Aircraft remote control delay compensation response method and system

Abstract

The invention discloses an aircraft remote control delay compensation response method and system, and the method comprises the steps: obtaining airborne data, and constructing a multi-source data set; calculating bidirectional transmission delay based on a bidirectional delay estimation algorithm, and calculating initial total delay; the initial total delay is dynamically calibrated by using a self-adaptive delay calibration algorithm, the confidence coefficient is evaluated, and the delay precision is improved; deducing a flight state in a delay time period by adopting a delay deduction hybrid model, and dynamically adjusting parameters along with load; according to the deduction result and the original instruction, generating a compensation instruction containing constraints by using a model prediction control algorithm; and executing the instruction after onboard verification, comparing the actual state with the expected state, and feeding back a deviation value to optimize the weight of the next-round model so as to form a closed-loop lifting mechanism. According to the method, the problems of incomplete delay composition analysis, poor environmental adaptability, lack of constraint collaboration of compensation instructions, no closed-loop optimization mechanism and the like in the existing aircraft remote control delay compensation are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote control of aircraft, and particularly relates to a remote control delay compensation response method and system for aircraft. BACKGROUND

[0002] Remote control of aircraft is widely used in the fields of civil inspection, logistics transportation and emergency rescue, but its control accuracy is easily affected by communication delay. Existing delay compensation methods mainly focus on single transmission delay estimation, and do not completely analyze the delay composition, often ignoring the influence of device processing time on total delay. At the same time, environmental fluctuations easily lead to delay estimation deviation, such as signal interference and weather changes, and traditional static calibration algorithms are difficult to dynamically adapt.

[0003] In addition, there is a blank in the perception of flight state during the delay period. The existing deduction model does not consider the influence of load change on flight characteristics, the compensation instruction generation lacks the coordination of physical constraints and task requirements, and there is no closed-loop feedback mechanism, which makes it difficult to continuously optimize the compensation accuracy and seriously affects the stability and safety of remote control.

[0004] The present application solves the problems of incomplete delay composition analysis, poor environmental adaptability, state deduction without considering load influence, compensation instruction without constraint coordination and no closed-loop optimization mechanism in the existing remote control delay compensation of aircraft, overcomes the defects of inaccurate delay estimation and low compensation accuracy, and ensures the stable and reliable operation of remote control in complex scenarios. SUMMARY

[0005] The present application provides a remote control delay compensation response method for aircraft, which comprises:

[0006] Obtain airborne data through multiple sensors, record control instructions synchronously, and construct a multi-source data set;

[0007] Calculate the two-way transmission delay based on a two-way delay estimation algorithm based on time stamp, superimpose the device processing time, obtain the initial total delay, and clearly define the basic delay composition;

[0008] Use an adaptive delay calibration algorithm to dynamically calibrate the initial total delay, evaluate the confidence, start the standby link detection at low time, and improve the delay accuracy;

[0009] Use a delay deduction hybrid model, input the modified delay and state sequence, deduce the flight state during the delay period, and dynamically adjust the parameters with the load;

[0010] Generate a compensation instruction containing constraints based on the deduction results and the original instruction using a model predictive control algorithm;

[0011] Execute the airborne calibration instruction, compare the actual and expected states, and feed back the deviation value to optimize the model weight of the next round, forming a closed-loop improvement mechanism.

[0012] The aircraft remote control delay compensation response method as claimed in any one of the above, wherein the on-board data is acquired by a multi-element sensor, the control instructions are synchronously recorded, and a multi-source data set is constructed, comprising:

[0013] The on-board data is collected, and a high-precision timestamp is bound for each frame of data to ensure the time reference consistency of the original data;

[0014] When the on-board data is received, the ground control instructions are synchronously recorded, the time-space matching of the flight state, the environmental parameters and the control instructions is performed through the timestamp alignment technology, and a multi-source associated data set is constructed.

[0015] The aircraft remote control delay compensation response method as claimed in any one of the above, wherein a two-way delay estimation algorithm based on timestamps is used to calculate the two-way transmission delay, the device processing time is superimposed to obtain the initial total delay, and the basic delay composition is determined, comprising:

[0016] The two-way transmission delay is calculated by the request-response timestamps of the on-board end and the ground station through the two-way delay estimation algorithm based on timestamps, the clock offset interference is eliminated, and the transmission link delay is quantified with high precision;

[0017] The instruction processing time of the on-board end and the ground station is extracted from the original interaction data, the initial total delay is calculated by superimposing the two-way transmission delay, and the basic composition ratio of the transmission delay and the processing time is determined.

[0018] The aircraft remote control delay compensation response method as claimed in any one of the above, wherein an adaptive delay calibration algorithm is used to dynamically calibrate the initial total delay, the confidence is evaluated, the standby link detection is started at low time, and the delay precision is improved, comprising:

[0019] Based on the data set, the adaptive delay calibration algorithm is used to fuse the current environmental parameters and the historical delay data to dynamically calibrate the initial total delay, and the influence of environmental fluctuations on delay estimation is weakened;

[0020] The confidence of the calibrated delay is evaluated in real time, when the confidence is lower than the threshold, the delay detection of the standby communication link is automatically started, and the delay precision is further improved through the data fusion of the primary and standby links.

[0021] The aircraft remote control delay compensation response method as claimed in any one of the above, wherein a model predictive control algorithm is used to generate a compensation instruction containing constraints according to the deduction result and the original instruction, comprising:

[0022] The deduction state in the delay period is compared with the expected state of the original control instruction, the deviation of the two is quantified, and the key dimensions that need to be compensated are determined;

[0023] Based on the deviation analysis result, a model predictive control algorithm is used to generate a targeted delay compensation instruction under the premise of meeting the physical constraints and task constraints of the aircraft, and to correct the execution deviation of the original instruction.

[0024] The aircraft remote control delay compensation response method as described above, wherein the on-board verification instruction is executed after the actual and expected states are compared, and the deviation value is fed back to optimize the model weight of the next round, forming a closed-loop improvement mechanism, comprising:

[0025] After the on-board system receives the compensation instruction, it first performs a legality verification, executes the instruction after the verification is passed, and records the real-time state data during the execution of the instruction;

[0026] The actual state after execution is compared with the deduced expected state and the compensation target, the deviation value is calculated, and the weight parameters of the calibration algorithm and the deduction model are dynamically adjusted through the closed-loop feedback mechanism to continuously improve the accuracy of the next round of delay compensation.

[0027] An aircraft remote control delay compensation response system, comprising:

[0028] The multi-source data fusion module is used to obtain on-board data through multi-element sensors, record control instructions synchronously, and construct a multi-source data set;

[0029] The delay quantization analysis module is used to calculate the two-way transmission delay based on the two-way delay estimation algorithm of the time stamp, superimpose the device processing time, obtain the initial total delay, and clarify the composition of the basic delay;

[0030] The adaptive delay calibration module is used to dynamically calibrate the initial total delay using an adaptive delay calibration algorithm, evaluate the confidence, start the standby link detection at low time, and improve the delay accuracy;

[0031] The load-aware state deduction module is used to input the corrected delay and state sequence into a delay deduction hybrid model, deduce the flight state in the delay period, and dynamically adjust the parameters with the load;

[0032] The constraint compensation instruction generation module is used to generate a compensation instruction containing constraints based on the deduction result and the original instruction using a model predictive control algorithm;

[0033] The closed-loop feedback optimization module is used to compare the actual and expected states after the on-board verification instruction is executed, feed back the deviation value to optimize the model weight of the next round, and form a closed-loop improvement mechanism.

[0034] A computer-readable storage medium, comprising one or more program instructions for being executed by a processor to implement the aircraft remote control delay compensation response method of any one of claims 1-6.

[0035] The beneficial effects achieved by the present application are as follows:

[0036] By fusing multi-source data to build a complete dataset, the system accurately analyzes delay components. Adaptive calibration and backup link detection dynamically offset environmental interference, improving delay estimation accuracy. A hybrid deduction model with payload sensing fills the gap in state perception during delay periods. Constrained compensation instructions are generated based on model predictive control to ensure safe execution. This significantly improves delay compensation accuracy and dynamic adaptability, effectively ensuring the stability and reliability of remote control of the aircraft. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0038] Figure 1 This is a flow chart of a delay compensation response method for remote control of an aircraft provided in Example 1 of the present application.

[0039] Figure 2 This is a schematic diagram of a delay compensation response system for remote control of an aircraft provided in Example 2 of the present application. DETAILED DESCRIPTION

[0040] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0041] Example 1

[0042] like Figure 1 As shown, the first embodiment of the present application provides a method for delay compensation response of remote control of an aircraft, comprising:

[0043] S1: Acquire airborne data through multi-sensor technology, simultaneously record control commands, and construct a multi-source dataset;

[0044] The process of acquiring airborne data through multi-sensor systems, synchronously recording control commands, and constructing a multi-source dataset includes the following sub-steps:

[0045] S11: Collects airborne data and binds a high-precision timestamp to each frame of data to ensure the time base consistency of the original data;

[0046] The multi-element heterogeneous sensor array is used to collect the airborne data, wherein the airborne data at least includes flight states and environmental parameters, sensor data, covers attitude measurement, positioning and environmental perception modules, and each module outputs data at a set frequency. The system binds a high-precision timestamp for each frame of original data, and the timestamp is derived from an airborne unified clock source, so as to ensure that the time references of different sensor data are consistent. Through the data preprocessing module, the collected data is standardized in format, abnormal values are filtered, and data integrity is maintained.

[0047] S12: When receiving the airborne data, the ground control instruction is recorded synchronously, the time-space matching of the flight state, the environmental parameter and the control instruction is performed through the timestamp alignment technology, and a multi-source associated data set is constructed.

[0048] The airborne transmission data is received, and a ground control instruction recording mechanism is started synchronously, and a generation timestamp is added for each instruction. Based on the timestamp alignment algorithm, the time-space matching of the flight state data, the environmental parameter and the control instruction is performed, the similar timestamp data is associated through a sliding window, and a multi-source associated data set is constructed. The data set is organized in time sequence and stored in a structured database, and a data set containing a complete interaction process is formed.

[0049] S2: A two-way delay estimation algorithm based on a timestamp is used to calculate the two-way transmission delay, superimpose the equipment processing time consumption, obtain the initial total delay, and clearly define the basic delay composition.

[0050] The two-way delay estimation algorithm based on the timestamp is used to calculate the two-way transmission delay, superimpose the equipment processing time consumption, obtain the initial total delay, and clearly define the basic delay composition, including the following sub-steps:

[0051] S21: A two-way delay estimation algorithm based on a timestamp is used to calculate the two-way transmission delay through the request-response timestamps of the airborne end and the ground station, eliminate the clock offset interference, and quantize the transmission link delay with high precision;

[0052] A timestamp synchronization module deployed on the airborne end and the ground station is used to establish a two-way request-response interaction mechanism. The airborne end sends a request data packet with an initial timestamp, the ground station records a receiving timestamp after receiving the request data packet, then generates a response data packet and adds a sending timestamp of the ground station, and the airborne end records a final timestamp after receiving the response data packet. Based on the set of timestamp data, a two-way delay estimation algorithm is used to calculate the transmission round-trip time, and a clock offset compensation formula is used to eliminate the influence of the clock asynchronization of the two ends, so as to independently quantize the transmission link delay and ensure the accuracy of the two-way transmission delay data.

[0053] The two-way transmission delay calculation formula is:

[0054]

[0055] wherein, T1 represents the local timestamp of the airborne terminal sending the request data packet, which is the reference time of the entire two-way interaction; T2 represents the local timestamp of the ground station receiving the request data packet, reflecting the time when the ground station actually receives the request; d1(t) represents the time-varying transmission delay function of the airborne terminal to the ground station at t time, describing the dynamic change of the uplink delay with time; T3 represents the local timestamp of the ground station sending the response data packet, reflecting the time when the ground station sends the response after processing; T4 represents the local timestamp of the airborne terminal receiving the response data packet, reflecting the time when the airborne terminal actually receives the response; d2(t) represents the time-varying transmission delay function of the ground station to the airborne terminal at t time, describing the dynamic characteristics of the downlink delay;

[0056] Δt represents the clock offset between the airborne terminal and the ground station; θ represents the periodic variation characteristics of the transmission environment, which is used to quantify the influence of periodic interference on the delay; represents the variance of the uplink delay, describing the intensity of the random fluctuation of the uplink delay; represents the variance of the downlink delay, describing the intensity of the random fluctuation of the downlink delay; cov(d1, d2) represents the covariance of the uplink and downlink delays, quantifying the statistical correlation of the uplink and downlink delays; τ1 represents the processing time random variable of the ground station; τ2 represents the processing time random variable of the airborne terminal; represents the variance of the processing time, describing the fluctuation degree of τ1 and τ2 as a whole; α represents the normalization constant of the Gaussian function; ε k represents the Gaussian white noise of the kth sampling; δ represents a very small positive value; ω k represents the weighting coefficient of the kth sampling; n represents the total number of samplings; T k represents the time when the airborne terminal sends the request data packet corresponding to the kth sampling; represents the time rate of change of the signal transmission phase.

[0057] S22: Extract the instruction processing time of the airborne terminal and the ground station from the original interaction data, superimpose the two-way transmission delay to calculate the initial total delay, and clearly indicate the basic constituent ratio of the transmission delay and the processing time.

[0058] The system extracts the instruction processing related time nodes from the interaction log of the airborne terminal and the ground station, and obtains the time consumption of the data decoding of the airborne terminal, the instruction analysis and the instruction generation and encoding of the ground station through time difference calculation. Superimpose these processing time consumptions and the transmission delay of S21 to obtain the initial total delay.

[0059] wherein, the initial total delay calculation formula is:

[0060]

[0061] where D denotes the two-way transmission delay; k1 denotes the transmission delay weight coefficient, reflecting the basic proportion of transmission delay in the total delay; k2 denotes the processing time consumption weight coefficient, reflecting the basic proportion of processing time consumption; t d (s) denotes the time-varying function of airborne data decoding time consumption, s is the continuous time variable in the processing process, and describes the dynamic change of decoding time consumption with data volume; denotes the hyperbolic cosine function, used to modulate the nonlinear growth characteristics of airborne decoding time consumption, where t g (s) denotes the time-varying function of ground station instruction generation time consumption, describing the dynamic change of ground station generation control instruction time consumption with task complexity; e -αs denotes the exponential decay function, where a is the decay coefficient; τ denotes the characteristic time constant of the processing process, representing the transition time length of processing time consumption from start to stabilization;

[0062] denotes the overall variance of processing time, quantifying the random fluctuation intensity of all processing steps (decoding, instruction generation, etc.); ρ denotes the correlation coefficient of airborne and ground station processing time, reflecting the coordinated change trend of processing time consumption of the two ends; μ A is the average processing time consumption of the airborne end, μ B is the average processing time consumption of the ground station, and the difference between the two reflects the difference in processing capacity; ξ is a nonlinear interference factor, simulating a sudden disturbance; ∈ is a very small positive value, avoiding numerical overflow caused by denominator approaching 0; denotes the derivative operation on the characteristic time τ, depicting the change rate of transmission-processing coupling effect with processing stage; cov(t proc , D) denotes the covariance of total processing time t proc and transmission delay D, quantifying the statistical correlation of the two; denotes the sine function, simulating the periodic change of coupling effect; where τ is the characteristic time constant of the processing process, Θ p is the periodic constant of the processing process; μ p denotes the overall mean of processing time, describing the average level of processing time consumption.

[0063] The system further determines the proportion of transmission delay and processing time consumption in the total delay through proportional analysis, providing quantifiable delay composition basis for subsequent targeted optimization.

[0064] S3: dynamically calibrating the initial total delay using an adaptive delay calibration algorithm, evaluating confidence, starting backup link detection at low time, and improving delay precision;

[0065] where the adaptive delay calibration algorithm is used to dynamically calibrate the initial total delay, evaluate confidence, start backup link detection at low time, and improve delay precision, including the following sub-steps:

[0066] S31: Based on the data set, the initial total delay is dynamically calibrated by fusing the current environment parameters and historical delay data through an adaptive delay calibration algorithm, and the influence of environmental fluctuations on delay estimation is weakened;

[0067] The adaptive delay calibration algorithm is called to process the initial total delay data. The current environment parameters and historical delay records are extracted from the multi-source data set, the similarity between the current environment and the historical environment is calculated through the parameter matching module, and the dynamic weight of the historical delay data is assigned. The initial total delay and the weighted historical delay are dynamically calibrated to generate the calibrated delay value, and the short-term fluctuations are smoothed through the sliding window mechanism to weaken the interference of environmental factor mutation on delay estimation.

[0068] wherein the dynamic calibration formula is:

[0069]

[0070] M represents the final calibrated delay value, which is used for delay compensation of remote control of the aircraft; T total represents the initial total delay; S(t) represents an environmental similarity function, which describes the matching degree of the current environment and the historical environment; k represents the kth historical data; N represents the total number of historical data; w k (t) represents the dynamic weight function of the kth historical delay data, which is calculated in real time by the parameter matching module according to the environmental similarity; D hist,k represents the kth historical delay record; c k represents the confidence of the kth historical data, reflecting the collection quality of the historical data; represents a time decay term, λ is a time decay coefficient, t is a current timestamp, t k is the collection timestamp of the kth historical data;

[0071] γ represents an environmental mutation factor, which is used to attenuate the output of the calibration base term when the environment fluctuates violently, avoiding abnormal value interference; represents the current t time environmental fluctuation variance, which describes the real-time fluctuation degree of the environmental parameters; N represents the size of the sliding window, i.e. the number of historical data participating in the fluctuation smoothing calculation; Var(D hist (τ)) represents a variance function of historical delay in the sliding window, which quantifies the dispersion degree of historical delay data in the window; D hist (τ) represents a continuous function of historical delay in the sliding window, τ is a time variable in the window, which describes the change of historical delay with time; represents an exponential decay function, |t-τ| represents the time difference between the current time t and the time point τ in the window; Δt represents the time interval of the sliding window, i.e. the collection time interval of adjacent two historical data; Represents the short-term volatility variance, which quantifies the random fluctuation intensity of delayed data;

[0072] S32: Evaluate the confidence level of the calibrated delay in real time. When the confidence level is lower than the threshold, automatically start delay detection on the backup communication link, and further improve delay accuracy by fusing data from the primary and backup links.

[0073] The confidence level of the calibrated delay is calculated through historical data consistency verification and current deviation analysis. When the confidence level falls below a threshold, the backup communication link detection module is automatically activated to simultaneously collect delay data from the backup link. The data from the primary and backup links is cross-validated, and the reliability assessment model assigns appropriate weights to the data from different links. The final delay result is generated through fusion calculations, ensuring a secondary improvement in accuracy in low-confidence scenarios.

[0074] S4: Using a delay-deduction hybrid model, input the corrected delay and state sequence, deduce the flight state during the delay period, and dynamically adjust the parameters according to the payload;

[0075] The corrected delay data and continuous flight state sequence are integrated as input to a hybrid delay prediction model. This model integrates a physical kinematics module and a data-driven module, which work together through a dynamic weighting mechanism. The physical kinematics module is based on the aircraft dynamics equations, embedding aerodynamic constraints and kinematic restrictions to infer state changes from the perspective of physical laws. The data-driven module retrieves historical data sets, locates historical segments similar to the current operating conditions through a similarity matching algorithm, and extracts their state evolution characteristics as a reference.

[0076] The system initiates a simulation using the corrected delay duration as the time span: the physics module first generates a basic motion trajectory based on the current state parameters. The data-driven module then fine-tunes the basic trajectory by introducing deviation correction factors from similar historical cases. When the historical data matches well, the data-driven module's weight is increased to optimize details; when the operating conditions are unique, the physics module's weight is increased to ensure the physical plausibility of the simulation. During the simulation, the system acquires payload information in real time from onboard sensors and inputs a preset payload-parameter mapping model. This model dynamically adjusts the physics module's core parameters, such as the inertia coefficient and damping coefficient, based on the payload value. It also corrects the historical case matching weights of the data-driven module to ensure that the simulation results are adapted to the flight characteristics corresponding to the current payload, ultimately generating a state sequence for the delay period.

[0077] S5: Based on the deduction results and the original instructions, the model predictive control algorithm is used to generate compensation instructions with constraints;

[0078] Among them, based on the deduction results and the original instructions, the model predictive control algorithm is used to generate the compensation instructions with constraints, including the following sub-steps:

[0079] S51: Compare the inferred state in the delay period with the expected state of the original control instruction, quantify the deviation of both, and determine the key dimensions that need to be compensated;

[0080] The state sequence in the delay period and the original control instruction are retrieved and mapped to the same time dimension through a spatiotemporal alignment algorithm. The system extracts key state parameters and uses a multi-dimensional deviation quantification model to calculate the difference values of both in each parameter dimension, including instantaneous deviation and cumulative deviation. At the same time, combined with the task requirement, the deviation weight coefficient is defined to weight and integrate the deviations in different dimensions, and the key items that need to be compensated are determined.

[0081] S52: Based on the deviation analysis results, the model predictive control algorithm is used to generate targeted delay compensation instructions to correct the execution deviation of the original instruction under the premise of meeting the physical constraints and task constraints of the aircraft.

[0082] The multi-dimensional key deviation items, delay period state, and real-time physical parameters of the aircraft are used as inputs to the model predictive control module. The module embeds the delay time into the prediction time domain setting for the remote control scenario of the aircraft, so that the optimization window covers the complete period of delay impact. The core layer of the algorithm associates the current deviation with the future state evolution through the state equation, converts the physical constraints into inequality constraints, and converts the task constraints into objective function weights.

[0083] Through the rolling optimization mechanism, the system solves the optimal control sequence in a limited time domain at each calculation step and outputs the compensation instructions containing the adjustment amount of each control channel. The difference between the original control instruction and the compensation instruction is checked to ensure that the compensation amount is within the safety threshold, and the delay period state sequence inferred in S4 is associated through the state feedback item, so that the compensation instruction can dynamically offset the cumulative error of the delay, and finally generate the compensation instruction sequence.

[0084] S6: Execute the on-board verification instruction, compare the actual and expected states, and feedback the deviation value to optimize the model weight in the next round, forming a closed-loop improvement mechanism.

[0085] Among them, the on-board verification instruction is executed, the actual and expected states are compared, and the deviation value is fed back to optimize the model weight in the next round, forming a closed-loop improvement mechanism, including the following sub-steps:

[0086] S61: After the on-board system receives the compensation instruction, it first performs legality verification, executes the instruction after verification, and records the real-time state data during the execution of the instruction;

[0087] After receiving the compensation command, the onboard command verification module is activated to verify that the command complies with the actuator's physical range and mission safety constraints. Once verified, the command is parsed into control variables for each actuator and executed. Simultaneously, sensors deployed on S1 synchronously collect real-time flight status during command execution, attaching an execution timestamp to each frame of status data to form a complete command execution record.

[0088] S62: Compare the actual state after execution with the expected state and compensation target of the deduction, calculate the deviation value, and dynamically adjust the weight parameters of the calibration algorithm and the deduction model through a closed-loop feedback mechanism to continuously improve the accuracy of the next round of delay compensation.

[0089] The recorded actual execution state is spatially and temporally aligned with the output deduced expected state and the set compensation target state. A deviation analysis algorithm is then used to calculate the differences in each dimension between the three. Based on these deviations, the system initiates an adaptive weight adjustment mechanism: For the S3 adaptive delay calibration algorithm, environmental parameter weights are dynamically corrected based on delay estimation deviations. For the S4 deduction model, the fusion coefficients of the physical and data-driven modules are optimized based on state prediction deviations, making the corrected model parameters more suitable for the current flight scenario, thus forming a closed-loop optimization for delay compensation.

[0090] Example 2

[0091] like Figure 2 As shown, the second embodiment of the present application provides an aircraft remote control delay compensation response system, including:

[0092] Multi-source data fusion module 21: used to obtain airborne data through multiple sensors, synchronously record control instructions, and construct a multi-source data set;

[0093] Delay quantification and analysis module 22: used to calculate the two-way transmission delay based on the timestamp-based two-way delay estimation algorithm, add the device processing time, and obtain the initial total delay to clarify the basic delay composition;

[0094] Adaptive delay calibration module 23: used to dynamically calibrate the initial total delay using an adaptive delay calibration algorithm, evaluate the confidence level, and initiate backup link detection when the confidence level is low, thereby improving delay accuracy;

[0095] The payload sensing state deduction module 24 is used to use a delay deduction hybrid model, input the corrected delay and state sequence, deduce the flight state during the delay period, and dynamically adjust the parameters according to the payload;

[0096] Constrained compensation instruction generation module 25: used to generate constrained compensation instructions using a model predictive control algorithm based on the deduction results and the original instructions;

[0097] The closed-loop feedback optimization module 26 is used to perform the on-board verification instruction, compare the actual state with the expected state, and feed back the deviation value to optimize the model weight in the next round, thereby forming a closed-loop improvement mechanism.

[0098] Corresponding to the above-mentioned embodiments, the embodiments of the present application provide a computer storage medium, comprising at least one memory and at least one processor.

[0099] The memory is used to store one or more program instructions.

[0100] The processor is used to run the one or more program instructions to perform the aircraft remote control delay compensation response method.

[0101] Corresponding to the above-mentioned embodiments, the embodiments of the present application provide a computer readable storage medium, and the computer readable storage medium contains one or more program instructions, and the one or more program instructions are used to perform the aircraft remote control delay compensation response method by the processor.

[0102] The embodiments of the present application provide a computer readable storage medium, and the computer readable storage medium stores computer program instructions, and when the computer program instructions run on the computer, the computer executes the above-mentioned aircraft remote control delay compensation response method.

[0103] In the embodiments of the present application, the processor can be an integrated circuit chip with signal processing capability. The processor can be a general processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0104] The disclosed methods, steps and logic block diagrams in the embodiments of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as hardware code processor execution or executed by a combination of hardware and software modules in the code processor. The software module can be located in a random memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The processor reads the information in the storage medium and combines the hardware to complete the steps of the above-mentioned method.

[0105] The storage media can be a memory, such as can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory.

[0106] The non-volatile memory can be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory.

[0107] The volatile memory can be a Random Access Memory (RAM) used as an external cache memory. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).

[0108] The storage media described in the embodiments of the present application is intended to include, but not be limited to, these and any other suitable types of memory.

[0109] Those skilled in the art will realize that the functions described in the one or more examples above can be implemented in software as well as hardware. When applied in software, the functions can be stored in or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a general purpose or special purpose computer.

[0110] The above detailed description of the specific embodiments of the present application has been given to illustrate the purpose, technical solutions and beneficial effects of the present application. It should be understood that the above description is only a specific embodiment of the present application and is not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made on the basis of the technical solutions of the present application shall be included in the protection scope of the present application.

Claims

1. A method for delay compensation response of aircraft remote control, characterized in that: include: Acquire airborne data through multi-sensor technology, simultaneously record control commands, and construct multi-source data sets; A timestamp-based two-way delay estimation algorithm calculates the two-way transmission delay, adds the device processing time, and derives the initial total delay, clarifying the basic delay composition. Use an adaptive delay calibration algorithm to dynamically calibrate the initial total delay, evaluate the confidence level, and initiate backup link detection when the confidence level is low, thereby improving delay accuracy. A delay-deduction hybrid model is used to input the corrected delay and state sequence, deduce the flight state during the delay period, and dynamically adjust the parameters according to the load; Based on the deduction results and the original instructions, the model predictive control algorithm is used to generate compensation instructions with constraints; After the onboard verification instruction is executed, the actual and expected states are compared, and the deviation value is fed back to optimize the weight of the next round of model, forming a closed-loop improvement mechanism.

2. The method for delay compensation response of aircraft remote control according to claim 1, characterized in that: Acquire onboard data through multiple sensors, simultaneously record control commands, and build a multi-source dataset, including: Collect airborne data and bind a high-precision timestamp to each frame of data to ensure the time base consistency of the original data; When receiving airborne data, ground control instructions are recorded synchronously, and time-stamp alignment technology is used to perform spatiotemporal matching of flight status, environmental parameters and control instructions to construct a multi-source correlated data set.

3. The method for delay compensation response of aircraft remote control according to claim 1, characterized in that: The timestamp-based two-way delay estimation algorithm calculates the two-way transmission delay and adds the device processing time to obtain the initial total delay. This clarifies the basic delay components, including: A timestamp-based two-way delay estimation algorithm is used to calculate the two-way transmission delay using the request-response timestamps between the airborne terminal and the ground station, eliminating clock offset interference and quantifying the transmission link delay with high precision. The command processing time between the airborne terminal and the ground station is extracted from the original interaction data, and the initial total delay is calculated by adding it to the two-way transmission delay, and the basic composition ratio of transmission delay and processing time is clarified.

4. The method for delay compensation response of aircraft remote control according to claim 1, characterized in that: An adaptive delay calibration algorithm is used to dynamically calibrate the initial total delay, assess the confidence level, and initiate backup link detection when the confidence level is low, improving delay accuracy. This includes: Based on the data set, an adaptive delay calibration algorithm is used to integrate current environmental parameters with historical delay data to dynamically calibrate the initial total delay, reducing the impact of environmental fluctuations on delay estimation. The confidence level of the calibrated delay is evaluated in real time. When the confidence level falls below the threshold, delay detection on the backup communication link is automatically started, further improving delay accuracy by fusing data from the primary and backup links.

5. The method for delay compensation response of aircraft remote control according to claim 1, characterized in that: Based on the deduction results and the original instructions, the model predictive control algorithm is used to generate compensation instructions with constraints, including: Compare the simulated state during the delay period with the expected state of the original control command, quantify the deviation between the two, and identify the key dimensions that need to be compensated; Based on the deviation analysis results, the model predictive control algorithm is used to generate targeted delay compensation instructions and correct the execution deviation of the original instructions while meeting the physical constraints and mission constraints of the aircraft.

6. The method for delay compensation response of aircraft remote control according to claim 1, characterized in that: After the onboard verification command is executed, the actual and expected states are compared and the deviation value is fed back to optimize the next round of model weights, forming a closed-loop improvement mechanism, including: After receiving the compensation instruction, the airborne system first performs a legality check, executes the instruction after passing the check, and records the real-time status data during the execution of the instruction; The actual state after execution is compared with the expected state and compensation target of the deduction, and the deviation value is calculated. The weight parameters of the calibration algorithm and the deduction model are dynamically adjusted through a closed-loop feedback mechanism to continuously improve the accuracy of the next round of delay compensation.

7. An aircraft remote control delay compensation response system, characterized in that: include: The multi-source data fusion module is used to acquire airborne data through multiple sensors, synchronously record control instructions, and construct a multi-source data set; The delay quantification and analysis module is used to calculate the two-way transmission delay based on the timestamp-based two-way delay estimation algorithm. It adds the device processing time to obtain the initial total delay and clarify the basic delay composition. The adaptive delay calibration module is used to dynamically calibrate the initial total delay using an adaptive delay calibration algorithm, evaluate the confidence level, and initiate backup link detection when the confidence level is low, thereby improving delay accuracy. The payload sensing state deduction module is used to use a delay deduction hybrid model, input the corrected delay and state sequence, deduce the flight state during the delay period, and dynamically adjust the parameters according to the payload; The constraint compensation instruction generation module is used to generate constraint compensation instructions using the model predictive control algorithm based on the deduction results and the original instructions; The closed-loop feedback optimization module is used to execute after the onboard verification instruction, compare the actual and expected states, and feedback the deviation value to optimize the next round of model weights, forming a closed-loop improvement mechanism.

8. A computer-readable storage medium, characterized in that The method comprises one or more program instructions, wherein the one or more program instructions are used to be executed by a processor to implement the aircraft remote control delay compensation response method as described in any one of claims 1 to 6.

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