A real-time monitoring method and system for flight interface data based on integrated display and controller

Abstract

This invention relates to the field of data monitoring technology, and in particular to a method and system for real-time monitoring of flight interface data based on an integrated display and control unit (IDE). The method includes: Step 1, acquiring raw data from the flight system; Step 2, acquiring associated data related to the target IDE from the raw data, and generating display content for the flight interface of the target IDE based on the associated data; Step 3, verifying the output of the display content, i.e., detecting whether the display content meets expectations before the pixels of the display content are output to the physical screen of the IDE; if yes, proceed to Step 4; otherwise, return to Step 1; Step 4, monitoring the reliability of the associated data and open-source components; Step 5, based on the monitoring results, outputting the display content to the target IDE in real time.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, and in particular to a method and system for real-time monitoring of flight interface data based on an integrated display and control unit. Background Technology

[0002] In an aircraft, a so-called integrated display and control unit (IDU) integrates key flight, navigation, engine, and system status information, which were originally scattered across numerous mechanical instruments, onto one or more large, color displays. The IDU's real-time monitoring of data involves processes such as data acquisition, high-speed data transmission, real-time data processing, and image rendering, ensuring that the content displayed on the flight interface is clear, intuitive, and reliable.

[0003] Therefore, how to quickly and accurately generate display content and monitor the content to be displayed before it is output to the physical screen of the integrated display controller in order to ensure the display effect and reliability of the content is an urgent problem to be solved. Summary of the Invention

[0004] This invention acquires the associated data required by the target integrated display controller, generates display content using the associated data, and performs output verification before the display content is output to ensure that the display content meets the expected effect.

[0005] The technical solution proposed in this invention is: a method for real-time monitoring of flight interface data based on an integrated display and control unit, the method comprising:

[0006] Step 1: Obtain raw data from the flight system;

[0007] Step 2: Obtain the associated data with the target integrated display and control unit from the raw data, and generate the display content of the flight interface of the target integrated display and control unit based on the associated data;

[0008] Step 3: Verify the output of the displayed content. That is, check whether the displayed content meets expectations before the pixels of the displayed content are output to the physical screen of the integrated display controller. If yes, proceed to step 4; otherwise, return to step 1.

[0009] Step 4: Monitor the reliability of related data and open-source components;

[0010] Step 5: Based on the monitoring results, output the display content to the target integrated display and control unit in real time.

[0011] Preferably, acquiring raw data from the flight system includes:

[0012] Multiple target buses are monitored through a multi-channel data bus interface to obtain multi-source data;

[0013] Virtual link scheduling is used to prioritize the transmission of high-priority data from multiple sources, including flight attitude data and airspeed.

[0014] The target bus protocol is parsed, and the corresponding feature values ​​are extracted from the multi-source data to form a set of feature values ​​for the display content, and a timestamp is added.

[0015] The Kalman filter algorithm is used to fuse data within the feature set of the displayed content to obtain estimated values ​​that can be used for display; including:

[0016] The feature vector is constructed by selecting multiple data points from the feature value set of the displayed content. ;in, This represents the set of feature values ​​for the displayed content; The dimension of the eigenvector;

[0017] The fused feature vector is ;in, Represents the state transition matrix; Represents the process noise matrix;

[0018] Then, the estimated value that can be displayed ;in, Represents the observation matrix. Represents the observation noise matrix;

[0019] By aligning multiple estimated values ​​on the timeline using timestamps, the estimated values ​​of parameters in the same frame of the displayed content are made temporally consistent.

[0020] Preferably, the step of obtaining the associated data related to the target integrated display and control unit from the raw data includes:

[0021] During the system initialization phase, a static configuration association mapping table is constructed to associate the parameters required by the target integrated display and controller with the original data; including:

[0022] Obtain the configuration description of the target integrated display and control unit;

[0023] Semantic features of the integrated display controller are extracted from the configuration description. After preprocessing the semantic features, a semantic feature vector is constructed. The semantic features include the target integrated display controller ID, the target bus interface ID, and the allowed data types to be received.

[0024] Data features are extracted from the raw data, and a data feature vector is constructed after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type;

[0025] By filtering the raw data using the bus interface ID and the allowed data types, the associated data can be obtained.

[0026] That is, the original data must match the data transmission bus ID and the target bus interface ID, and the received data type must match the source data type.

[0027] Preferably, the display content of the flight interface of the target integrated display and control unit generated based on the associated data includes:

[0028] Obtain the currently active display page from the integrated display controller, and determine the list of graphic elements and their dynamic attributes in the display content used for that page, including:

[0029] Based on the ARINC661 cockpit display system standard, a display list is generated, including:

[0030] Obtain the currently active display page of the integrated display controller, and determine the graphic element list and graphic element attributes according to the preset display template and data binding rules of each display page, that is, generate the display list;

[0031] The primitives include: points, lines, surfaces, text, symbols, and bitmaps; the primitive attributes include the primitive's geometry, color, texture, and estimated binding values;

[0032] Using OpenGL SC or Vulkan SC, the primitives in the display list are converted to screen pixels of the integrated display controller; and the image is refreshed at a constant rate for each frame.

[0033] Preferably, detecting whether the displayed content meets expectations before the pixel output of the displayed content is sent to the physical screen of the integrated display controller includes:

[0034] Verification of the frame buffer contents includes:

[0035] Set up two independent image channels;

[0036] The frame image data in the frame buffer is output independently through two separate image channels;

[0037] Before output, image data in two independent image channels are compared pixel by pixel and compared with a preset color difference threshold.

[0038] If the pixel color difference between two independent image channels exceeds a preset color difference threshold, that is: If the displayed content does not meet expectations, an alarm will be triggered, and the display will be downgraded.

[0039] in, and These represent the color values ​​of the same pixel in two independent channels; Indicates the color difference threshold;

[0040] if Then proceed to image integrity monitoring;

[0041] The image integrity monitoring includes:

[0042] Obtain the current frame image data within one image channel and calculate the average brightness of the current frame image data. and color histogram entropy and the average brightness of the previous frame image data. and color histogram entropy Compare and obtain the average brightness difference Difference between color histogram entropy and ;

[0043] Right now:

[0044] ;

[0045] if or If the image is incomplete, an alarm is triggered, and the process returns to step 1.

[0046] Otherwise, the image is judged to be complete, meaning the displayed content meets expectations;

[0047] in, , These represent the average brightness difference threshold and the color histogram entropy difference threshold, respectively.

[0048] Preferably, the reliability of the monitored associated data and open-source components includes:

[0049] Using the physical and dynamic constraints between flight parameters, perform consistency checks on correlated data; including:

[0050] Define parameter association groups, which include:

[0051] Attitude group Navigation Group Engine set ;in, Represents the pitch angle, roll angle, yaw angle, and angular velocity vector; These represent latitude and longitude, flight altitude, airspeed, and heading, respectively. These represent engine speed, exhaust temperature, and fuel flow rate, respectively.

[0052] Construct the associated parameter constraint equations:

[0053] ;

[0054] in, Represents gravitational acceleration; , Represents the correlation coefficient; Indicates pitch angular velocity; Indicates yaw rate;

[0055] Calculate the physical constraint residuals for each associated group parameter:

[0056] ;in, Indicates the standard deviation of measurement noise; Indicates the observed values ​​of the input variable. Indicates the observed values ​​of the output variable;

[0057] Set an adaptive residual threshold ;in, , These represent the fitting coefficients for the flight envelope data; Indicates dynamic pressure; ; Indicates air density; Indicates the volume of the aircraft;

[0058] Fault decision logic based on associated parameters: If If the associated data is found to be abnormal, a sensor anomaly alarm will be triggered, and a backup sensor will be activated.

[0059] Constructing a quantitative credibility model to quantitatively assess the reliability of open-source components includes:

[0060] Constructing an evaluation framework: ; , Indicates the quantitative weight of the evaluation;

[0061] Code instruction credibility ; This indicates the static defect density of the code. Indicates test coverage;

[0062] Runtime performance reliability ; Indicates runtime error rate; This indicates the worst-case execution time deviation.

[0063] Preferably, the monitoring of the associated data and the reliability of the open-source components further includes:

[0064] Construct a hybrid interpretable monitoring model to obtain the probability of anomalies and confidence levels of anomaly types in correlated data, including:

[0065] Perform deep feature extraction: in Indicates input data; This represents the encoder function, used to... Deep features mapped into the latent space ; The decoder function represents the deep features. Map back to output space; Indicates the error value;

[0066] Constructing an uncertainty quantification residual interpreter:

[0067] ;in, Indicates the weight of physical constraints; This represents the credibility coefficient of deep learning. Indicates the probability of anomalies in the associated data; The sigmoid function is represented by MLP; MLP stands for Multilayer Perceptron. This represents a long short-term memory recurrent neural network;

[0068] Construct a type confidence vector model:

[0069] ;in, This represents a confidence vector for anomalous data types. This represents vector concatenation. Represents the weight matrix. Represents the bias vector;

[0070] This represents the activation function. Represents the physical constraint residual vector. ;

[0071] if If the associated data is deemed abnormal, a backup sensor is activated, and the confidence level for each type of abnormality is calculated. Classify the associated data of the anomalies according to the confidence level of the anomaly type; return to step 1.

[0072] Preferably, the step of outputting display content to the target integrated display and control unit in real time based on the monitoring results includes:

[0073] if Establish mathematical relationships between displayed content and associated data, and quantify the impact of monitoring results on the reliability of displayed content, including:

[0074] Construct a model of data dependencies: ;in, Indicates the content to be displayed and the associated dataset. ; This represents the function that generates the displayed content; This indicates noise and errors during the content generation process.

[0075] Calculate the dependency of the displayed content on the associated dataset. ;

[0076] Obtain the data credibility score of the associated dataset ; , This indicates the weight of associated data and the weight of color consistency; it also indicates the credibility of the displayed content. = , Weighting of average brightness difference and weighting of color histogram entropy difference. Indicates the attenuation coefficient;

[0077] The overall credibility of the displayed content is then... ;if Then the corresponding display content will be output; where, Total confidence threshold for displayed content

[0078] A real-time monitoring system for flight interface data based on an integrated display and control unit, the system being used to execute the aforementioned real-time monitoring method for flight interface data based on an integrated display and control unit.

[0079] A computer-readable storage medium storing a computer program that is executed by a processor to implement the above-described method for real-time monitoring of flight interface data based on an integrated display and control unit.

[0080] The beneficial effects of this invention are:

[0081] This invention utilizes the physical and dynamic constraints between associated data (flight parameters acquired by various sensors) when generating display content to monitor the reliability of associated data related to the display content in real time, rather than relying solely on statistical correlation as in traditional technologies. At the same time, in some aircraft using open-source components (Linux kernel, OpenGL driver, etc.), the reliability of open-source components is also monitored, solving the black-box problem of open-source data. Attached Figure Description

[0082] Figure 1 This is a flowchart of a real-time monitoring method for flight interface data based on an integrated display and control unit according to the present invention. Detailed Implementation

[0083] The following description is intended to disclose the present invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious modifications will occur to those skilled in the art. The basic principles of the invention defined in the following description can be applied to other embodiments, modifications, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the invention.

[0084] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0085] refer to Figure 1 The technical solution provided by this invention is: a method for real-time monitoring of flight interface data based on an integrated display and control unit, comprising the following steps:

[0086] Step 1: Obtain raw data from the flight system; including the following steps:

[0087] Multiple target buses (including ARINC429 bus, AFDX bus, etc.) are monitored through a multi-channel data bus interface to obtain multi-source data;

[0088] Virtual link scheduling prioritizes the transmission of high-priority data from multiple sources, for example, using weighted fair queue (WFQ) or priority queue (PQ) scheduling algorithms. This high-priority data includes flight attitude data and airspeed.

[0089] The target bus protocol is parsed, and the corresponding feature values ​​are extracted from the multi-source data to form a set of feature values ​​for the display content, and a timestamp is added.

[0090] The Kalman filter algorithm is used to fuse data within the feature set of the displayed content to obtain estimated values ​​that can be used for display; including:

[0091] The feature vector is constructed by selecting multiple data points from the feature value set of the displayed content. ;in, This represents the set of feature values ​​for the displayed content; The dimension of the eigenvector;

[0092] The fused feature vector is ;in, Represents the state transition matrix; Represents the process noise matrix;

[0093] Then, the estimated value that can be displayed ;in, Represents the observation matrix. Represents the observation noise matrix;

[0094] By aligning multiple estimated values ​​on the timeline using timestamps, the estimated values ​​of parameters in the same frame of the displayed content are made temporally consistent.

[0095] Step 2: Obtain the associated data with the target integrated display and control unit from the raw data, and generate the display content of the flight interface of the target integrated display and control unit based on the associated data;

[0096] The process of filtering associated data includes the following steps: During the system initialization phase, a static configuration association mapping table is constructed to associate the parameters required by the target integrated display and controller with the original data; including:

[0097] Obtain the configuration description of the target integrated display and control unit;

[0098] Semantic features of the integrated display controller are extracted from the configuration description. After preprocessing the semantic features, a semantic feature vector is constructed. The semantic features include the target integrated display controller ID, the target bus interface ID, and the allowed data types to be received.

[0099] Data features are extracted from the raw data, and a data feature vector is constructed after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type;

[0100] By filtering the raw data using the bus interface ID and the allowed data types, the associated data can be obtained.

[0101] That is, the original data must match the data transmission bus ID and the target bus interface ID, and the received data type must match the source data type.

[0102] The content generation process includes the following steps:

[0103] Obtain the currently active display page from the integrated display controller, and determine the list of graphic elements and their dynamic attributes in the display content used for that page, including:

[0104] Based on the ARINC661 cockpit display system standard, a display list is generated, including:

[0105] Obtain the currently active display page of the integrated display controller, and determine the graphic element list and graphic element attributes according to the preset display template and data binding rules of each display page, that is, generate the display list;

[0106] The primitives include: points, lines, surfaces, text, symbols, and bitmaps; the primitive attributes include the primitive's geometry, color, texture, and estimated binding values;

[0107] The primitives in the display list are converted to screen pixels of the integrated display controller via OpenGL SC or Vulkan SC; and refreshed at a constant rate (e.g., 60Hz refresh rate), using double / triple buffering algorithms to avoid image tearing;

[0108] Step 3: Verify the output of the displayed content. That is, check whether the displayed content meets expectations before the pixels of the displayed content are output to the physical screen of the integrated display controller. If yes, proceed to step 4; otherwise, return to step 1.

[0109] The process of determining whether the displayed content meets expectations includes the following steps:

[0110] Verification of the frame buffer contents includes:

[0111] Set up two independent image channels;

[0112] The frame image data in the frame buffer is output independently through two separate image channels;

[0113] Before output, image data in two independent image channels are compared pixel by pixel and compared with a preset color difference threshold.

[0114] If the pixel color difference between two independent image channels exceeds a preset color difference threshold, that is: If the displayed content does not meet expectations, an alarm will be triggered, and the display will be downgraded.

[0115] in, and These represent the color values ​​of the same pixel in two independent channels; Indicates the color difference threshold;

[0116] if Then proceed to image integrity monitoring;

[0117] The image integrity monitoring includes:

[0118] Obtain the current frame image data within one image channel and calculate the average brightness of the current frame image data. and color histogram entropy and the average brightness of the previous frame image data. and color histogram entropy Compare and obtain the average brightness difference Difference between color histogram entropy and ;

[0119] Right now:

[0120] ;

[0121] if or If the image is incomplete, an alarm is triggered, and the process returns to step 1.

[0122] Otherwise, the image is judged to be complete, meaning the displayed content meets expectations; where, , These represent the average brightness difference threshold and the color histogram entropy difference threshold, respectively.

[0123] Step 4: Monitor the reliability of related data and open-source components; including the following steps:

[0124] Using the physical and dynamic constraints between flight parameters, perform consistency checks on correlated data; including:

[0125] Define parameter association groups, which include:

[0126] Attitude group Navigation Group Engine set ;in, Represents the pitch angle, roll angle, yaw angle, and angular velocity vector; These represent latitude and longitude, flight altitude, airspeed, and heading, respectively. These represent engine speed (maximum speed percentage), exhaust temperature, and fuel flow rate, respectively.

[0127] Construct the associated parameter constraint equations:

[0128] ;

[0129] in, Represents gravitational acceleration; , Represents the correlation coefficient; Indicates pitch angular velocity; Indicates yaw rate;

[0130] Calculate the physical constraint residuals for each associated group parameter:

[0131] ;in, Indicates the standard deviation of measurement noise; Indicates the observed values ​​of the input variable. Indicates the observed values ​​of the output variable;

[0132] Set an adaptive residual threshold ;in, , These represent the fitting coefficients for the flight envelope data; Indicates dynamic pressure; ; Indicates air density; Indicates the volume of the aircraft;

[0133] Fault decision logic based on associated parameters: If If the associated data is found to be abnormal, a sensor anomaly alarm will be triggered, and a backup sensor will be activated.

[0134] Constructing a quantitative credibility model to quantitatively assess the reliability of open-source components includes:

[0135] Constructing an evaluation framework: ; , Indicates the quantitative weight of the evaluation;

[0136] Code instruction credibility ; This indicates the static defect density of the code. Indicates test coverage;

[0137] Runtime performance reliability ; Indicates runtime error rate; This indicates the worst-case execution time deviation.

[0138] By covering unknown scenarios with physical constraints in the above steps, the limitations of traditional fault injection models that rely on limited fault scenarios are overcome.

[0139] Some aircraft use open-source components, but these components suffer from black-box decision-making problems. This can be addressed through the following steps:

[0140] Construct a hybrid interpretable monitoring model to obtain the probability of anomalies and confidence levels of anomaly types in correlated data, including:

[0141] Perform deep feature extraction: in This indicates input data (such as parameters within a parameter association group). This represents an encoder function (e.g., a neural network) used to... Deep features mapped into the latent space ; This represents a decoder function (e.g., a neural network) used to process deep features. Map back to output space; Indicates the error value;

[0142] Constructing an uncertainty quantification residual interpreter:

[0143] ;in, Indicates the physical constraint weights (determined through FMEA analysis); This represents the credibility coefficient of deep learning. Indicates the probability of anomalies in the associated data; The sigmoid function is represented by MLP; MLP stands for Multilayer Perceptron. This represents a long short-term memory recurrent neural network;

[0144] Construct a type confidence vector model:

[0145] ;in, This represents a confidence vector for anomalous data types. This represents vector concatenation. Represents the weight matrix. Represents the bias vector;

[0146] This represents the activation function, which transforms the linearly transformed output into a probability distribution, and represents the confidence level of various anomaly types to classify faults.

[0147] Represents the physical constraint residual vector. ;

[0148] if If the associated data is deemed abnormal, a backup sensor is activated, and the confidence level for each type of abnormality is calculated. Classify the associated data of the anomalies according to the confidence level of the anomaly type; return to step 1.

[0149] By classifying fault types using residual vectors, the problem of black-box decision-making in open-source components and the difficulty in tracing faults is solved.

[0150] Step 5: Based on the monitoring results, output the display content to the target integrated display and control unit in real time, including the following steps:

[0151] if Establish mathematical relationships between displayed content and associated data, and quantify the impact of monitoring results on the reliability of displayed content, including:

[0152] Construct a model of data dependencies: ;in, Indicates the content to be displayed and the associated dataset. ; This indicates the function that generates the displayed content (including coordinate transformations and conformal mappings). This indicates noise and errors during the content generation process.

[0153] Calculate the dependency of the displayed content on the associated dataset. ;

[0154] Obtain the data credibility score of the associated dataset ; , This indicates the weight of associated data and the weight of color consistency; it also indicates the credibility of the displayed content. = , Weighting of average brightness difference and weighting of color histogram entropy difference. Indicates the attenuation coefficient;

[0155] The overall credibility of the displayed content is then... ;if Then the corresponding display content will be output; where, The overall confidence threshold for displayed content.

[0156] This step quantifies the relationship between associated data and displayed content, as well as the impact of the monitoring results of associated data on the credibility of displayed content.

[0157] The present invention also provides a real-time monitoring system for flight interface data based on an integrated display and control unit, the system being used to execute the aforementioned real-time monitoring method for flight interface data based on an integrated display and control unit.

[0158] A computer-readable storage medium storing a computer program that is executed by a processor to implement the above-described method for real-time monitoring of flight interface data based on an integrated display and control unit.

[0159] The processes described above with reference to the flowcharts in the embodiments disclosed in this invention can be implemented as computer software programs. The embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wire segments, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless segments, wire segments, optical fibers, RF, etc., or any suitable combination thereof.

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0161] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functions and structural principles of the present invention have been shown and explained in the embodiments. Without departing from the principles described, the implementation of the present invention may have any changes or modifications.

Claims

1. A method for real-time monitoring of flight interface data based on an integrated display and control unit, characterized in that, The method includes: Step 1: Obtain raw data from the flight system; Step 2: Obtain the associated data with the target integrated display and control unit from the raw data, and generate the display content of the flight interface of the target integrated display and control unit based on the associated data; Step 3: Verify the output of the displayed content. That is, check whether the displayed content meets expectations before the pixels of the displayed content are output to the physical screen of the integrated display controller. If yes, proceed to step 4; otherwise, return to step 1. Step 4: Monitor the reliability of related data and open-source components, including: Using the physical and dynamic constraints between flight parameters, perform consistency checks on correlated data; including: Define parameter association groups, which include: Attitude group Navigation Group Engine set ;in, Represents the pitch angle, roll angle, yaw angle, and angular velocity vector; These represent latitude and longitude, flight altitude, airspeed, and heading, respectively. These represent engine speed, exhaust temperature, and fuel flow rate, respectively. Construct the associated parameter constraint equations: ； in, Represents gravitational acceleration; , Represents the correlation coefficient; Indicates pitch angular velocity; Indicates yaw rate; Calculate the physical constraint residuals for each associated group parameter: ;in, Indicates the standard deviation of measurement noise; Indicates the observed values ​​of the input variable. Indicates the observed values ​​of the output variable; Set an adaptive residual threshold ;in, , These represent the fitting coefficients for the flight envelope data; Indicates dynamic pressure; ; Indicates air density; Indicates the volume of the aircraft; Fault decision logic based on associated parameters: If If the associated data is found to be abnormal, a sensor anomaly alarm will be triggered, and a backup sensor will be activated. Constructing a quantitative credibility model to quantitatively assess the reliability of open-source components includes: Constructing an evaluation framework: ; , Indicates the quantitative weight of the evaluation; Code instruction credibility ; This indicates the static defect density of the code. Indicates test coverage; Runtime performance reliability ; Indicates runtime error rate; Indicates the worst-case execution time deviation; Step 5: Based on the monitoring results, output the display content to the target integrated display and control unit in real time.

2. The method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 1, characterized in that, The acquisition of raw data from the flight system includes: Multiple target buses are monitored through a multi-channel data bus interface to obtain multi-source data; Virtual link scheduling is used to prioritize the transmission of high-priority data from multiple sources, including flight attitude data and airspeed. The target bus protocol is parsed, and the corresponding feature values ​​are extracted from the multi-source data to form a set of feature values ​​for the display content, and a timestamp is added. The Kalman filter algorithm is used to fuse data within the feature set of the displayed content to obtain estimated values ​​that can be used for display; including: The feature vector is constructed by selecting multiple data points from the feature value set of the displayed content. ;in, This represents the set of feature values ​​for the displayed content; The dimension of the eigenvector; The fused feature vector is ;in, Represents the state transition matrix; Represents the process noise matrix; Then, the estimated value that can be displayed ;in, Represents the observation matrix. Represents the observation noise matrix; By aligning multiple estimated values ​​on the timeline using timestamps, the estimated values ​​of parameters in the same frame of the displayed content are made temporally consistent.

3. The method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 2, characterized in that, The step of obtaining the associated data related to the target integrated display and control unit from the raw data includes: During the system initialization phase, a static configuration association mapping table is constructed to associate the parameters required by the target integrated display and controller with the original data; including: Obtain the configuration description of the target integrated display and control unit; Semantic features of the integrated display controller are extracted from the configuration description. After preprocessing the semantic features, a semantic feature vector is constructed. The semantic features include the target integrated display controller ID, the target bus interface ID, and the allowed data types to be received. Data features are extracted from the raw data, and a data feature vector is constructed after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type; By filtering the raw data using the bus interface ID and the allowed data types, the associated data can be obtained. That is, the original data must match the data transmission bus ID and the target bus interface ID, and the received data type must match the source data type.

4. The method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 3, characterized in that, The display content of the flight interface of the target integrated display and control unit generated based on the associated data includes: Obtain the currently active display page from the integrated display controller, and determine the list of graphic elements and their dynamic attributes in the display content used for that page, including: Based on the ARINC661 cockpit display system standard, a display list is generated, including: Obtain the currently active display page of the integrated display controller, and determine the graphic element list and graphic element attributes according to the preset display template and data binding rules of each display page, that is, generate the display list; The primitives include: points, lines, surfaces, text, symbols, and bitmaps; the primitive attributes include the primitive's geometry, color, texture, and estimated binding values; Using OpenGL SC or Vulkan SC, the primitives in the display list are converted to screen pixels of the integrated display controller; and the image is refreshed at a constant rate for each frame.

5. A method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 4, characterized in that, The step of detecting whether the displayed content meets expectations before the pixel output of the displayed content is sent to the physical screen of the integrated display controller includes: Verification of the frame buffer contents includes: Set up two independent image channels; The frame image data in the frame buffer is output independently through two separate image channels; Before output, image data in two independent image channels are compared pixel by pixel and compared with a preset color difference threshold. If the pixel color difference between two independent image channels exceeds a preset color difference threshold, that is: If the displayed content does not meet expectations, an alarm will be triggered, and the display will be downgraded. in, and These represent the color values ​​of the same pixel in two independent channels; Indicates the color difference threshold; if Then proceed to image integrity monitoring; The image integrity monitoring includes: Obtain the current frame image data within one image channel and calculate the average brightness of the current frame image data. and color histogram entropy and the average brightness of the previous frame image data and color histogram entropy Compare and obtain the average brightness difference Difference between color histogram entropy and ; Right now: ； if or If the image is incomplete, an alarm is triggered, and the process returns to step 1. Otherwise, the image is judged to be complete, meaning the displayed content meets expectations; in, , These represent the average brightness difference threshold and the color histogram entropy difference threshold, respectively.

6. The method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 5, characterized in that, The monitoring of associated data and the reliability of open-source components also include: Construct a hybrid interpretable monitoring model to obtain the probability of anomalies and confidence levels of anomaly types in correlated data, including: Perform deep feature extraction: in Indicates input data; This represents the encoder function, used to... Deep features mapped into the latent space ; The decoder function represents the deep features. Map back to output space; Indicates the error value; Constructing an uncertainty quantification residual interpreter: ;in, Indicates the weight of physical constraints; This represents the credibility coefficient of deep learning. Indicates the probability of anomalies in the associated data; The sigmoid function is represented by MLP; MLP stands for Multilayer Perceptron. This represents a long short-term memory recurrent neural network; Construct a type confidence vector model: ;in, This represents a confidence vector for anomalous data types. This represents vector concatenation. Represents the weight matrix. Represents the bias vector; This represents the activation function. Represents the physical constraint residual vector. ; if If the associated data is deemed abnormal, a backup sensor is activated, and the confidence level for each type of abnormality is calculated. Classify the associated data of the anomalies according to the confidence level of the anomaly type; return to step 1.

7. A method for real-time monitoring of flight interface data based on an integrated display and control unit according to claim 6, characterized in that, The real-time output of display content to the target integrated display and control unit based on the monitoring results includes: if Establish mathematical relationships between displayed content and associated data, and quantify the impact of monitoring results on the reliability of displayed content, including: Construct a model of data dependencies: ;in, Indicates the content to be displayed and the associated dataset. ; This represents the function that generates the displayed content; This indicates noise and errors during the content generation process. Calculate the dependency of the displayed content on the associated dataset. ; Obtain the data credibility score of the associated dataset ; , This indicates the weight of associated data and the weight of color consistency; it also indicates the credibility of the displayed content. = , Weighting of average brightness difference and weighting of color histogram entropy difference. Indicates the attenuation coefficient; The overall credibility of the displayed content is then... ;if Then the corresponding display content will be output; where, The overall confidence threshold for displayed content.

8. A real-time monitoring system for flight interface data based on an integrated display and control unit, characterized in that, The system is used to execute the real-time monitoring method for flight interface data based on an integrated display and control unit as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which is executed by a processor to implement the real-time monitoring method for flight interface data based on an integrated display and control unit as described in any one of claims 1-7.

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