized, and its initial value can be taken as the aforementioned maximum data recording period threshold, which is then updated based on the processing results of the previous round.

[0034] Third, data records when the reference time is greater than the global minimum synchronization period. If T0 > ΔTmin, then ΔTmin is used as the adjusted data recording period for this round, and the queues corresponding to each data source are traversed, checking whether the ratio ΔTx / ΔTmin between the data period of that data source and the data recording period of this round is less than 1. When ΔTx / ΔTmin < 1, it indicates that the data period of that data source is shorter than the data recording period of this round, and two adjacent data points fall within the same recording period. In this case, the data points are adjusted according to the conditions. Tx1, Dx1 and Tx2, Dx2 Record them together to avoid data from the high-speed channel being discarded during the recording process.

[0035] Fourth, the cyclic expansion recording of long-period data sources. When T0 > ΔTmin and ΔTx / ΔTmin ≥ 1, it indicates that the data period of the data source is not shorter than the data recording period of this round, and one or more recording periods are spanned between two adjacent data points. In this case, the number of cyclic recordings i of the data source in this round is calculated using the following formula: i = ΔTx % ΔTmin; Then, using ΔTmin as the time interval, record the data points of this data source cyclically starting from timestamp Tx1: Tx1 + k·ΔTmin, Dx1 k = 0, 1, …, i-1 Where i represents the number of times the data source is recorded in this round, % represents the integer division operator, i is an integer multiple between ΔTx and ΔTmin; k is the loop variable. Therefore, the data in the low-speed channel is expanded at equal intervals according to the recording period within its data cycle, and its recording time is consistent with that of the high-speed channel.

[0036] Fifth, data records with a reference time no greater than the global minimum synchronization period. If T0 ≤ ΔTmin, then T0 is used as the adjusted data recording period for this round. The queues corresponding to each data source are traversed, and the number of times i is recorded in this round for that data source is calculated using the following formula: i = T0 % ΔTmin; Then, using T0 as the time interval, record the data points of this data source cyclically starting from timestamp Tx1: Tx1 + k·T0, Dx1 k = 0, 1, …, i-1; And record data points simultaneously Tx2, Dx2 This is to ensure that the data at both ends of the recording cycle is completely preserved.

[0037] Sixth, the reference time is reset and looped. After completing the timing alignment process of this round, the reference time is reset to T0 = ΔTmin, which serves as the comparison benchmark for the next round of recording cycle adjustment. If new data still arrives from each data source, the process returns to one of them to continue acquisition and enter the next round of processing; otherwise, the process ends.

[0038] Therefore, the data recording period slides and converges towards the current global minimum value in each round, following the real-time period of each channel's data: when a channel experiences an increase in data flow and a shortening of the data period due to changes in flight conditions, the global minimum synchronization period decreases accordingly, and the data recording period tightens synchronously; when the flow decreases, the data recording period also widens, thus avoiding unnecessary redundant recording while ensuring the synchronization and alignment of data across channels. In practical applications, the channel containing the flight control chain data can be selected as the baseline channel, and the data points of other channels are aligned to the time axis of this baseline channel, ensuring that the data of each channel corresponds one-to-one in time within each data recording period. This provides a synchronized and aligned data foundation for subsequent automatic event analysis and linked chart display.

[0039] Step S14: Initiate recursive computation from the termination node of the filtering rule model and trace back layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, calculate the original bus data after synchronization and alignment based on the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and use the physical quantity value as the value of the input parameter; use the operators configured in each graphical node to perform calculations on the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; return the calculation results of each level layer by layer, and use the calculation result of the termination node as the data processing result.

[0040] In this embodiment, see Figure 5As shown, in the filtering rule model, operators are modularized into independent lightweight node units. Nodes are connected by unidirectional arrows (i.e., directed lines) to form hierarchical execution paths. The recursion depth can be dynamically and adaptively adjusted according to the data volume; for example, the maximum recursion depth can support 10 levels. Each graphical node, as the smallest unit of rule execution, integrates operation types (logical operations, arithmetic operations, etc.), input parameters (UAV data sources, ICD signals, constants, etc.), conditional judgment logic, and behavioral feedback mechanisms (such as threshold alarms and data anomaly annotations). During data analysis, the analysis process is initialized according to the preset event rule models. Each level of node executes operations sequentially, and the operation results serve as the input or constraint conditions for the next level of node. Finally, data results conforming to the event model are output, thus automatically completing the analysis of the target data events.

[0041] Specifically, initiating recursive computation from the termination node of the filtering rule model can include: automatically identifying the termination node based on preset termination node characteristics, and initiating the recursive computation from the identified termination node; the termination node characteristics include fault alarm node characteristics and flight parameter threshold node characteristics. Correspondingly, tracing forward layer by layer to obtain the input parameters of each graphical node can include: when the computation of the current graphical node depends on the output of the preceding graphical node, tracing forward layer by layer until the input parameters are obtained, and caching the data obtained during the tracing process so that the cached data can be directly called in subsequent tracing. Correspondingly, returning the computation results of each level layer by layer can include: returning the computation results of each level along the recursive call stack to the graphical node that initiated the call; the recursive call stack is equipped with a cache area for caching the calculated physical quantity values. That is, the recursive execution mechanism of the rule engine starts from the terminal computation node, which is characterized by having no subsequent connection lines, and the system can automatically identify it as a termination node based on characteristics such as fault alarms and flight parameter thresholds. During execution, the system first locates the termination node and parses its computational logic. If the node's computation depends on the output of the pre-operator, it performs layer-by-layer computation through lightweight forward tracing until the original input data is obtained. During the tracing process, the acquired data is cached to avoid redundant calculations and improve execution efficiency. For signal inputs, the system performs real-time computation on the synchronized and aligned original bus data based on the byte offset, bit offset, bit width, and conversion formula defined in the currently loaded protocol description file, converting the hexadecimal bus data into effective physical quantities such as flight altitude and flight speed. For constant inputs, their values ​​are directly called for computation. All computation processes are managed through a recursive call stack. A dedicated buffer in the call stack is used to cache commonly used ICD computation results, ensuring that the computation results of each layer can be accurately and quickly transmitted to the next level node, ultimately completing the execution chain of the entire rule model. This layered execution and dynamic data transmission mechanism ensures the scalability of complex analysis rules while reducing system coupling through modular and lightweight design.

[0042] Step S15: Draw and display charts of the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement or scaling operation on any chart, adjust the cursor position or scaling range of the remaining charts on the same time axis to be consistent with the chart being operated on.

[0043] In this embodiment, since the aforementioned steps have synchronized the data from each channel within each data recording period, all charts can be drawn directly along the same time axis without the need for analysts to manually align timestamps. Furthermore, when a user moves the cursor on any chart, the cursors of the other charts will automatically move to the same point on the same time axis; when a user zooms in or out on any chart, the horizontal axis display range of the other charts will also adjust to match the manipulated chart. In addition, multiple charts can be merged into the same coordinate system for display, facilitating direct cross-comparison of multi-source data. Thus, analysts can complete correlation analysis without repeatedly switching between multiple windows, avoiding the omission of key data correlations.

[0044] As can be seen from the above, this application uses an XML protocol description file built based on the ICD interface file as a unified entry point for data parsing. The position of the signal in the original bus data and its physical quantity conversion relationship are described in a tagged manner. This allows the same set of analysis functions to be adapted to different models and states of UAV systems by changing the protocol description file, without the need to develop separate parsing interfaces for each model. Furthermore, a filtering rule model is constructed through graphical nodes and a hierarchical topology structure, transforming the filtering logic that originally required programming into a visual configuration operation. Real-time verification is performed during configuration, and execution is directly deployed after configuration. This reduces the professional skill requirements for operators and avoids problems caused by generating intermediate code. This approach introduces additional latency. Simultaneously, by sensing historical data periods and real-time data flow changes in each channel, the data recording period is adaptively adjusted, aligning multiple data streams with different transmission periods within the same recording period. This improves the data synchronization problem caused by period differences and sudden changes in flow. Furthermore, recursive operations starting from the termination node, combined with ICD protocol processing, can automatically complete the conversion from raw bus data to analysis conclusions, reducing manual interpretation and its associated errors. Finally, data from each channel and data processing results are displayed in a linked chart format along the same timeline, with cursors and zooms synchronized across charts. Analysts can perform multi-source data correlation and comparison without repeatedly switching between multiple windows. All these steps are integrated into a single workflow, enabling efficient, accurate, and real-time synchronous analysis of multi-source heterogeneous UAV data.

[0045] See Figure 6 As shown in the figure, this application discloses a data processing device for an unmanned aerial vehicle (UAV) system, comprising: The protocol file acquisition module 11 is used to acquire a protocol description file that matches the UAV system to be analyzed. The protocol description file is an XML file built based on the UAV system's ICD interface file and uniquely corresponds to the UAV model and ICD version. The protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset, and bit width for locating the position of the signal in the original bus data of the UAV system, as well as a conversion formula for converting the original value of the signal into a physical quantity. The rule modeling module 12 is used to decompose the filtering conditions to be analyzed into multiple graphical nodes, and connect each graphical node with directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. Data recording module 13 is used to acquire multi-channel data transmitted by the UAV system via multiple data links, adaptively adjust the data recording period according to the historical data period and real-time data flow rate of each channel data, and record each channel data according to the adjusted data recording period so that the channel data recorded in each data recording period are synchronously aligned; each channel data includes the original bus data; The data processing module 14 is used to initiate recursive calculations from the termination node of the filtering rule model and trace back layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the original bus data after synchronization and alignment is calculated according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter; the operators configured in each graphical node are used to calculate the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; the calculation results of each level are returned layer by layer, and the calculation result of the termination node is used as the data processing result; The data display module 15 is used to draw and display charts of the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement operation or zoom operation for any of the charts, the cursor positions or zoom ranges of the remaining charts on the same time axis are adjusted to be consistent with the operated chart.

[0046] In some specific embodiments, the UAV ICD dedicated tag also includes ICD version, signal type, aircraft model, aircraft number, frame count, frame check, and bus type; Accordingly, the protocol file acquisition module 11 may specifically include: The protocol description file storage unit is used to store multiple protocol description files using the UAV model and the ICD version as keys, and employing a hash table structure. The protocol description file matching unit is used to retrieve and load the protocol description file that matches the model and ICD version of the UAV system to be analyzed from the hash table structure in response to a model switching operation.

[0047] In some specific embodiments, the rule modeling module 12 may specifically include: A graphical node generation unit is used to bind the selected operator to a preset data source input box in response to selection and drag operations of the built-in operator library, so as to form the graphical node; the operator library includes threshold comparison operator, data verification operator, multi-signal logic combination operator, difference calculation operator and flow dynamic judgment operator; A logical nesting relationship establishment unit is used to establish the logical nesting relationship between each of the graphical nodes through the directed connection; the logical nesting relationship includes AND nesting and OR nesting, and the number of nesting levels is dynamically adjusted according to the amount of data; The syntax verification unit is used to perform syntax verification in real time during the establishment of the logical nesting relationship, and to graphically annotate the graphical nodes that fail the syntax verification. The logic transformation unit is used to convert the filtering rule model into structured data retrieval logic and send it to the analysis engine for execution.

[0048] In some specific embodiments, the data recording module 13 may specifically include: An adaptive submodule is used to pre-set the maximum data recording period threshold of the UAV system, and to adaptively adjust the data recording period according to the proportional relationship between the historical data period of each channel data, the real-time data flow change rate and the maximum data recording period threshold. Accordingly, the adaptive submodule may specifically include: The minimum synchronization period determination unit is used to collect data from multiple data sources corresponding to each of the channel data, determine the data period of each of the data sources, and determine the minimum value among all the data periods as the global minimum synchronization period for this round; the data period is the time difference between the timestamps of two adjacent data from the corresponding data source; The data recording period determination unit is used to determine the smaller of the reference time and the global minimum synchronization period of the current round as the adjusted data recording period of the current round; the initial value of the reference time is a preset value; A data recording unit is used to determine the number of cyclic recordings corresponding to each data source based on the multiple relationship between the data period of the data source and the adjusted data recording period, and to cyclically record the data points of the data source according to the number of cyclic recordings and the adjusted data recording period as the time interval. The reference time adjustment unit is used to reset the reference time to the global minimum synchronization period of the current round after completing the current recording; if new data exists, it jumps to the step of collecting data from multiple data sources corresponding to each of the channel data, otherwise it ends.

[0049] In some specific embodiments, the data processing module 14 may specifically include: The termination node identification unit is used to automatically identify the termination node according to preset termination node features, and start the recursive operation from the identified termination node; the termination node features include fault alarm node features and flight parameter threshold node features; The input parameter acquisition unit is used to trace back layer by layer until the input parameter is obtained when the operation of the current graphical node depends on the output of the previous graphical node, and to cache the data acquired during the tracing process so that the cached data can be directly called in subsequent tracing. The calculation result return unit is used to return the calculation results of each level to the graphical node that initiated the call along the recursive call stack; the recursive call stack is provided with a cache area for caching the calculated physical quantity values.

[0050] Furthermore, embodiments of this application also disclose an electronic device, Figure 7 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the UAV system data processing method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0051] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0052] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0053] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the UAV system data processing method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0054] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned unmanned aerial vehicle (UAV) system data processing method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0055] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0056] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0057] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0058] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0059] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A data processing method for an unmanned aerial vehicle (UAV) system, characterized in that, include: Obtain a protocol description file that matches the UAV system to be analyzed; the protocol description file is an XML file built based on the UAV system's ICD interface file, and it uniquely corresponds to the UAV model and ICD version; the protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset and bit width for locating the position of the signal in the raw bus data of the UAV system, and a conversion formula for converting the raw value of the signal into a physical quantity; The filtering conditions to be analyzed are decomposed into multiple graphical nodes, and each graphical node is connected by directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. The system acquires multi-channel data transmitted via multiple data links from the UAV system. The data recording period is adaptively adjusted based on the historical data period and real-time data flow rate of each channel data. Each channel data is recorded according to the adjusted data recording period to ensure that the channel data recorded within each data recording period is synchronized. Each channel data includes the original bus data. The recursive operation starts from the termination node of the filtering rule model and traces backward layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the original bus data after synchronization and alignment is calculated according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter; the operators configured in each graphical node are used to calculate the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; the calculation results of each level are returned layer by layer, and the calculation result of the termination node is used as the data processing result; Charts are drawn and displayed for the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement or scaling operation on any chart, the cursor position or scaling range of the remaining charts on the same time axis is adjusted to be consistent with the chart being operated on.

2. The data processing method for an unmanned aerial vehicle system according to claim 1, characterized in that, The dedicated ICD tag for drones also includes ICD version, signal type, aircraft model, aircraft number, frame count, frame check, and bus type; Accordingly, obtaining the protocol description file matching the unmanned aerial vehicle system to be analyzed includes: Using the drone model and the ICD version as keys, multiple protocol description files are stored in a hash table structure; In response to a model switching operation, the protocol description file matching the model and ICD version of the UAV system to be analyzed is retrieved from the hash table structure and loaded.

3. The data processing method for an unmanned aerial vehicle system according to claim 1, characterized in that, Also includes: The interface data in the protocol description file is broken down into bus, frame, parameter and signal levels to determine the hierarchical relationship and data boundary between each level. Based on the attribution relationship and the data boundary, frame verification rules, numerical range rules, bus format rules and configuration consistency rules are generated, and the legality of the protocol description file is judged using the generated rules. When the result of the legality judgment is that there is a data anomaly or configuration error, the system traces upwards level by level according to the attribution relationship to locate the level where the data anomaly or configuration error is located, and generates information including the error location, anomaly type and correction prompt.

4. The data processing method for an unmanned aerial vehicle system according to claim 1, characterized in that, The process of decomposing the screening criteria to be analyzed into multiple graphical nodes and connecting each graphical node with directed lines to obtain a screening rule model with a hierarchical topology structure for expressing the logical nesting relationship between the graphical nodes includes: In response to selection and drag-and-drop operations on the built-in operator library, the selected operator is bound to a preset data source input box to form the graphical node; the operator library includes threshold comparison operators, data verification operators, multi-signal logic combination operators, difference calculation operators, and flow dynamic judgment operators; The logical nesting relationship between each of the graphical nodes is established through the directed connection; the logical nesting relationship includes AND nesting and OR nesting, and the number of nesting levels is dynamically adjusted according to the amount of data; Syntax validation is performed in real time during the establishment of the logical nesting relationship, and graphical annotations are applied to graphical nodes that fail the syntax validation. The filtering rule model is converted into structured data retrieval logic and sent to the analysis engine for execution.

5. The data processing method for an unmanned aerial vehicle system according to claim 1, characterized in that, The step of initiating recursive computation from the termination node of the filtering rule model includes: The termination node is automatically identified based on preset termination node characteristics, and the recursive operation is initiated from the identified termination node; the termination node characteristics include fault alarm node characteristics and flight parameter threshold node characteristics; Accordingly, the forward tracing layer by layer to obtain the input parameters of each graphical node includes: When the operation of the current graphical node depends on the output of the previous graphical node, trace back layer by layer until the input parameter is obtained, and cache the data obtained during the tracing process so that the cached data can be directly called in subsequent tracing. Accordingly, returning the calculation results at each level layer by layer includes: The calculation results at each level are returned along the recursive call stack to the graphical node that initiated the call; the recursive call stack is equipped with a cache area for caching the calculated physical quantity values.

6. The data processing method for an unmanned aerial vehicle system according to claim 1, characterized in that, The adaptive sliding adjustment of the data recording period based on the historical data period and real-time data flow rate of each channel includes: The maximum data recording period threshold of the UAV system is preset, and the data recording period is adaptively adjusted by sliding according to the proportional relationship between the historical data period of each channel data, the real-time data flow change rate and the maximum data recording period threshold.

7. The data processing method for an unmanned aerial vehicle system according to claim 6, characterized in that, The adaptive sliding adjustment of the data recording period includes: Data is collected from multiple data sources corresponding to each of the aforementioned channels, the data period of each of the aforementioned data sources is determined, and the minimum value among all the aforementioned data periods is determined as the global minimum synchronization period for this round; the data period is the time difference between the timestamps of two adjacent data sources corresponding to the same data source. The smaller of the reference time and the global minimum synchronization period of this round is determined as the adjusted data recording period of this round; the initial value of the reference time is a preset value; For each data source, the number of cyclic recordings corresponding to the data source is determined according to the multiple relationship between the data period of the data source and the adjusted data recording period, and the data points of the data source are cyclically recorded according to the number of cyclic recordings, with the adjusted data recording period as the time interval; After completing this round of recording, the reference time is reset to the global minimum synchronization period of this round; if new data exists, the process jumps to the step of collecting data from multiple data sources corresponding to each channel data, otherwise it ends.

8. A data processing device for an unmanned aerial vehicle (UAV) system, characterized in that, include: The protocol file acquisition module is used to acquire a protocol description file that matches the UAV system to be analyzed. The protocol description file is an XML file built based on the UAV system's ICD interface file and uniquely corresponds to the UAV model and ICD version. The protocol description file defines a dedicated UAV ICD tag, which includes byte offset, bit offset, and bit width for locating the position of the signal in the original bus data of the UAV system, as well as a conversion formula for converting the original value of the signal into a physical quantity. The rule modeling module is used to decompose the filtering conditions to be analyzed into multiple graphical nodes, and connect each graphical node with directed lines to obtain a filtering rule model with a hierarchical topology structure for expressing the logical nesting relationship between each graphical node; wherein, each graphical node corresponds to a data filtering expression and is configured with operators and input parameters. The data recording module is used to acquire multi-channel data transmitted by the UAV system via multiple data links, adaptively adjust the data recording period according to the historical data period and real-time data flow rate of each channel data, and record each channel data according to the adjusted data recording period so that the channel data recorded in each data recording period are synchronously aligned; each channel data includes the original bus data; The data processing module is used to initiate recursive calculations from the termination node of the filtering rule model and trace back layer by layer to obtain the input parameters of each graphical node; the termination node is a graphical node without subsequent directed connections; when the input parameter is a signal, the original bus data after synchronization and alignment is calculated according to the UAV ICD special tag to obtain the physical quantity value corresponding to the signal, and the physical quantity value is used as the value of the input parameter; the operators configured in each graphical node are used to calculate the value of the input parameter of the corresponding graphical node to obtain the calculation result of each graphical node; the calculation results of each level are returned layer by layer, and the calculation result of the termination node is used as the data processing result; The data display module is used to draw and display charts of the synchronized and aligned channel data and the data processing results on the same time axis; in response to a cursor movement or zoom operation on any of the charts, the cursor positions or zoom ranges of the remaining charts on the same time axis are adjusted to be consistent with the operated chart.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the unmanned aerial vehicle system data processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the unmanned aerial vehicle system data processing method as described in any one of claims 1 to 7.

Images