Intelligent control methods and equipment for airborne testing equipment

Abstract

This application provides an intelligent control method and device for airborne test equipment, applied in the field of airborne test technology. It includes: acquiring the status information of multiple airborne test devices within a preset time period; sampling and extracting features from the status information of each airborne test device to obtain initial physical quantities; mapping the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than that of the two-dimensional space and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determining the current operating status of the airborne test equipment based on the positional relationship between the target feature parameters and a preset decision hyperplane. This significantly simplifies the cumbersome process of manually switching and checking each interface individually, greatly shortens pre-flight preparation time, effectively supports the mission requirements of multiple remote simultaneous launches, and effectively avoids flight mission failures caused by hidden faults.

Description

Technical Field

[0001] This application relates to the field of airborne testing technology, and in particular to an intelligent control method and equipment for airborne testing equipment. Background Technology

[0002] With the continuous development of airborne testing technology and the increasing number and variety of airborne testing equipment, airborne testing personnel need to check the operational status of airborne testing equipment on-site before flight missions, switching back and forth between various equipment interfaces. In other words, traditional pre-flight checks largely rely on manual inspection, a cumbersome and time-consuming process. Furthermore, due to the complexity of airborne testing systems, traditional methods often struggle to check the operational status of airborne testing equipment in real time, potentially leading to equipment malfunctions after a single check, resulting in serious problems such as flight mission failure.

[0003] Therefore, how to efficiently, in real time and accurately achieve integrated monitoring and reliable control of the status of various heterogeneous airborne test equipment has become a technical problem that urgently needs to be solved in the field of airborne test technology. Summary of the Invention

[0004] This application provides an intelligent management and control method and device for airborne test equipment. By automatically collecting status information of multiple heterogeneous airborne test equipment in one stop, it significantly simplifies the tedious process of manually switching and checking each interface one by one, greatly shortens the pre-flight preparation time, and effectively supports the mission requirements of multiple remote simultaneous launches. In addition, by using high-dimensional mapping to construct nonlinear and interactive features, and combining them with a preset decision hyperplane to achieve automatic judgment, it can accurately capture the current operating status of airborne test equipment and effectively avoid flight mission failures caused by hidden faults.

[0005] This application provides an intelligent control method for airborne testing equipment, including: Within a preset time period, acquire the status information of each of the multiple airborne test devices; For each airborne test device, the state information of the airborne test device is sampled and features are extracted to obtain initial physical quantities; the initial physical quantities are mapped from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; the current operating state of the airborne test device is determined according to the positional relationship between the target feature parameters and a preset decision hyperplane.

[0006] According to an embodiment of this application, an intelligent control method for airborne testing equipment is provided. The step of mapping the initial physical quantity from a two-dimensional space to a target dimension space and constructing target feature parameters includes: extracting two first sub-parameters from the initial physical quantity in the two-dimensional space; constructing five second sub-parameters in a five-dimensional space based on the two first sub-parameters through polynomial feature expansion; determining the second sub-parameters in the target dimension space from the five second sub-parameters and using them as target parameters; wherein the dimension of the target dimension space is greater than 2 and less than or equal to 5; and using multiple target parameters as the target feature parameters.

[0007] According to an embodiment of this application, an intelligent control method for airborne testing equipment is provided, wherein the two first sub-parameters are first sub-parameter X1 and first sub-parameter X2; the five second sub-parameters are second sub-parameter Z1, second sub-parameter Z2, second sub-parameter Z3, second sub-parameter Z4, and second sub-parameter Z5; wherein, the second sub-parameter Z1 is the first sub-parameter X1; the second sub-parameter Z2 is the square of the first sub-parameter X1; the second sub-parameter Z3 is the first sub-parameter X2; the second sub-parameter Z4 is the square of the first sub-parameter X2; and the second sub-parameter Z5 is the product of the first sub-parameter X1 and the first sub-parameter X2.

[0008] According to an embodiment of this application, an intelligent management and control method for airborne test equipment is provided. The method for determining the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and a preset decision hyperplane includes: among a plurality of target parameters, if there is a target parameter located on a first side of the preset decision hyperplane, then an abnormal state is taken as the current operating state of the airborne test equipment; if there is no target parameter located on the first side of the preset decision hyperplane, then a normal state is taken as the current operating state; or, from the plurality of target parameters, the number of first target parameters located on the first side of the preset decision hyperplane and the number of second target parameters located on a second side of the preset decision hyperplane are counted. The quantity; if the quantity of the first target parameter is greater than the quantity of the second target parameter, then the abnormal state is taken as the current operating state; if the quantity of the first target parameter is less than the quantity of the second target parameter, then the normal state is taken as the current operating state; if the quantity of the first target parameter is equal to the quantity of the second target parameter, then the fault critical state is taken as the current operating state; or, the average value of multiple target parameters is determined; if the average value is located on the first side of the preset decision hyperplane, then the abnormal state is taken as the current operating state of the airborne test equipment; if the average value is located on the second side of the preset decision hyperplane, then the normal state is taken as the current operating state.

[0009] According to an embodiment of this application, an intelligent control method for airborne test equipment is provided. The method further includes: upon receiving an encrypted network packet from a host computer, parsing the remote control command carried in the encrypted network packet; parsing the remote control command according to a communication protocol to obtain control information including a device identifier and a control value field; wherein the device identifier is used to identify the airborne test equipment to be controlled from among the plurality of airborne test equipment, and the control value field includes: a transmission mode command for switching between Pulse Code Modulation (PCM) and Network Mode, a modulation mode command for switching between Frequency Modulation (FM) and Shaped Offset Quadrature Phase Shift Keying (SOQPSK) Mode, and a frequency point configuration command; and controlling the power supply and operating status of the airborne test equipment to be controlled according to the control information.

[0010] According to an embodiment of this application, an intelligent management and control method for airborne test equipment is provided. The method further includes: encapsulating the communication protocol of each airborne test equipment according to a preset format; wherein, the communication protocol includes: a frame header, a data payload, and a frame tail; the frame header includes the aircraft number PlaneCode; the data payload includes the test system configuration SysConfig and the subsystem type SubSysType; the communication protocol carries two types of instructions, namely control instructions and query instructions, the control instructions are used to receive encrypted network packets sent by the host computer, and the query instructions are used to upload the status information of the corresponding airborne test equipment to the host computer.

[0011] According to an embodiment of this application, an intelligent control method for airborne test equipment is provided, wherein the status information corresponding to the query instruction is encoded using binary bit mapping; wherein, preset bits in a 16-bit bit sequence respectively represent: channel enable status, temperature status, packet rate threshold status, synchronization status, and time source abnormal status.

[0012] According to an embodiment of this application, an intelligent management and control method for airborne test equipment is provided. The method further includes: during a flight test, acquiring multiple execution threads for managing the multiple airborne test equipment, and determining the initial weight of each of the multiple execution threads; calculating the single-run time and resource consumption order of each of the multiple execution threads during the flight test; determining the real-time weight of each execution thread based on its initial weight, single-run time, and resource consumption order of magnitude; and allocating system resources to each of the multiple execution threads and executing them according to their real-time weights.

[0013] According to an embodiment of this application, an intelligent management and control method for airborne testing equipment is provided. The method further includes performing the following operations for each airborne testing equipment: using the main program to continuously monitor the running status of the subroutine through a thread watchdog, and detecting program fault information of the airborne testing equipment; if it is determined that the airborne testing equipment has a fault, restarting the subroutine by running a script; if the number of consecutive restart failures of the subroutine reaches a preset threshold, outputting fault warning information and triggering a watchdog hardware alarm.

[0014] This application embodiment also provides an intelligent control device for airborne testing equipment, including: The PCM acquisition module is used to acquire the status information of multiple airborne test devices within a preset time period. The data processing module is used to sample and extract features from the state information of each airborne test device to obtain initial physical quantities; map the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determine the current operating state of the airborne test device based on the positional relationship between the target feature parameters and a preset decision hyperplane.

[0015] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the intelligent control method for any of the airborne test devices described above.

[0016] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the intelligent control method for airborne test equipment as described above.

[0017] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the intelligent control method for any of the airborne test equipment described above.

[0018] The intelligent management method and device for airborne test equipment provided in this application acquires the status information of multiple airborne test equipment within a preset time period; for each airborne test equipment, the status information is sampled and features are extracted to obtain initial physical quantities; the initial physical quantities are mapped from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; the current operating state of the airborne test equipment is determined according to the positional relationship between the target feature parameters and a preset decision hyperplane. By automatically collecting the status information of multiple heterogeneous airborne test equipment in a one-stop manner, the tedious process of manually switching and checking each interface is significantly simplified, the pre-flight preparation time is greatly shortened, and the mission requirements of multiple remote simultaneous launches are effectively supported. In addition, by using high-dimensional mapping to construct nonlinear and interaction term features and combining them with a preset decision hyperplane to achieve automatic judgment, the current operating state of the airborne test equipment can be accurately captured, effectively avoiding flight mission failures caused by hidden faults. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the intelligent control method for airborne testing equipment provided in the embodiments of this application; Figure 2 This is a schematic diagram of the intelligent control system for airborne testing equipment provided in the embodiments of this application; Figure 3 This is a schematic diagram of a scenario for the intelligent control system of airborne testing equipment provided in the embodiments of this application; Figure 4a This is a schematic diagram of the design of the intelligent control device provided in the embodiments of this application; Figure 4b This is a schematic diagram of the main software interface of the intelligent control device provided in this application embodiment; Figure 4c This is a schematic diagram of the actual operation of the intelligent control device provided in the embodiments of this application; Figure 5a This is a schematic diagram of parameter division provided in the embodiments of this application; Figure 5b This is a schematic diagram of parameters in five-dimensional space provided in the embodiments of this application; Figure 5c This is a schematic diagram of parameters in three-dimensional space provided in the embodiments of this application; Figure 6 This is a schematic diagram of the structure of the intelligent control device for the airborne testing equipment provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] It should be noted that the execution subject involved in the embodiments of this application can be the intelligent control device of the airborne test equipment or an electronic device. Optionally, the electronic device may include: computer / laptop, mobile terminal, server, electronic assembly equipment and electrical production equipment, etc.

[0023] The following section uses intelligent control equipment as an example to elaborate on the intelligent control method for airborne testing equipment provided in this application embodiment: Figure 1 This is a flowchart illustrating the intelligent control method for airborne testing equipment provided in an embodiment of this application. Figure 1 As shown, the method includes the following steps 101-102.

[0024] Step 101: Within a preset time period, acquire the status information of each of the multiple airborne test devices.

[0025] The preset duration refers to the time period during which the intelligent control device performs a complete data acquisition task, such as 100ms.

[0026] Airborne testing equipment refers to hardware units installed on aircraft to perform tasks such as data acquisition, processing, recording, transmission, and power supply control.

[0027] Status information refers to a multi-dimensional data set that characterizes the physical state, resource usage, performance, and configuration parameters of airborne test equipment during operation.

[0028] Optionally, the multiple airborne test devices may include at least: airborne data acquisition unit, airborne data recorder, telemetry transmitter, programmable power supply, main control network switch, optoelectronic equipment, and airborne data processor, etc.

[0029] The system includes: an airborne data acquisition unit (AGU) for conditioning and digitizing sensor signals; an airborne data logger for non-volatile storage of test data; a telemetry transmitter for real-time wireless transmission of test data to the ground station; a programmable power supply for controlled power distribution to the test system; a main control network switch for data exchange between nodes within the test system; optoelectronic devices for payloads involving optical imaging or laser ranging; and an airborne data processor for complex algorithm calculations and system coordination control.

[0030] Optionally, each status information may include at least: voltage (such as bus voltage, module voltage), current (such as load current, quiescent current), frequency (such as operating frequency, channel center frequency), signal strength, packet forwarding rate, number of network packet losses, disk capacity (such as remaining storage space, disk write rate, storage sector health), central processing unit (CPU) usage overview (such as real-time utilization, core temperature, interrupt response time), internal temperature, humidity, vibration level of the equipment chassis, and synchronization status of external time source, etc.

[0031] For example, Figure 2 This is a schematic diagram of the intelligent control system for airborne testing equipment provided in an embodiment of this application. Figure 2 The intelligent control system includes intelligent control equipment, three airborne testing devices, and configuration management equipment.

[0032] The intelligent control equipment integrates a main control module, and power supply module, multi-port network module, Pulse Code Modulation (PCM) acquisition module, serial port expansion module, and storage module, all electrically connected to the main control module. It should be noted that the main control module, as the core computing unit, connects to the storage module via a mini-Serial Advanced Technology Attachment (mSATA) interface for local backup storage of control logs and test data. The power supply module connects to an external 28VDC power supply, providing power to the entire device, specifically converting the 28V input power into the 5V / 12V DC power required by each module. The multi-port network module primarily handles the reception of network data packets for the airborne test system. The PCM acquisition module primarily handles PCM data acquisition. The serial port expansion module primarily handles RS232 data acquisition.

[0033] The three airborne test devices are designated as the first, second, and third airborne test devices. These three devices connect to the intelligent control device via heterogeneous interfaces. Specifically: the first airborne test device connects to the multi-port network module via a gigabit / megabit Ethernet port; the second airborne test device connects to the PCM acquisition module via a PCM interface; and the third airborne test device connects to the serial port expansion module via a serial port.

[0034] The aforementioned configuration management device is connected to a multi-port module via a gigabit Ethernet port to enable remote control command issuance and status information monitoring of airborne test equipment.

[0035] For example, combined Figure 2 , Figure 3 This is a schematic diagram of a scenario for the intelligent control system of airborne testing equipment provided in an embodiment of this application. Figure 3 In China, the intelligent control system includes airborne and ground-based systems.

[0036] The airborne system includes airborne test equipment such as airborne data acquisition units, airborne data recorders, telemetry transmitters, programmable power supplies, main control network switches, optoelectronic equipment, and airborne data processors, as well as intelligent management and control equipment.

[0037] The ground-side system includes a host computer (i.e., ground configuration management software), antennas, a ground receiving system, and a ground monitoring system.

[0038] In the configuration and control link, the main control module in the intelligent management and control equipment communicates with the host computer in the ground system through a wired or wireless network interface to receive remote control commands or configuration parameters issued by the host computer.

[0039] In the wireless transmission link, the intelligent control equipment is electrically connected to the wireless transmission equipment, and wireless data transmission is carried out through the airborne antenna and the antenna in the ground system to realize the real-time uploading of the status information of each airborne test equipment during the flight test.

[0040] Ground processing link: The wireless signals received by the antennas in the ground system can be transmitted to the ground receiving system for demodulation and analysis, and finally summarized to the ground monitoring system for real-time display (such as parameter graphical display) and analysis.

[0041] For example, Figure 4a This is a schematic diagram of the design of the intelligent control device provided in the embodiments of this application; Figure 4b This is a schematic diagram of the main software interface of the intelligent control device provided in this application embodiment; Figure 4c This is a schematic diagram of the actual operation of the intelligent control device provided in the embodiments of this application. Figures 4a-4cAs can be seen from the above, in terms of hardware form, the intelligent control equipment adopts an integrated design that integrates a display screen, a ruggedized chassis, and various professional aviation interfaces (such as power supply, Ethernet, and signal transmission interfaces), which can adapt to the complex environment of airborne flight testing and support on-site visual interaction. In terms of interactive functions, its "central control unit" interface realizes the graphical centralized display of heterogeneous equipment information such as power distribution status, PCM telemetry, video monitoring, and recorder capacity, thereby providing users with one-stop real-time status perception and control capabilities.

[0042] In step 101, combined Figure 2 As shown in Figure 4, the intelligent control equipment acquires the status information of multiple airborne test devices through the PCM acquisition module within a preset time period. The entire process achieves real-time automatic capture of specific airborne data streams, which not only ensures the synchronous acquisition and highly reliable access of multi-source status information, but also significantly solves the time-consuming bottleneck caused by frequent interface switching and cumbersome operation procedures when manually inspecting PCM devices on-site from the physical interface layer.

[0043] It should be noted that since the status information of each airborne test device is continuously collected within a preset time period, this status information can be used for the status display and remote transmission of the corresponding airborne test device.

[0044] Optionally, after step 101, the method may further include: the intelligent control device classifying and storing the status information of each airborne test device.

[0045] Step 102: For each airborne test device, sample and extract features from the state information of the airborne test device to obtain initial physical quantities; map the initial physical quantities from two-dimensional space to target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; determine the current operating state of the airborne test device based on the positional relationship between the target feature parameters and the preset decision hyperplane.

[0046] The initial physical quantity refers to the numerical value with clear engineering units and physical meaning obtained after deframing and quantizing the state information (original code value) of the airborne test equipment.

[0047] The target feature parameters refer to the composite feature vectors generated in the target dimension space after the initial physical quantities are transformed by a preset mapping function (such as polynomial extension), which contain linear terms, nonlinear terms and interaction terms.

[0048] A pre-defined decision hyperplane is a mathematical boundary in a high-dimensional target space, pre-trained or pre-defined, used to delineate different operational states (such as "abnormal states" and "normal states"). The first side of the pre-defined decision hyperplane corresponds to the abnormal state, and the second side corresponds to the normal state.

[0049] In step 102, the intelligent control device can use a kernel function to map the parameter point set in the original feature space to a high-dimensional space, and can determine whether the parameters can be separated in the newly added dimension (e.g., Figure 4a (As shown). For example, many data points are linearly inseparable in low-dimensional space (such as the XOR problem), but may be linearly separable in high-dimensional space. Based on this, the intelligent control device can transform a set of points that cannot be linearly divided in low-dimensional space into a set of points in high-dimensional space, thus making it linearly separable. Then, combined with a preset decision hyperplane, it can determine the current operating state of the airborne test equipment. Specifically, for each of the multiple airborne test equipment, the following operations are performed: The intelligent control device first samples and extracts features from the state information of the airborne test equipment to obtain discrete initial physical quantities; then, the intelligent control device maps these initial physical quantities from two-dimensional space to a high-dimensional space (i.e., the target dimension space), constructs linearly separable target feature parameters, and then determines the current operating state of the airborne test equipment by determining the positional relationship between these target feature parameters and the preset decision hyperplane. Based on this, the intelligent control device can determine the current operating state of each of these multiple airborne test equipment.

[0050] The entire process uses high-dimensional mapping technology to transform complex nonlinear states that are originally difficult to judge into high-dimensional linearly separable features, eliminating the blind spot in judgment from a mathematical perspective, and greatly improving the accuracy and reliability of fault diagnosis for airborne multi-heterogeneous equipment under complex operating conditions.

[0051] It should be noted that directly calculating the inner product of parameters in high-dimensional space is very complex. Kernel functions avoid explicitly calculating high-dimensional mappings and directly calculate equivalent inner products in low-dimensional space. In other words, this kernel function can solve the dimensionality explosion problem by directly mapping discrete points to high-dimensional space through inner product operations in low-dimensional space, and realize trend prediction of two-dimensional discrete points through high-dimensional space.

[0052] Understandably, under multiple consecutive preset durations, the intelligent control device can obtain the current operating status of each airborne test device for each preset duration. Based on this, for each airborne test device, the intelligent control device can determine the current operating status of the airborne test device for each of the multiple consecutive preset durations, thereby enabling the prediction of the operating status of the airborne test device.

[0053] Optionally, the intelligent control equipment samples and extracts features from the status information of the airborne test equipment to obtain initial physical quantities. This may include: the intelligent control equipment sampling the status information of the airborne test equipment according to the calibration file to obtain binary code values; the intelligent control equipment then performs frame de-framing and feature extraction on these code values ​​to obtain initial physical quantities that reflect the true physical state of the equipment. The entire process achieves automated mapping of airborne multi-source heterogeneous data from the underlying bitstream to standardized physical indicators, providing an accurate and unified data benchmark for subsequent high-dimensional feature extraction.

[0054] Among them, the calibration file refers to the configuration file that stores the configuration information, data mapping relationship and engineering quantity conversion coefficient of each acquisition channel of the airborne test equipment.

[0055] In some embodiments, the intelligent control device maps initial physical quantities from a two-dimensional space to a target dimension space and constructs target feature parameters. This may include: the intelligent control device extracting two first sub-parameters in the two-dimensional space from the initial physical quantities; the intelligent control device constructing five second sub-parameters in a five-dimensional space based on the two first sub-parameters through polynomial feature expansion; the intelligent control device determining the second sub-parameter in the target dimension space from the five second sub-parameters and using it as the target parameter; wherein the dimension of the target dimension space is greater than 2 and less than or equal to 5; and the intelligent control device using multiple target parameters as target feature parameters.

[0056] In this embodiment, the intelligent control device first extracts two relevant first sub-parameters in two-dimensional space from the initial physical quantities (e.g., output voltage and load current for a programmable power supply). Then, it increases the observation dimension of these two first sub-parameters. Specifically, it constructs five second sub-parameters in five-dimensional space through polynomial feature expansion (e.g., ...). Figure 4b As shown), it should be noted that although five-dimensional features are generated, the intelligent control device does not blindly use them all, but selects them according to actual needs. That is, assuming the target dimension space is three-dimensional space, three second sub-parameters can be determined from these five second sub-parameters (such as...). Figure 4c As shown in the diagram, this approach ensures the accuracy of the judgment (dimension greater than 2) while avoiding the curse of dimensionality by eliminating irrelevant interference terms, effectively saving limited airborne computing resources. Next, the intelligent control device uses these three second sub-parameters as a set of target parameters. Based on this, the intelligent control device acquires multiple sets of target parameters and uses them as target parameters. The entire process, by extending the initial physical quantities with polynomial features to construct a high-dimensional feature space containing nonlinear and interaction terms, achieves accurate judgment of the complex evolution trends and coupling risks of airborne testing equipment, significantly improving the intelligent control device's early identification capability for latent faults and the efficiency of automated control.

[0057] Optionally, the two first sub-parameters in two-dimensional space should satisfy the following polynomial: a1X1+a2(X1) 2 +a3X2+a4(X2) 2 +a5X1X2+a6=0.

[0058] Where a1 and a3 represent linear term coefficients; a2 and a4 represent nonlinear term coefficients, i.e. quadratic term coefficients; a5 represents interaction term coefficients, characterizing the coupling and correlation effects between two different first sub-parameters (e.g., the complex risks when voltage drop and current rise occur simultaneously); a6 represents constant term or bias term / intercept, which determines the basic position of the preset decision hyperplane in the feature space and is used to adjust the overall tightness of the judgment criteria (threshold benchmark).

[0059] In some embodiments, the two first sub-parameters are first sub-parameter X1 and first sub-parameter X2, respectively; the five second sub-parameters are second sub-parameter Z1, second sub-parameter Z2, second sub-parameter Z3, second sub-parameter Z4 and second sub-parameter Z5, respectively.

[0060] Wherein, the second sub-parameter Z1 is the first sub-parameter X1, that is, Z1=X1; The second sub-parameter Z2 is the square of the first sub-parameter X1, that is, Z2 = (X1). 2 ; The second sub-parameter Z3 is the same as the first sub-parameter X2, that is, Z3 = X2; The second sub-parameter Z4 is the square of the first sub-parameter X2, that is, Z4 = (X2). 2 ; The second sub-parameter Z5 is the product of the first sub-parameter X1 and the first sub-parameter X2, i.e., Z5 = X1X2.

[0061] In some embodiments, the intelligent control device determines the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and the preset decision hyperplane, which may include one of the following implementation methods: Implementation Method 1: Among multiple target parameters, if there is a target parameter located on the first side of the preset decision hyperplane, the intelligent control device will take the abnormal state as the current operating state of the airborne test equipment; if there is no target parameter located on the first side of the preset decision hyperplane, the intelligent control device will take the normal state as the current operating state.

[0062] In implementation method 1, based on extreme value sensitive logic, multiple target parameters are analyzed. Specifically, the intelligent control device determines whether any of the target parameters are located on the first side of the preset decision hyperplane. If so, it indicates that at least one component of the airborne test equipment's multiple dimensional features has touched or crossed the safety boundary, posing a transient or sudden failure risk. In this case, the intelligent control device takes the abnormal state as the current operating state of the airborne test equipment. If not, it indicates that all target feature parameters mapped to the high-dimensional space have completely fallen within the preset safety decision space, and the overall performance of the airborne equipment is within a steady-state control range. In this case, the intelligent control device takes the normal state as the current operating state of the airborne test equipment. The entire process utilizes a veto-based sensitive discrimination mechanism, effectively solving the drawback of the traditional weighted average method, which easily masks individual peak failures. This significantly improves the sensitivity and response speed of the intelligent control device in capturing transient hidden risks of highly critical airborne test equipment.

[0063] Implementation Method 2: The intelligent control device counts the number of first target parameters located on the first side of the preset decision hyperplane and the number of second target parameters located on the second side of the preset decision hyperplane from multiple target parameters. If the number of first target parameters is greater than the number of second target parameters, the intelligent control device takes the abnormal state as the current operating state. If the number of first target parameters is less than the number of second target parameters, the intelligent control device takes the normal state as the current operating state. If the number of first target parameters is equal to the number of second target parameters, the intelligent control device takes the fault critical state as the current operating state.

[0064] In implementation method 2, based on majority voting logic, multiple target parameters are analyzed. Specifically, the intelligent control device determines the position of the multiple target parameters on a preset decision hyperplane and counts the number of first target parameters located on the first side of the preset decision hyperplane and the number of second target parameters located on the second side of the preset decision hyperplane. Then, the intelligent control device compares the number of first target parameters and the number of second target parameters and determines the current operating status of the airborne test equipment based on the comparison result. Specifically, if the number of first target parameters is greater than the number of second target parameters, it indicates that the abnormal characteristics exhibited by the airborne test equipment during the observation period (i.e., the preset duration) are dominant, and the airborne test equipment... If a systematic shift in the standby state has occurred, the intelligent control device will take the abnormal state as the current operating state. If the number of the first target parameters is less than the number of the second target parameters, it indicates that although there are individual isolated abnormal feature components, the overall operating trend of the airborne test equipment is still within the safety envelope, and individual outliers can be identified as transient noise interference. In this case, the intelligent control device will take the normal state as the current operating state. If the number of the first target parameters is equal to the number of the second target parameters, it indicates that the airborne test equipment is at a performance degradation equilibrium point evolving from normal to abnormal, that is, the health margin of the airborne test equipment has dropped to the critical range. In this case, the intelligent control device will take the fault critical state as the current operating state. The entire process effectively filters out sporadic sampling noise interference in the complex airborne environment through majority voting logic. On the basis of significantly enhancing the robustness of the judgment results, it realizes the forward-looking early warning of the performance degradation of the airborne test equipment by using the identification of the fault critical state.

[0065] Implementation Method 3: The intelligent control device determines the average value of multiple target parameters; if the average value is located on the first side of the preset decision hyperplane, the intelligent control device will take the abnormal state as the current operating state of the airborne test equipment; if the average value is located on the second side of the preset decision hyperplane, the intelligent control device will take the normal state as the current operating state.

[0066] Among them, the mean values ​​of multiple target parameters are used to characterize the overall distribution center and macroscopic operating trend of the airborne test equipment's operating status within a specific observation period.

[0067] In implementation method 3, based on mean-center logic, multiple target parameters are analyzed. Specifically, the intelligent control device first calculates the mean of these multiple target parameters and determines the positional relationship between the mean and the preset decision hyperplane. Specifically, if the mean is located on the first side of the preset decision hyperplane, it indicates that the overall performance center of gravity of the airborne test equipment has undergone a systematic shift, and the steady-state operating trajectory of the airborne test equipment has entered the abnormal region. In this case, the intelligent control device takes the abnormal state as the current operating state of the airborne test equipment. If the mean is located on the second side of the preset decision hyperplane, it indicates that although there may be individual instantaneous fluctuations in characteristic points, the overall operating state of the airborne test equipment remains within the safety envelope, meeting the requirements of steady-state operation. The intelligent control device takes the normal state as the current operating state. The entire process effectively filters out discrimination jitter caused by high-frequency random noise through trend discrimination of the feature center, achieving stable evaluation of the long-term operating state of the airborne test equipment and greatly reducing the system false alarm rate under complex operating conditions.

[0068] Understandably, the three implementation methods described above can be flexibly configured according to the mission criticality and signal characteristics of different airborne test equipment. For example, for programmable power supplies with higher safety levels, implementation method 1 can be prioritized to ensure real-time early warning; while for PCM acquisition data that is more susceptible to electromagnetic interference, implementation method 2 or 3 can be used to improve the accuracy of the judgment. Through this hierarchical and categorized judgment strategy, the adaptability of intelligent control equipment in complex flight environments is further enhanced.

[0069] In some embodiments, after step 102, the method may further include at least one of the following implementations: Implementation Method 1: Upon receiving encrypted network packets from the host computer, the intelligent control device parses the remote control commands carried in the encrypted network packets; the intelligent control device parses the remote control commands according to the communication protocol to obtain control information containing device identifier and control value fields; the intelligent control device controls the power supply and working status of the airborne test equipment to be controlled according to the control information.

[0070] Among them, the communication protocol, also known as the remote control protocol, is a custom network protocol that can encode the status information of each airborne test device into the corresponding data packet.

[0071] The device identifier (ID) is used to identify the airborne test device to be controlled from multiple airborne test devices. The control value fields include: transmission mode instructions for switching between Pulse Code Modulation (PCM) and network modes, modulation mode instructions for switching between Frequency Modulation (FM) and Shaped Offset Quadrature Phase Shift Keying (SOQPSK) modes, and frequency point configuration instructions.

[0072] In implementation method 1, the intelligent control device receives data (i.e., encrypted network packets) from the host computer via a network interface (such as a gigabit Ethernet port) or a wireless transmission link, decrypts and verifies the encrypted network packets to obtain remote control commands. Then, the intelligent control device parses the remote control commands according to the preset Type-Length-Value (TLV) format in the communication protocol to obtain control information containing device identifier and control value fields. Finally, the main control module in the intelligent control device sends the control information to the airborne test device to be controlled corresponding to the device identifier. This control information is used to control the power supply and operating status of the airborne test device to be controlled corresponding to the device identifier. The entire process, through the command distribution mechanism, realizes secure access and precise control of airborne multi-source heterogeneous devices from the ground side, significantly improving the real-time performance of system state switching and the determinism of mission response during flight testing.

[0073] It should be noted that different airborne test equipment corresponds to different communication protocols. All communication protocols are written into the intelligent control device, enabling the intelligent control device to support multiple airborne test equipment communication protocols at the same time, thus solving the compatibility problem of multiple types of airborne test equipment in complex systems.

[0074] In other words, the intelligent control equipment processes and forwards the status information of the airborne test equipment through wireless communication protocols, and sends the status information to the ground receiving station (i.e., the aforementioned host computer). The entire process enables unified interface management of various types of airborne test equipment, accurate and efficient management and forwarding of the status information of the airborne test equipment, and allows for remote network checks of the onboard equipment status. This enables ground personnel to conduct real-time and efficient pre-flight equipment status checks and control, improving the work efficiency of test personnel and enabling accurate fault diagnosis. It is suitable for multi-tasking on-site flight test management.

[0075] Optionally, during the verification process of encrypted network packets, based on known protocol verification algorithms (such as Cyclic Redundancy Check (CRC)), the verification algorithm is used to calculate the encrypted network packet's validity against multiple communication protocols. The check value calculated by the communication protocol .

[0076] Among them, the check value The calculation formula is: modM.

[0077] in, Indicates the first The data content in each data packet (or byte). The counting index represents the cumulative operation, which increases cyclically from 1 to... M represents the modulus specified in the communication protocol, used for the final modulo operation; Indicates the length of the data packet. This represents the sequence number or index of the communication protocol currently being used among multiple communication protocols.

[0078] Optionally, after implementing method 1, the method may further include: the intelligent control device encapsulating the communication protocols of each airborne test device according to a preset format.

[0079] The communication protocol includes: frame header, data payload, and frame trailer; The frame header includes the aircraft number (PlaneCode); The data payload includes the test system configuration SysConfig and the subsystem type SubSysType; The communication protocol carries two types of instructions: control instructions and query instructions. Control instructions are used to receive encrypted network packets sent by the host computer, while query instructions are used to upload the status information of the corresponding airborne test equipment to the host computer.

[0080] The PlaneCode refers to the numerical or character code used to uniquely identify the specific aircraft entity participating in the flight test.

[0081] The test system configuration SysConfig refers to the combined logical state of airborne test hardware and software defined for a specific flight course / mission.

[0082] Subsystem type refers to the specialized classification and identification of the functional modules within each airborne test equipment.

[0083] For example, Table 1 is a data table of the communication protocol provided in the embodiments of this application.

[0084] Table 1: Table 1 shows the data definitions, data lengths, data types, and data descriptions for the frame header, data payload, and frame trailer, respectively.

[0085] In addition, the frame header also includes the synchronization word Sync, the version number Version, the instruction type CmdType, the sequence number SequenceNum, and the timestamp TimeStamp.

[0086] The data payload also includes payload data encapsulated in TLV format.

[0087] The frame tail includes a 16-bit Cyclic Redundancy Check (CRC16) checksum for data integrity verification.

[0088] The Remain extension is reserved.

[0089] Optionally, the status information corresponding to the above query command is encoded using binary bit mapping.

[0090] The preset bits in the 16-bit bit sequence represent the following: channel enable status, temperature status (i.e., whether the temperature is too low / too high), packet rate threshold status (i.e., whether the packet rate is below the threshold), synchronization status (i.e., whether the synchronization is normal), and time source abnormal status (i.e., whether there is an external time source and whether the external time source is abnormal).

[0091] It should be noted that the status information of each airborne test device is encrypted using binary, with each bit or multiple bits representing a different status word. Table 2 shows a query command for a certain airborne test device, which carries status information. Specifically, preset bits in a 16-bit bit sequence represent various types of status information.

[0092] Table 2: Table 3 shows the control commands for a certain airborne test equipment. The control target (i.e., the airborne test equipment to be controlled) is confirmed by parsing the equipment ID. At the same time, control information is sent to the airborne test equipment to be controlled to change the equipment status.

[0093] Table 3: As shown in Table 3, the data structure of the control command consists of three dimensions: the composition of the control command, the function of its fields, and its data specifications. Specifically, the control command is a structured message with a total length of 2 + 1 + 1 + 4 = 8 bytes.

[0094] The control command consists of four core fields, arranged in order: Device ID, occupying the first 2 bytes, used to accurately locate the specific airborne test device to be controlled in a complex airborne network; Control value field 1 (transmission mode): occupying 1 byte, responsible for switching logical links; Control value field 2 (modulation mode): occupying 1 byte, responsible for switching physical waveforms; Control value field 3 (frequency point): occupying 4 bytes, responsible for configuring specific physical parameters.

[0095] In addition, regarding transmission mode switching: it supports switching between PCM (0x00) mode and network (0x01) mode, corresponding to... Figure 2 and Figure 3 The system simultaneously contains a PCM acquisition module and a multi-port network module; Regarding modulation mode switching: It supports switching between two modulation schemes, FM (0x00) and SOQPSK (0x01), which is a core configuration parameter for RF equipment such as telemetry transmitters; For precise frequency configuration: Use a 4-byte high-precision field to set the frequency point, which can cover a wide frequency range and ensure accurate parameter delivery.

[0096] For example, when the device ID is 0x0111, the control command sets the target airborne test device to "network transmission mode" and "uses SOQPSK modulation". This fixed-length and clearly defined field structure greatly reduces the parsing pressure on the airborne embedded terminal (main control module) and ensures the real-time performance of the control actions.

[0097] Implementation Method 2: During flight testing, the intelligent control equipment acquires and manages multiple execution threads of multiple airborne test equipment and determines the initial weight of each execution thread. The intelligent control equipment then calculates the single-run time and resource consumption of each execution thread during the flight test. For each execution thread, the intelligent control equipment determines the real-time weight based on the initial weight, single-run time, and resource consumption. Based on the real-time weights of each execution thread, the intelligent control equipment allocates system resources to each execution thread and executes them.

[0098] The initial weight is used to characterize the business importance or task criticality level preset by the intelligent management and control device in a static environment for the execution thread.

[0099] The single execution time refers to the actual time consumed by the executing thread from receiving the call instruction to completing a single business processing logic (such as a data parsing or a dimensionality increase calculation) and releasing CPU usage.

[0100] The order of magnitude of resource usage refers to the quantified level of onboard hardware resources (such as CPU utilization, memory space usage, and bus bandwidth load) occupied by an executing thread during a single run.

[0101] Real-time weights are used to characterize the actual execution priority index in the dynamic environment of flight testing, taking into account the original mission priority, operational efficiency requirements, and system resource consumption.

[0102] In implementation method 2, each airborne test device runs at least one execution thread during flight testing. Each execution thread has its own response time. If the execution threads are allowed to run haphazardly, they will become sluggish due to resource contention. Therefore, optimized resource scheduling is needed to ensure smooth operation of all execution threads. Specifically: During flight testing, the intelligent control device first acquires multiple execution threads running on multiple airborne test devices. Then, based on the preset priorities of each execution thread, the intelligent control device assigns an initial weight α0 to each thread. Simultaneously, the intelligent control device calculates the single-run time T1 and resource consumption order of magnitude T2 for each execution thread during flight testing. Then, for each execution thread, the real-time weight is determined using the weight calculation formula α1=(α0+T1) / T2, where α1 represents the real-time weight. Based on this, the intelligent control device can ultimately determine the real-time weight of each execution thread. Since real-time weight is inversely proportional to thread priority, a smaller real-time weight corresponds to a higher thread priority. In this case, the intelligent control device can sort the real-time weights of these multiple execution threads, allocate system resources to each thread in ascending order, and execute the threads in descending order of priority. This process not only ensures the reliable execution of critical tasks but also automatically optimizes the resource allocation order among the execution threads by monitoring real-time operational data. This ensures that even in complex environments with multiple heterogeneous airborne test devices operating concurrently, the intelligent control device can still allocate resources reasonably and possess extremely high smoothness and deterministic response capabilities.

[0103] Implementation Method 3: Perform the following operations for each airborne test device: The intelligent control device uses the main program to continuously monitor the running status of the subroutines through a thread watchdog and detects program fault information of the airborne test devices. If a fault is found in the airborne test device, the subroutines are restarted by running a script. If the number of consecutive restart failures of the subroutines reaches a preset threshold, the intelligent control device outputs fault warning information and triggers a watchdog hardware alarm.

[0104] The main program refers to the parent program running in the main control module of the intelligent control device. It is responsible for initializing the system environment, allocating task resources, distributing remote control commands, and coordinating the management of all subroutines that perform specific tasks.

[0105] A thread watchdog is a software-level monitoring logic or heartbeat monitoring mechanism that runs inside the main program. Specifically, it determines whether a task is stuck, crashed, or trapped in an infinite loop by periodically querying the heartbeat signals or status flags of each subroutine, thereby determining whether the onboard test equipment is faulty.

[0106] A subroutine is an independent execution unit controlled by the main program and responsible for executing specific business logic. Examples include programs that perform PCM data parsing, programs that perform high-dimensional space mapping algorithms, protocol programs that process remote control commands, and programs that execute disk write records.

[0107] A running script refers to a sequence of automated execution commands pre-stored in the storage module of an intelligent control device.

[0108] Fault warning information is used to report data messages or electrical signals of the current unrecoverable abnormal state of airborne test equipment.

[0109] Watchdog hardware refers to a physical electronic circuit or chip module that operates independently of the intelligent control device and has the ability to automatically trigger the intelligent control device to reset. Optionally, the watchdog hardware may include at least: a watchdog lamp, a dedicated external watchdog chip (such as a monitoring circuit like MAX706), and an intelligent power distribution unit with power cycle management function.

[0110] In implementation method 3, the following fault self-detection operations are performed on each airborne test device: The intelligent control device uses a main program to continuously feed a watchdog timer via a thread to monitor the main program's running status. This main program also uses the watchdog timer to continuously monitor the running status of subroutines, ensuring that the subroutines remain in a normal state. Simultaneously, the main program detects program fault information from the airborne test devices. If a fault is detected in the airborne test device, it indicates that the subroutine is in an abnormal state. In this case, a warning is not immediately triggered; instead, a script is run to attempt to restart the subroutine. This addresses the issues caused by airborne electromagnetic interference and occasional... The temporary program crash caused by an occasional memory overflow greatly improves the system's robustness. Next, the intelligent control device counts the number of consecutive failed restarts of the subroutine. If the number of consecutive failed restarts reaches a preset threshold, it indicates hardware damage and / or serious logical errors in the airborne test equipment. The intelligent control device outputs a fault warning (e.g., uploading the warning to the host computer and displaying it on the host computer's screen) and triggers a watchdog hardware alarm (e.g., the watchdog light illuminates red) to prevent the fault from spreading and to facilitate further investigation and troubleshooting. The entire process, through main-subroutine decoupling and a dual hardware and software watchdog design, ensures that any anomalies (such as main program anomalies, subroutine anomalies, or airborne test equipment anomalies) during flight testing can be detected immediately. This not only solves the problem of equipment failure due to the complexity of the airborne environment but also ensures the continuity of status information recording and monitoring through an automatic script restart mechanism, minimizing flight mission failures caused by occasional software failures.

[0111] Optionally, if a fault is determined in the airborne test equipment, the method may further include: the intelligent control device recording the fault information of the airborne test equipment and writing the fault information to a log file. The entire process provides detailed data evidence for accurate fault replay and equipment health assessment after flight testing, significantly improving system maintainability and mission safety under complex testing environments.

[0112] In the embodiments of this application, the technical solutions described in steps 101-102 above significantly simplify the tedious process of manually switching and checking each interface one by one by automatically collecting the status information of multiple heterogeneous airborne test equipment in one stop, greatly shorten the pre-flight preparation time, and effectively support the mission requirements of multiple remote simultaneous launches. In addition, by using high-dimensional mapping to construct nonlinear and interactive features, and combining them with a preset decision hyperplane to achieve automatic judgment, the current operating status of airborne test equipment can be accurately captured, effectively avoiding flight mission failures caused by latent faults.

[0113] Furthermore, the aforementioned technical solution enables remote management and control of airborne test equipment status, and combines deep learning algorithms to detect parameter status in real time. Compared to previous manual equipment inspections, this solution can remotely manage multiple aircraft (i.e., multiple airborne test devices) simultaneously, reducing labor costs and improving the efficiency of test personnel. With the support of deep learning algorithms, the workload of manual configuration is reduced, laying a technological foundation for future intelligent testing.

[0114] The intelligent management and control device for airborne test equipment provided in the embodiments of this application will be described below. The intelligent management and control device for airborne test equipment described below can be referred to in correspondence with the intelligent management and control method for airborne test equipment described above.

[0115] Figure 6 This is a schematic diagram of the intelligent control device for the airborne testing equipment provided in this application embodiment. (See attached diagram.) Figure 6 As shown, the device includes a PCM acquisition module 601 and a data processing module 602.

[0116] PCM acquisition module 601 is used to acquire the status information of multiple airborne test devices within a preset time period. The data processing module 602 is used to sample and extract features from the state information of each airborne test device to obtain initial physical quantities; map the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determine the current operating state of the airborne test device based on the positional relationship between the target feature parameters and the preset decision hyperplane.

[0117] Optionally, the data processing module 602 is specifically used to extract two first sub-parameters in the two-dimensional space from the initial physical quantity; construct five second sub-parameters in the five-dimensional space based on the two first sub-parameters through polynomial feature expansion; determine the second sub-parameter in the target dimension space from the five second sub-parameters and use it as the target parameter; wherein the dimension of the target dimension space is greater than 2 and less than or equal to 5; and use multiple target parameters as the target feature parameters.

[0118] Optionally, the two first sub-parameters are first sub-parameter X1 and first sub-parameter X2; the five second sub-parameters are second sub-parameter Z1, second sub-parameter Z2, second sub-parameter Z3, second sub-parameter Z4 and second sub-parameter Z5; wherein, the second sub-parameter Z1 is the first sub-parameter X1; the second sub-parameter Z2 is the square of the first sub-parameter X1; the second sub-parameter Z3 is the first sub-parameter X2; the second sub-parameter Z4 is the square of the first sub-parameter X2; and the second sub-parameter Z5 is the product of the first sub-parameter X1 and the first sub-parameter X2.

[0119] Optionally, the data processing module 602 is specifically configured to: among the multiple target parameters, if there is a target parameter located on the first side of the preset decision hyperplane, then take the abnormal state as the current operating state of the airborne test equipment; if there is no target parameter located on the first side of the preset decision hyperplane, then take the normal state as the current operating state; or, count the number of first target parameters located on the first side of the preset decision hyperplane and the number of second target parameters located on the second side of the preset decision hyperplane from the multiple target parameters; if the number of first target parameters is greater than the number of second target parameters, then take the abnormal state as the current operating state; if the number of first target parameters is less than the number of second target parameters, then take the normal state as the current operating state; if the number of first target parameters is equal to the number of second target parameters, then take the fault critical state as the current operating state; or, determine the average value of the multiple target parameters; if the average value is located on the first side of the preset decision hyperplane, then take the abnormal state as the current operating state of the airborne test equipment; if the average value is located on the second side of the preset decision hyperplane, then take the normal state as the current operating state.

[0120] Optionally, the data processing module 602 is further configured to, upon receiving an encrypted network packet from a host computer, parse the remote control command carried in the encrypted network packet; parse the remote control command according to the communication protocol to obtain control information including a device identifier and a control value field; wherein, the device identifier is used to identify the airborne test device to be controlled from among the plurality of airborne test devices, and the control value field includes: a transmission mode command for switching between Pulse Code Modulation (PCM) and network mode, a modulation mode command for switching between Frequency Modulation (FM) and Shaped Offset Quadrature Phase Shift Keying (SOQPSK) mode, and a frequency point configuration command; and control the power supply and operating status of the airborne test device to be controlled according to the control information.

[0121] Optionally, the data processing module 602 is further configured to encapsulate the communication protocol of each airborne test device according to a preset format; wherein, the communication protocol includes: a frame header, a data payload, and a frame trailer; the frame header includes the aircraft number PlaneCode; the data payload includes the test system configuration SysConfig and the subsystem type SubSysType; the communication protocol carries two types of instructions, namely control instructions and query instructions, the control instructions are used to receive encrypted network packets sent by the host computer, and the query instructions are used to upload the status information of the corresponding airborne test device to the host computer.

[0122] Optionally, the status information corresponding to the query instruction is encoded using binary bit mapping; wherein, preset bits in the 16-bit bit sequence respectively represent: channel enable status, temperature status, packet rate threshold status, synchronization status, and time source abnormal status.

[0123] Optionally, the data processing module 602 is further configured to, during the flight test, acquire and control multiple execution threads of the multiple airborne test devices, and determine the initial weight of each of the multiple execution threads; calculate the single run time and resource consumption order of each of the multiple execution threads during the flight test; determine the real-time weight of each execution thread based on its initial weight, single run time, and resource consumption order of order; and allocate system resources to each of the multiple execution threads and execute them according to their real-time weights.

[0124] Optionally, the data processing module 602 is also used to perform the following operations for each airborne test device: continuously monitor the running status of the subroutine through the thread watchdog using the main program, and detect the program fault information of the airborne test device; if it is determined that the airborne test device has a fault, restart the subroutine by running a script; if the number of consecutive restart failures of the subroutine reaches a preset threshold, output fault warning information and trigger the watchdog hardware alarm.

[0125] Figure 7 This is a schematic diagram of the structure of the electronic device provided in an embodiment of this application. For example... Figure 7 As shown, the electronic device may include: a processor 710, a communications interface 720, a memory 730, and a communication bus 740, wherein the processor 710, communications interface 720, and memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute an intelligent control method for airborne test equipment. This method includes: acquiring the status information of multiple airborne test equipment; sampling and extracting features from the status information of each airborne test equipment to obtain initial physical quantities; mapping the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determining the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and a preset decision hyperplane.

[0126] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the intelligent control method for airborne test equipment provided by the above methods. The method includes: acquiring the state information of multiple airborne test equipment; sampling and extracting features from the state information of each airborne test equipment to obtain initial physical quantities; mapping the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determining the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and a preset decision hyperplane.

[0128] In another aspect, embodiments of this application also provide a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements an intelligent control method for airborne test equipment provided by the methods described above. The method includes: acquiring state information of multiple airborne test equipment; sampling and extracting features from the state information of each airborne test equipment to obtain initial physical quantities; mapping the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determining the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and a preset decision hyperplane.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent control of airborne testing equipment, characterized in that, include: Within a preset time period, acquire the status information of each of the multiple airborne test devices; For each airborne test device, the state information of the airborne test device is sampled and features are extracted to obtain initial physical quantities; the initial physical quantities are mapped from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; the current operating state of the airborne test device is determined according to the positional relationship between the target feature parameters and a preset decision hyperplane.

2. The intelligent control method for airborne testing equipment according to claim 1, characterized in that, The step of mapping the initial physical quantity from two-dimensional space to the target dimensional space and constructing target feature parameters includes: From the initial physical quantities, extract two first sub-parameters in the two-dimensional space; Based on the two first sub-parameters, five second sub-parameters in five-dimensional space are constructed through polynomial feature extension; The second sub-parameter in the target dimension space is determined from the five second sub-parameters and used as the target parameter; wherein the dimension of the target dimension space is greater than 2 and less than or equal to 5; Multiple target parameters are used as target feature parameters.

3. The intelligent control method for airborne testing equipment according to claim 2, characterized in that, The two first sub-parameters are first sub-parameter X1 and first sub-parameter X2, respectively. The five second sub-parameters are Z1, Z2, Z3, Z4, and Z5, respectively. Wherein, the second sub-parameter Z1 is the first sub-parameter X1; The second sub-parameter Z2 is the square of the first sub-parameter X1; The second sub-parameter Z3 is the same as the first sub-parameter X2; The second sub-parameter Z4 is the square of the first sub-parameter X2; The second sub-parameter Z5 is the product of the first sub-parameter X1 and the first sub-parameter X2.

4. The intelligent control method for airborne testing equipment according to claim 2 or 3, characterized in that, Determining the current operating state of the airborne test equipment based on the positional relationship between the target feature parameters and the preset decision hyperplane includes: If, among the multiple target parameters, there exists a target parameter located on the first side of the preset decision hyperplane, then the abnormal state is taken as the current operating state of the airborne test equipment; if there is no target parameter located on the first side of the preset decision hyperplane, then the normal state is taken as the current operating state; or... From the plurality of target parameters, count the number of first target parameters located on the first side of the preset decision hyperplane and the number of second target parameters located on the second side of the preset decision hyperplane; if the number of first target parameters is greater than the number of second target parameters, then the abnormal state is taken as the current operating state; if the number of first target parameters is less than the number of second target parameters, then the normal state is taken as the current operating state; if the number of first target parameters is equal to the number of second target parameters, then the fault critical state is taken as the current operating state; or, The mean of multiple target parameters is determined; if the mean is located on the first side of the preset decision hyperplane, the abnormal state is taken as the current operating state of the airborne test equipment; if the mean is located on the second side of the preset decision hyperplane, the normal state is taken as the current operating state.

5. The intelligent control method for airborne testing equipment according to any one of claims 1-3, characterized in that, The method further includes: Upon receiving an encrypted network packet from the host computer, the remote control command carried in the encrypted network packet is parsed. The remote control command is parsed according to the communication protocol to obtain control information containing device identifier and control value fields; wherein, the device identifier is used to identify the airborne test device to be controlled from the plurality of airborne test devices, and the control value field includes: a transmission mode command for switching pulse code modulation (PCM) or network mode, a modulation mode command for switching frequency modulation (FM) or shaping offset quadrature phase shift keying (SOQPSK) mode, and a frequency point configuration command; Based on the control information, the power supply and operating status of the airborne test equipment to be controlled are controlled.

6. The intelligent control method for airborne testing equipment according to claim 5, characterized in that, The method further includes: The communication protocols of each airborne test device are encapsulated according to a preset format; The communication protocol includes: a frame header, a data payload, and a frame trailer; The frame header includes the aircraft number PlaneCode; The data payload includes the test system configuration SysConfig and the subsystem type SubSysType; The communication protocol carries two types of instructions: a control instruction and a query instruction. The control instruction is used to receive encrypted network packets sent by the host computer, and the query instruction is used to upload the status information of the corresponding airborne test equipment to the host computer.

7. The intelligent control method for airborne testing equipment according to claim 6, characterized in that, The status information corresponding to the query command is encoded using binary bit mapping. Among them, the preset bits in the 16-bit bit sequence represent: channel enable status, temperature status, packet rate threshold status, synchronization status, and time source abnormal status.

8. The intelligent control method for airborne testing equipment according to any one of claims 1-3, characterized in that, The method further includes: During the flight test, multiple execution threads that control the multiple airborne test devices are acquired, and the initial weights of each of the multiple execution threads are determined. The execution time and resource consumption of each of the multiple execution threads during the flight test were statistically analyzed. For each execution thread, the real-time weight of the execution thread is determined based on the initial weight, single execution time, and order of magnitude of resources consumed. Based on the real-time weights of the multiple execution threads, system resources are allocated to each of the multiple execution threads and they are then executed.

9. The intelligent control method for airborne testing equipment according to any one of claims 1-3, characterized in that, The method further includes: The following operations are performed on each of the aforementioned airborne test devices: The main program continuously monitors the running status of the subroutine through a thread watchdog and detects program fault information of the airborne test equipment. If the airborne test equipment is found to be faulty, the subroutine is restarted by running a script. If the number of consecutive failed restarts of the subroutine reaches a preset threshold, a fault warning message will be output and a watchdog hardware alarm will be triggered.

10. An intelligent control device for airborne testing equipment, characterized in that, include: The PCM acquisition module is used to acquire the status information of multiple airborne test devices within a preset time period. The data processing module is used to sample and extract features from the state information of each airborne test device to obtain initial physical quantities; map the initial physical quantities from a two-dimensional space to a target dimension space to construct target feature parameters, wherein the dimension of the target dimension space is higher than the dimension of the two-dimensional space, and the target feature parameters include at least nonlinear terms and / or interaction terms constructed from the initial physical quantities; and determine the current operating state of the airborne test device based on the positional relationship between the target feature parameters and a preset decision hyperplane.

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