Flight interface data real-time monitoring method and system based on comprehensive display controller

Abstract

The invention relates to the technical field of data monitoring, in particular to a flight interface data real-time monitoring method and system based on a comprehensive display controller, and the method comprises the steps: 1, obtaining original data from a flight system; 2, acquiring associated data associated with the target comprehensive display and control device from the original data, and generating display content of a flight interface of the target comprehensive display and control device based on the associated data; 3, performing output verification on the display content, namely detecting whether the display content meets expectation or not before pixels of the display content are output to a physical screen of the comprehensive display controller; if yes, entering the step 4, otherwise, returning to the step 1; 4, monitoring the reliability of the associated data and the open source component; and step 5, based on a monitoring result, outputting display content to the target comprehensive display controller in real time.

Description

Technical Field

[0001] The present invention relates to the technical field of data monitoring, and in particular to a method and system for real-time monitoring of flight interface data based on an integrated display and controller. Background Art

[0002] In aircraft, an integrated display and controller integrates critical flight, navigation, engine, and system status information, previously distributed across numerous mechanical instruments, onto one or more large, color displays. This integrated display and controller monitors real-time data, encompassing processes such as data acquisition, high-speed data transmission, real-time data processing, and image rendering, ensuring clear, intuitive, and reliable display content on the flight interface.

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

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

[0005] The technical solution proposed by the present invention is: a real-time monitoring method for flight interface data based on an integrated display and controller, the method comprising:

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

[0007] Step 2: acquiring associated data associated with the target integrated display controller from the original data, and generating display content of the flight interface of the target integrated display controller based on the associated data;

[0008] Step 3: Verify the output of the display content, that is, check whether the display content meets expectations before the pixels of the display 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 linked data and open source components;

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

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

[0012] Monitor multiple target buses through a multi-channel data bus interface to obtain multi-source data;

[0013] High-priority data among multi-source data is preferentially transmitted through virtual link scheduling, wherein the high-priority data includes flight attitude data and airspeed;

[0014] Parse the target bus protocol, extract corresponding feature values ​​from multi-source data, form a display content feature value set, and add a timestamp;

[0015] The Kalman filter algorithm is used to fuse the data in the display content feature set to obtain an estimated value that can be used for display; including:

[0016] Display content eigenvalues ​​Select multiple data to form a eigenvector ;in, Represents a set of display content feature values; represents the dimension of the feature vector;

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

[0018] Then, an estimated value can be used to display ;in, represents the observation matrix, represents the observation noise matrix;

[0019] The multiple estimated values ​​obtained are aligned on the time axis using the timestamp, so that the estimated values ​​of the parameters on the same frame of the displayed content have temporal consistency.

[0020] Preferably, the step of obtaining associated data associated with the target integrated display controller from the original 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 controller with the original data; this includes:

[0022] Get the configuration description of the target integrated display controller;

[0023] Extracting semantic features of the integrated display controller from the configuration description, pre-processing the semantic features, and constructing a semantic feature vector; the semantic features include the target integrated display controller ID, the target bus interface ID, and the type of data allowed to be received;

[0024] Extract data features from the original data, and construct a data feature vector after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type;

[0025] Filter the original data by bus interface ID and the type of data allowed to be received to obtain related data;

[0026] That is, the original data satisfies the matching of the data transmission bus ID and the target bus interface ID, allowing the received data type to match the source data type.

[0027] Preferably, the generating of display content of the flight interface of the target integrated display controller based on the associated data includes:

[0028] Get the currently activated display page of the integrated display controller, and determine the list of graphic elements in the display content used to display the page and their dynamic properties, including:

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

[0030] Obtain the currently activated 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 a display list;

[0031] The graphic primitives include: points, lines, surfaces, texts, symbols and bitmaps; the graphic primitive attributes include the geometric shape, color, texture and binding estimation value of the graphic primitive;

[0032] Convert the primitives in the display list to screen pixels of the integrated display controller through OpenGL SC or Vulkan SC; and refresh each frame of the image at a constant rate.

[0033] Preferably, the detecting whether the display content meets expectations before the pixels of the display content are output to the physical screen of the integrated display controller includes:

[0034] Verify the contents of the frame buffer, including:

[0035] Set up two independent image channels;

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

[0037] Compare the image data in two independent image channels pixel by pixel before output and compare with the 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 is triggered and the display is downgraded;

[0039] in, and Represents the color value of the same pixel in two independent channels respectively; represents the color difference threshold;

[0040] if ; then enter image integrity monitoring;

[0041] The image integrity monitoring includes:

[0042] Get the current frame image data in an 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 get the average brightness difference and color histogram entropy difference ;

[0043] Right now:

[0044] ;

[0045] if or , the image is judged to be incomplete, an alarm is triggered, and the process returns to step 1;

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

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

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

[0049] Use physical dynamic constraints between flight parameters to perform consistency checks on associated data; including:

[0050] A parameter association group is defined, wherein the parameter association group includes:

[0051] Posture Group ; Navigation Group ; Engine Group ;in, Represents pitch angle, roll angle, yaw angle, and angular velocity vector; Represent latitude and longitude, flight altitude, airspeed and heading respectively; They represent engine speed, exhaust temperature and fuel flow respectively;

[0052] Construct the associated parameter constraint equation:

[0053] ;

[0054] in, represents the acceleration due to gravity; 、 represents the correlation coefficient; represents the pitch angular velocity; represents the yaw angular velocity;

[0055] Compute the physical constraint residuals for each link group parameter:

[0056] ;in, represents the standard deviation of measurement noise; represents the observed value of the input variable, represents the observed value of the output variable;

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

[0058] Associated parameter failure decision logic: If ; The associated data is judged to be abnormal, the sensor abnormality alarm is triggered, and the backup sensor is activated;

[0059] Construct a quantitative credibility model to quantitatively evaluate the reliability of open source components, including:

[0060] Building an evaluation framework: ; 、 Indicates the evaluation quantization weight;

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

[0062] Runtime performance confidence ; represents the runtime error rate; Indicates the worst execution time deviation.

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

[0064] Build a hybrid explainable monitoring model to obtain the anomaly probability and anomaly type confidence of the associated data, including:

[0065] Perform deep feature extraction: in Represents input data; Represents the encoder function, which is used to Deep features mapped into latent space ; Represents the decoder function, which is used to transform the deep features Map back to the output space; Indicates the error value;

[0066] Construct an uncertainty quantification residual explainer:

[0067] ;in, represents the physical constraint weight; Represents the deep learning credibility coefficient; represents the probability of abnormality of associated data; Represents Sigmoid function; MLP represents multi-layer perceptron; represents a long short-term memory recurrent neural network;

[0068] Construct a type confidence vector model:

[0069] ;in, represents the confidence vector of abnormal data type, represents vector concatenation, represents the weight matrix, represents the bias vector;

[0070] represents the activation function, represents the physical constraint residual vector, ;

[0071] if , then determine that the associated data is abnormal, enable the backup sensor, and calculate the confidence of each abnormal type , classify the abnormal associated data according to the abnormal type confidence; return to step 1.

[0072] Preferably, the real-time output of display content to the target integrated display controller based on the monitoring result includes:

[0073] if , establish a mathematical relationship between display content and associated data, and quantify the impact of monitoring results on the credibility of display content, including:

[0074] Build a dependency model for associated data: ;in, Indicates display content and associated data sets ; Represents the display content generation function; Represents the noise and error in the process of display content generation;

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

[0076] Get the data credibility score of the linked dataset ; 、 , represents the weight of associated data, displays the color consistency weight; obtains the content display credibility: = 、 Average brightness difference weight and color histogram entropy difference weight, represents the attenuation coefficient;

[0077] The total credibility of the displayed content is ;if ; then the corresponding display content is output; where, Display content total confidence threshold

[0078] A real-time monitoring system for flight interface data based on an integrated display and controller is provided, wherein the system is used to execute a real-time monitoring method for flight interface data based on an integrated display and controller.

[0079] A computer-readable storage medium stores a computer program, which is executed by a processor to implement the above-mentioned real-time monitoring method for flight interface data based on an integrated display and controller.

[0080] Beneficial effects of the present invention:

[0081] When generating display content, the present invention utilizes the physical dynamic constraints between associated data (flight parameters acquired by various sensors) 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. Furthermore, in aircraft that use open source components (such as the Linux kernel and OpenGL drivers), the reliability of these open source components is monitored simultaneously, solving the black box problem that exists with open source data. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 The present invention is a flow chart of a method for real-time monitoring of flight interface data based on an integrated display and controller. DETAILED DESCRIPTION

[0083] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are for illustrative purposes only, and those skilled in the art will readily appreciate other obvious variations. The basic principles of the present invention defined in the following description may be applied to other embodiments, variations, improvements, equivalents, and other technical solutions that do not depart from the spirit and scope of the present invention.

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

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

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

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

[0088] High-priority data from multiple sources is prioritized for transmission through virtual link scheduling, such as using a weighted fair queue (WFQ) or priority queue (PQ) scheduling algorithm. The high-priority data includes flight attitude data and airspeed.

[0089] Parse the target bus protocol, extract corresponding feature values ​​from multi-source data, form a display content feature value set, and add a timestamp;

[0090] The Kalman filter algorithm is used to fuse the data in the display content feature set to obtain an estimated value that can be used for display; including:

[0091] Display content eigenvalues ​​Select multiple data to form a eigenvector ;in, Represents a set of display content feature values; represents the dimension of the feature vector;

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

[0093] Then, an estimated value can be used to display ;in, represents the observation matrix, represents the observation noise matrix;

[0094] The multiple estimated values ​​obtained are aligned on the time axis using the timestamp, so that the estimated values ​​of the parameters on the same frame of the displayed content have temporal consistency.

[0095] Step 2: acquiring associated data associated with the target integrated display controller from the original data, and generating display content of the flight interface of the target integrated display controller based on the associated data;

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

[0097] Get the configuration description of the target integrated display controller;

[0098] Extracting semantic features of the integrated display controller from the configuration description, pre-processing the semantic features, and constructing a semantic feature vector; the semantic features include the target integrated display controller ID, the target bus interface ID, and the type of data allowed to be received;

[0099] Extract data features from the original data, and construct a data feature vector after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type;

[0100] Filter the original data by bus interface ID and the type of data allowed to be received to obtain related data;

[0101] That is, the original data satisfies the matching of the data transmission bus ID and the target bus interface ID, allowing the received data type to match the source data type.

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

[0103] Get the currently activated display page of the integrated display controller, and determine the list of graphic elements in the display content used to display the page and their dynamic properties, including:

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

[0105] Obtain the currently activated 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 a display list;

[0106] The graphic primitives include: points, lines, surfaces, texts, symbols and bitmaps; the graphic primitive attributes include the geometric shape, color, texture and binding estimation value of the graphic primitive;

[0107] Convert the primitives in the display list to screen pixels of the integrated display controller through OpenGL SC or Vulkan SC; refresh at a constant rate (for example, 60Hz refresh rate), and use double / triple buffering algorithm to avoid image tearing;

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

[0109] Determining whether the displayed content meets expectations includes the following steps:

[0110] Verify the contents of the frame buffer, including:

[0111] Set up two independent image channels;

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

[0113] Compare the image data in two independent image channels pixel by pixel before output and compare with the 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 is triggered and the display is downgraded;

[0115] in, and Represents the color value of the same pixel in two independent channels respectively; represents the color difference threshold;

[0116] if ; then enter image integrity monitoring;

[0117] The image integrity monitoring includes:

[0118] Get the current frame image data in an 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 get the average brightness difference and color histogram entropy difference ;

[0119] Right now:

[0120] ;

[0121] if or , the image is judged to be incomplete, an alarm is triggered, and the process returns to step 1;

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

[0123] Step 4: Monitor the reliability of linked data and open source components. This includes the following steps:

[0124] Use physical dynamic constraints between flight parameters to perform consistency checks on associated data; including:

[0125] A parameter association group is defined, wherein the parameter association group includes:

[0126] Posture Group ; Navigation Group ; Engine Group ;in, Represents pitch angle, roll angle, yaw angle, and angular velocity vector; Represent latitude and longitude, flight altitude, airspeed and heading respectively; Respectively represent engine speed (percentage of maximum speed), exhaust temperature and fuel flow;

[0127] Construct the associated parameter constraint equation:

[0128] ;

[0129] in, represents the acceleration due to gravity; 、 represents the correlation coefficient; represents the pitch angular velocity; represents the yaw angular velocity;

[0130] Compute the physical constraint residuals for each link group parameter:

[0131] ;in, represents the standard deviation of measurement noise; represents the observed value of the input variable, represents the observed value of the output variable;

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

[0133] Associated parameter failure decision logic: If ; The associated data is judged to be abnormal, the sensor abnormality alarm is triggered, and the backup sensor is activated;

[0134] Construct a quantitative credibility model to quantitatively evaluate the reliability of open source components, including:

[0135] Building an evaluation framework: ; 、 Indicates the evaluation quantization weight;

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

[0137] Runtime performance confidence ; represents the runtime error rate; Indicates the worst execution time deviation.

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

[0139] In some aircraft, open source components are used, but there is a black box decision problem in the unit components. This can be solved by the following steps:

[0140] Build a hybrid explainable monitoring model to obtain the anomaly probability and anomaly type confidence of the associated data, including:

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

[0142] Construct an uncertainty quantification residual explainer:

[0143] ;in, Represents the physical constraint weight (determined through FMEA analysis); Represents the deep learning credibility coefficient; represents the probability of abnormality of associated data; Represents Sigmoid function; MLP represents multi-layer perceptron; represents a long short-term memory recurrent neural network;

[0144] Construct a type confidence vector model:

[0145] ;in, represents the confidence vector of abnormal data type, represents vector concatenation, represents the weight matrix, represents the bias vector;

[0146] represents the activation function, which is used to convert the output after linear transformation into a probability distribution, indicating the confidence of various abnormal types to classify faults;

[0147] represents the physical constraint residual vector, ;

[0148] if , then determine that the associated data is abnormal, enable the backup sensor, and calculate the confidence of each abnormal type , classify the abnormal associated data according to the abnormal type confidence; return to step 1.

[0149] The residual vector is used to classify the fault type, solving the problem of black-box decision-making of open source components and difficulty in tracing faults.

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

[0151] if , establish a mathematical relationship between display content and associated data, and quantify the impact of monitoring results on the credibility of display content, including:

[0152] Build a dependency model for associated data: ;in, Indicates display content and associated data sets ; Represents the display content generation function (including coordinate changes and conformance mapping); Represents the noise and error in the process of display content generation;

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

[0154] Get the data credibility score of the linked dataset ; 、 , represents the weight of associated data, displays the color consistency weight; obtains the content display credibility: = 、 Average brightness difference weight and color histogram entropy difference weight, represents the attenuation coefficient;

[0155] The total credibility of the displayed content is ;if ; then the corresponding display content is output; where, Displays the overall content confidence threshold.

[0156] Through this step, the relationship between the associated data and the displayed content is quantified, as well as the impact of the associated data monitoring results on the credibility of the displayed content.

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

[0158] A computer-readable storage medium stores a computer program, which is executed by a processor to implement the above-mentioned real-time monitoring method for flight interface data based on an integrated display and controller.

[0159] In the embodiments disclosed in the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. The embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit (CPU), the above-mentioned functions defined in the method of the present application are performed. It should be noted that the computer-readable medium mentioned above in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wire segments, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this application, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a propagated data signal, either in baseband or as part of a carrier wave, embodying computer-readable program code. Such a propagated data signal may take various forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, electrical wire, optical fiber cable, RF, etc., or any suitable combination thereof.

[0160] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or portion of code that contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two boxes shown in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or can be implemented 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 only examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments. Without departing from the principles, the implementation methods of the present invention may be subject to any changes or modifications.

Claims

1. A real-time monitoring method for flight interface data based on an integrated display and controller, characterized in that: The method comprises: Step 1: Obtain raw data from the flight system; Step 2: acquiring associated data associated with the target integrated display controller from the original data, and generating display content of the flight interface of the target integrated display controller based on the associated data; Step 3: Verify the output of the display content, that is, check whether the display content meets expectations before the pixels of the display 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 linked data and open source components; Step 5: Based on the monitoring results, the display content is output to the target integrated display controller in real time.

2. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 1, characterized in that: The obtaining of raw data from the flight system includes: Monitor multiple target buses through a multi-channel data bus interface to obtain multi-source data; High-priority data among multi-source data is preferentially transmitted through virtual link scheduling, wherein the high-priority data includes flight attitude data and airspeed; Parse the target bus protocol, extract corresponding feature values ​​from multi-source data, form a display content feature value set, and add a timestamp; The Kalman filter algorithm is used to fuse the data in the display content feature set to obtain an estimated value that can be used for display; including: Display content eigenvalues ​​Select multiple data to form a eigenvector ;in, Represents a set of display content feature values; represents the dimension of the feature vector; The fused feature vector is ;in, represents the state transition matrix; represents the process noise matrix; Then, an estimated value can be used to display ;in, represents the observation matrix, represents the observation noise matrix; The multiple estimated values ​​obtained are aligned on the time axis using the timestamp, so that the estimated values ​​of the parameters on the same frame of the displayed content have temporal consistency.

3. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 2, characterized in that: The step of obtaining associated data associated with the target integrated display controller from the original 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 controller with the original data; this includes: Get the configuration description of the target integrated display controller; Extracting semantic features of the integrated display controller from the configuration description, pre-processing the semantic features, and constructing a semantic feature vector; the semantic features include the target integrated display controller ID, the target bus interface ID, and the type of data allowed to be received; Extract data features from the original data, and construct a data feature vector after preprocessing the data features; the data features include data source, data transmission bus ID, and source data type; Filter the original data by bus interface ID and the type of data allowed to be received to obtain related data; That is, the original data satisfies the matching of the data transmission bus ID and the target bus interface ID, allowing the received data type to match the source data type.

4. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 3, characterized in that: The display content of the flight interface of the target integrated display controller is generated based on the associated data, including: Get the currently activated display page of the integrated display controller, and determine the list of graphic elements in the display content used to display the page and their dynamic properties, including: Based on the cockpit display system standard ARINC661, a display list is generated, including: Obtain the currently activated 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 a display list; The graphic primitives include: points, lines, surfaces, texts, symbols and bitmaps; the graphic primitive attributes include the geometric shape, color, texture and binding estimation value of the graphic primitive; Convert the primitives in the display list to screen pixels of the integrated display controller through OpenGL SC or Vulkan SC; and refresh each frame of the image at a constant rate.

5. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 4, characterized in that: The detecting whether the display content meets expectations before the pixels of the display content are output to the physical screen of the integrated display controller includes: Verify the contents of the frame buffer, including: Set up two independent image channels; The frame image data in the frame buffer is output independently through two independent image channels; Compare the image data in two independent image channels pixel by pixel before output and compare with the 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 is triggered and the display is downgraded; in, and Represents the color value of the same pixel in two independent channels respectively; represents the color difference threshold; if ; then enter image integrity monitoring; The image integrity monitoring includes: Get the current frame image data in an 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 get the average brightness difference and color histogram entropy difference ; Right now: ； if or , the image is judged to be incomplete, an alarm is triggered, and the process returns to step 1; Otherwise, the image is judged to be complete, that is, the displayed content meets expectations; in, 、 They represent the average brightness difference threshold and color histogram entropy difference threshold respectively.

6. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 5, characterized in that: The monitoring of the reliability of associated data and open source components includes: Use physical dynamic constraints between flight parameters to perform consistency checks on associated data; including: A parameter association group is defined, wherein the parameter association group includes: Posture Group ; Navigation Group ; Engine Group ;in, Represents pitch angle, roll angle, yaw angle, and angular velocity vector; Respectively represent latitude and longitude, flight altitude, airspeed and heading; They represent engine speed, exhaust temperature and fuel flow respectively; Construct the associated parameter constraint equation: ； in, represents the acceleration due to gravity; 、 represents the correlation coefficient; represents the pitch angular velocity; represents the yaw angular velocity; Compute the physical constraint residuals for each link group parameter: ;in, represents the standard deviation of measurement noise; represents the observed value of the input variable, represents the observed value of the output variable; Set adaptive residual threshold ;in, 、 They represent the flight envelope data fitting coefficients respectively; Indicates dynamic pressure; ; Indicates the air density; Indicates the volume of the aircraft; Associated parameter failure decision logic: If ; The associated data is judged to be abnormal, the sensor abnormality alarm is triggered, and the backup sensor is activated; Construct a quantitative credibility model to quantitatively evaluate the reliability of open source components, including: Building an evaluation framework: ; 、 Indicates the evaluation quantization weight; Code instruction credibility ; Indicates the static defect density of the code; Indicates test coverage; Runtime performance confidence ; represents the runtime error rate; Indicates the worst execution time deviation.

7. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 6, characterized in that: The monitoring of the reliability of the associated data and open source components further includes: Build a hybrid explainable monitoring model to obtain the anomaly probability and anomaly type confidence of the associated data, including: Perform deep feature extraction: in Represents input data; Represents the encoder function, which is used to Deep features mapped into latent space ; Represents the decoder function, which is used to transform the deep features Map back to the output space; Indicates the error value; Construct an uncertainty quantification residual explainer: ;in, represents the physical constraint weight; represents the deep learning credibility coefficient; represents the probability of abnormality of associated data; Represents Sigmoid function; MLP represents multi-layer perceptron; represents a long short-term memory recurrent neural network; Construct a type confidence vector model: ;in, represents the confidence vector of abnormal data type, represents vector concatenation, represents the weight matrix, represents the bias vector; represents the activation function, represents the physical constraint residual vector, ; if , then determine that the associated data is abnormal, enable the backup sensor, and calculate the confidence of each abnormal type , classify the abnormal associated data according to the abnormal type confidence; return to step 1.

8. The method for real-time monitoring of flight interface data based on an integrated display and controller according to claim 7, characterized in that: The method of outputting display content to the target integrated display controller in real time based on the monitoring results includes: if , establish a mathematical relationship between display content and associated data, and quantify the impact of monitoring results on the credibility of display content, including: Build a dependency model for associated data: ;in, Indicates display content and associated data sets ; Represents the display content generation function; Represents the noise and error in the process of display content generation; Calculate the dependency of displayed content on associated datasets ; Get the credibility score of the linked dataset ; 、 , represents the weight of associated data, displays the color consistency weight; obtains the content display credibility: = 、 Average brightness difference weight and color histogram entropy difference weight, represents the attenuation coefficient; The total credibility of the displayed content is ;if ; then the corresponding display content is output; where, Displays the overall content confidence threshold.

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

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the real-time monitoring method of flight interface data based on an integrated display and controller as described in any one of claims 1 to 8.

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