Airborne system whole life cycle configuration state management system based on digital twinning

Abstract

This invention relates to the field of digital twin technology, specifically to a digital twin-based airborne system full lifecycle configuration status management system. This system includes acquiring the airborne bus protocol, hardware and software logic version codes, real-time impedance spectrum data, and electrical response frequency characteristic fingerprints of the fleet's aircraft, and combining this with collaborative task profile vectors. A real-time configuration dynamic evolution map of each aircraft is constructed using a pre-set digital twin model, and the overlap of configuration envelopes between map node attribute parameters and task constraints is calculated. Fleet configuration distribution entropy values ​​are introduced to identify common-mode failure risks caused by configuration homogenization, and a fleet collaborative dispatch decision instruction set is generated by combining risk impact factors. Furthermore, reverse calibration of physical performance aging parameters is performed using actual task residuals to ensure the continuous confidence of the digital twin model. This invention achieves precise configuration control through two-layer mapping and closed-loop correction.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, specifically to a configuration and status management system for the entire lifecycle of an airborne system based on digital twins. Background Technology

[0002] With the rapid development of aviation technology, the integration and complexity of airborne systems are constantly increasing, making configuration management of aircraft throughout their entire lifecycle increasingly important. Airborne system configuration encompasses not only the physical characteristics of the hardware but also the software logic versions running on it. During an aircraft's service life, the actual state of the airborne system is constantly changing due to hardware and software upgrades, scheduled maintenance, component replacements, and performance evolution caused by environmental stresses.

[0003] Traditional configuration management relies primarily on static historical documentation and periodic ground inspections to maintain aircraft status consistent with original design baselines. However, with the increasing complexity of modern air combat and civil aviation operational mission profiles, aircraft often need to perform missions under stringent constraints such as cross-domain collaboration, strong electromagnetic interference, and extreme flight envelopes. These missions place extremely high real-time demands on the aircraft's onboard bus real-time response capabilities, hardware and software logic compatibility, and physical anti-interference performance.

[0004] Against this backdrop, a key technical challenge is how to characterize the actual configuration state of each aircraft in the fleet throughout its entire lifecycle evolution in real time, and to achieve a quantitative assessment of the adaptability of this state to multi-dimensional, dynamically changing collaborative task constraints.

[0005] To address this, a digital twin-based airborne system full lifecycle configuration and status management system is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a digital twin-based airborne system full lifecycle configuration status management system, which achieves precise configuration control through two-layer mapping and closed-loop correction. This includes acquiring the airborne bus protocol, hardware and software logic version codes, real-time impedance spectrum data, and electrical response frequency characteristic fingerprints of the fleet's aircraft, and combining this with collaborative task profile vectors. A real-time configuration dynamic evolution map of each aircraft is constructed using a pre-set digital twin model, and the overlap of configuration envelopes between map node attribute parameters and task constraints is calculated. Fleet configuration distribution entropy values ​​are introduced to identify common-mode failure risks caused by configuration homogenization, and a fleet collaborative dispatch decision instruction set is generated by combining risk impact factors. Furthermore, reverse calibration of physical performance aging parameters is performed using actual task residuals to ensure the continuous confidence of the digital twin model.

[0007] To achieve the above objectives, the present invention provides the following technical solution: A digital twin-based airborne system lifecycle configuration and status management system includes: The perception module acquires in real time the airborne bus protocol data, hardware and software logic version codes, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative mission profile vectors for each aircraft in the fleet. The map construction module inputs airborne bus protocol data, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, and software and hardware logic version codes into a pre-set digital twin model to construct a real-time configuration dynamic evolution map for each aircraft. The evaluation module uses a dynamic evolution graph to calculate the configuration envelope overlap between the graph node attribute parameters and the collaborative task profile vector; the configuration envelope overlap is determined based on the physical protection effectiveness mapped from real-time impedance spectrum data, the hardware integrity confidence mapped from electrical response frequency feature fingerprints, and the protocol interoperability matrix corresponding to the software and hardware logic version codes. The decision-making instruction module, based on the dynamic evolution map of all aircraft in the fleet, calculates the fleet configuration distribution entropy value, matches the common mode risk influence factor in the preset risk matrix according to the fleet configuration entropy value, and obtains the fleet collaborative dispatch decision instruction set by combining the configuration envelope overlap degree and the common mode risk influence factor.

[0008] Preferably, the process of acquiring data for each aircraft in the fleet includes: applying a multi-frequency excitation current signal in the 10Hz to 1MHz frequency band to the cable shielding layer using an airborne sensing unit, and generating the real-time impedance spectrum data through fast Fourier transform processing; acquiring the high-frequency transient waveform of the power bus of the replaceable unit at the moment of power-on of the power distribution system, and extracting the power spectral density features to generate the electrical response frequency feature fingerprint; extracting the software and hardware logic version code, including part number, serial number, and software checksum, by listening to the self-test message of the bus, and attaching a timestamp based on the airborne unified clock; receiving multi-dimensional mission instruction packets from the mission planning system, parsing the electromagnetic radiation field strength distribution and interference frequency range of the mission airspace as electromagnetic environment effect level requirements; parsing the maximum instantaneous overload, extreme flight altitude, and minimum turning radius parameters required by the mission as the flight mission envelope constraints; and converging the obtained electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative communication parameters, including the end-to-end delay requirements of the cooperative communication link, to construct a cooperative mission profile vector.

[0009] Preferably, the process of constructing a real-time configuration dynamic evolution map for each aircraft includes: extracting bill of materials data from the initial design phase of the aircraft, establishing a configuration baseline map containing equipment nodes and logical connection relationships, and defining the nominal electrical characteristic parameters and standard version parameters of each equipment node in the design state; mapping the acquired airborne bus protocol data and hardware / software logical version codes to the logical attribute layer of the baseline map, and updating the real-time version status and communication topology of each equipment node; mapping real-time impedance spectrum data and electrical response frequency feature fingerprints to the physical performance layer of the configuration baseline map, and calculating the physical performance integrity data of each equipment node by comparing the nominal electrical characteristic parameters; using the phase shift in the real-time impedance spectrum data and the checksum changes in the hardware / software logical version codes, comparing the initial configuration baseline topology, and identifying the parameter offset of each equipment node from the initial design state to the current real-time state; combining the parameter offset, the data of the logical attribute layer, and the physical performance layer, and using a time axis association algorithm to sequentially connect the configuration states at different time nodes to generate a dynamic evolution map.

[0010] Preferably, the process of calculating the overlap of the configuration envelope between the attribute parameters of the spectrum nodes and the collaborative task profile vector includes: inputting real-time impedance spectrum data into the electromagnetic shielding effectiveness mapping matrix to calculate the electromagnetic coupling attenuation coefficient under different frequency nodes; using the electrical response frequency feature fingerprint and the pre-stored standard feature template to perform cross-correlation similarity calculation to determine the hardware integrity confidence; retrieving the software and hardware dependency table to determine the protocol compatibility parameters; using the electromagnetic coupling attenuation coefficient, hardware integrity confidence, and protocol compatibility parameters as feature vectors of coordinate axis dimensions to construct a feature space lattice in multidimensional Euclidean space; extracting electromagnetic environment effect level requirements, flight mission envelope constraints, and collaborative communication constraint parameters from the collaborative task profile vector, and fitting and generating electromagnetic safety boundary surfaces, dynamic load limitation surfaces, and logical interoperability boundary surfaces in multidimensional Euclidean space respectively, and enclosing them to form a closed hyperspace; calculating the lattice density distribution of the configuration feature space lattice inside the closed hyperspace in multidimensional Euclidean space, and performing a ratio calculation with the total density of the configuration feature space lattice to obtain the configuration envelope overlap.

[0011] Preferably, the process of matching common-mode risk impact factors in a preset risk matrix based on fleet configuration entropy values ​​includes: aggregating the dynamic evolution map of all aircraft in the fleet, extracting multi-dimensional configuration parameters including hardware production batches, software patch verification, and normalized physical performance deviations, and constructing configuration feature vectors representing the technical status of individual aircraft; within the multi-dimensional feature distribution space, using feature topological similarity recognition logic, classifying the configuration feature vectors, identifying aircraft groups with common technical characteristics, and calculating the distribution probability values ​​of each aircraft group in the total fleet sample; introducing information theory metric logic, performing weighted product operations on each distribution probability value and the corresponding logarithmic value, and taking the negative of the sum of each product to generate fleet configuration distribution entropy values; if the entropy value is lower than a preset low threshold, it is determined to be a common-mode failure risk caused by configuration homogenization, and the corresponding first risk correction coefficient is extracted from the logic table; if the entropy value is higher than a preset high threshold, the corresponding second risk correction coefficient is extracted from the logic table; and the first risk correction coefficient or the second risk correction coefficient is used as a common-mode risk impact factor.

[0012] Preferably, the process of obtaining the fleet collaborative dispatch decision instruction set includes: comparing the acquired common-mode risk impact factors with a preset risk level discrete mapping table, identifying the numerical range to which the impact factors belong and extracting the corresponding gradient deduction weights; using the gradient deduction weights to perform proportional reduction calculations on the configuration envelope overlap to obtain a quantitative task readiness score; real-time retrieval of a preset minimum equipment list database and airworthiness directive database, performing a logical XOR operation between the dynamic evolution map features and the prohibition dispatch criteria in the database; if the calculation result shows a logical conflict, then the corresponding task readiness score is set to zero, and the corresponding aircraft access is blocked. The process involves: identifying aircraft; combining the task readiness scores of all aircraft in the fleet with the task urgency parameters in the collaborative task profile vector; using conflict resolution logic to rank candidate aircraft in multiple dimensions and generating an aircraft scheduling candidate sequence; extracting hardware batches and software checksums from the real-time configuration dynamic evolution map of selected aircraft to generate configuration access feature labels; calculating physical access control bit parameters based on the deviation between the configuration envelope overlap and the physical boundary; encapsulating the aircraft access identifier, configuration access feature labels, and physical access control bit parameters to generate and issue a fleet collaborative dispatch decision instruction set; and releasing the aircraft by executing instructions through the airborne physical interface.

[0013] Preferably, the method further includes: during and after the aircraft performs a mission, synchronously collecting the measured impedance variation sequence and the measured profile of the mission load through the airborne maintenance system; calculating the deviation residuals of the measured feature feedback data and the predicted features in the dynamic evolution map; constructing a parameter sensitivity matrix using the residual values ​​and flight mission envelope constraints; performing reverse calibration compensation on the physical performance aging reference parameters according to the parameter sensitivity matrix; if the corrected deviation residuals exceed a preset health warning threshold, automatically triggering the preventive maintenance suggestion label for the corresponding node in the real-time configuration dynamic evolution map, and synchronously updating the configuration risk weight of the aircraft in subsequent missions; extracting the reverse calibration compensation parameters of a single aircraft as common evolution features, and synchronizing them to the dynamic evolution map of all aircraft in the fleet.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. By establishing a spatial projection mechanism between a multi-dimensional capability feature space and a mission requirement hyperspace, quantitative alignment of heterogeneous configuration parameters and complex mission constraints is achieved within the same coordinate system. This geometric overlap calculation logic effectively solves the problems of fuzzy qualitative judgments and difficulty in balancing multi-objective constraints in traditional manual assessments, enabling highly mathematically deterministic assessments of a single aircraft's adaptability to specific electromagnetic environments and flight envelopes. Utilizing the ratio of point cloud density distribution to geometric volume as an admission criterion not only improves the precision of dispatch decisions but also ensures that the mission execution boundary remains within the aircraft's current physical tolerance range through dynamic envelope contraction caused by physical performance degradation, significantly reducing safety hazards associated with operations exceeding the flight envelope.

[0015] 2. By introducing the coupling logic of fleet configuration distribution entropy value calculation and risk matrix, the scope of configuration auditing has been successfully elevated from single-aircraft compliance to the safety dimension of the entire fleet. This process identifies hidden technical clusters through feature topological clustering and quantifies the homogeneity of the fleet using entropy values, thereby establishing an early warning mechanism at the management level for common-mode failure risks caused by overly uniform configurations and collaborative efficiency losses caused by overly heterogeneous configurations. This two-way risk correction method based on group dispersion fills the gap in traditional single-aircraft airworthiness management, which cannot perceive the systemic vulnerabilities of the fleet.

[0016] 3. By constructing a closed-loop correction process based on actual mission feedback, the dynamic map of the digital twin was transformed from open-loop prediction to adaptive evolution. Residual analysis was performed using the measured impedance sequence and load profile after mission execution, and the aging baseline parameters were reverse-calibrated using the sensitivity matrix, effectively eliminating model drift errors caused by extreme environmental stress or nonlinear physical attenuation. This closed-loop mechanism ensures that the configuration state in the digital space always follows the real evolution trajectory of the physical entity with high fidelity, solving the problem of confidence decline after long-term service of the digital twin system. Simultaneously, by extracting individual correction parameters as common features and synchronizing them to the fleet, rapid sharing and evolution of collective experience were achieved. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the airborne system full lifecycle configuration status management system based on digital twins according to the present invention; Figure 2 This is a schematic diagram of the process of the airborne system full lifecycle configuration status management system based on digital twins according to the present invention; Figure 3 This is a schematic diagram of the matching common mode risk impact factor process of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figures 1 to 3 This invention provides a digital twin-based airborne system full lifecycle configuration and status management system, the technical solution of which is as follows:

[0020] Example 1: A digital twin-based airborne system lifecycle configuration and status management system, the specific process of which is as follows: Figure 1 and Figure 2 As shown, it includes: The perception module acquires in real time the airborne bus protocol data, hardware and software logic version codes, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative mission profile vectors for each aircraft in the fleet. The map construction module inputs airborne bus protocol data, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, and software and hardware logic version codes into a pre-set digital twin model to construct a real-time configuration dynamic evolution map for each aircraft. The evaluation module uses a dynamic evolution graph to calculate the configuration envelope overlap between the graph node attribute parameters and the collaborative task profile vector; the configuration envelope overlap is determined based on the physical protection effectiveness mapped from real-time impedance spectrum data, the hardware integrity confidence mapped from electrical response frequency feature fingerprints, and the protocol interoperability matrix corresponding to the software and hardware logic version codes. The decision-making instruction module, based on the dynamic evolution map of all aircraft in the fleet, calculates the fleet configuration distribution entropy value, matches the common mode risk influence factor in the preset risk matrix according to the fleet configuration entropy value, and obtains the fleet collaborative dispatch decision instruction set by combining the configuration envelope overlap degree and the common mode risk influence factor.

[0021] Furthermore, the process of acquiring data for each aircraft in the fleet includes: applying a multi-frequency excitation current signal in the 10Hz to 1MHz band to the cable shielding layer using an airborne sensing unit, and generating the real-time impedance spectrum data through fast Fourier transform processing; acquiring the high-frequency transient waveform of the power bus of the replaceable unit at the moment of power-on of the power distribution system, and extracting the power spectral density features to generate the electrical response frequency feature fingerprint; extracting the software and hardware logic version code, including part number, serial number, and software checksum, by listening to the self-test message of the bus, and attaching a timestamp based on the airborne unified clock; receiving multi-dimensional mission instruction packets from the mission planning system, parsing the electromagnetic radiation field strength distribution and interference frequency range of the mission airspace as electromagnetic environment effect level requirements; parsing the maximum instantaneous overload, extreme flight altitude, and minimum turning radius parameters required by the mission as the flight mission envelope constraints; and converging the obtained electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative communication parameters, including the end-to-end delay requirements of the cooperative communication link, to construct a cooperative mission profile vector.

[0022] Specifically, the acquisition of real-time impedance spectrum data is performed by an intelligent acquisition unit deployed at the airborne power distribution center or cable branch node. This unit generates a multi-frequency excitation current signal with a frequency range of 10Hz to 1MHz through a built-in sinusoidal frequency synthesizer, and injects it into the shielding layer of the target cable via a non-contact inductive coupling coil. The synchronous monitoring circuit captures the induced voltage and feedback current signals on the shielding layer in real time. The acquired time-domain analog signals are converted from analog to digital and then sent to a digital signal processor. The fast Fourier transform algorithm is used to calculate the complex impedance at each frequency point, and finally, an impedance spectrum dataset containing amplitude-frequency characteristics and phase-frequency characteristics is generated. In specific implementation, the digital signal processor performs cross-correlation calculation on the acquired induced voltage sequence and feedback current sequence to determine the phase difference between the two in the time domain. Subsequently, the modulus is calculated based on the ratio of the voltage and current amplitudes, and the real and imaginary parts of the complex impedance are calculated in combination with the phase difference, thereby constructing a complete impedance spectrum feature point.

[0023] When generating the electrical response frequency feature fingerprint, the system uses a high-speed data acquisition channel to monitor the power input terminal of the field replaceable unit in real time. When a sudden current caused by a system power-on trigger signal or load switching is detected, the acquisition circuit captures a high-frequency transient waveform sequence with a duration of 500ms at a sampling frequency of not less than 20MHz. The processor performs windowing processing and power spectral density estimation on the captured waveform sequence, extracts the first five resonant peak frequencies in the spectral distribution and their corresponding energy distribution weights, and encapsulates them into a feature vector reflecting the uniqueness of the hardware entity, i.e., the electrical response frequency feature fingerprint. The extraction process adopts dynamic threshold recognition logic, calculates the average noise floor level of the power spectral density and superimposes a preset gain as a judgment threshold, and uses the first derivative discrimination method to capture local maxima points in the spectral curve. If the number of identified peak points exceeds five, they are sorted in descending order of energy amplitude from high to low, and the first five feature points are captured as the fingerprint vector.

[0024] When acquiring the hardware and software logical version codes, the airborne data monitoring interface card listens in real time to the status messages on the ARINC 429 or MIL-STD-1553B avionics bus. According to the preset interface control file definition, the system automatically filters and captures specific ID messages containing subsystem self-test information, and parses out the original hexadecimal fields representing the hardware part number, serial number, and software checksum. The parsed configuration data is sent to the timestamp marking module, which retrieves the airborne synchronization clock based on the IEEE1588 protocol and adds microsecond-level synchronization timestamp information to each set of version data. To address the issue of inconsistent bus data update frequencies, the system aggregates static part number information and dynamic checksum messages through a data frame alignment buffer. Within the same time window, multiple heterogeneous messages are merged into a configuration status description frame with unified time attributes to eliminate the distortion of logical version information caused by bus transmission delay.

[0025] When acquiring electromagnetic environment effect level requirements and flight mission envelope constraints, the mission management computer receives encrypted mission planning instruction packets via a ground-to-air data link. The parsing module first unpacks the data packets according to the protocol specifications, extracting the mission airspace radiation source frequency mapping (e.g., 0.1 GHz to 18 GHz) and peak field strength threshold from the electromagnetic constraint field; then, it parses the maximum allowable axial overload, preset maximum flight ceiling, and minimum turning radius parameters based on the current airspace terrain limitations from the flight plan field, mapping them respectively to electromagnetic boundary values ​​and dynamic physical envelope boundary values ​​within the mission space.

[0026] When constructing the collaborative mission profile vector, the data aggregation module receives the electromagnetic environment effect level requirements, flight mission envelope constraints, and link end-to-end latency requirements obtained from the above analysis. The aggregation module performs normalization processing on the above heterogeneous parameters, converting physical parameters of different dimensions into dimensionless values ​​in the interval [0, 1]. The normalization process is performed based on a pre-stored aircraft performance limit reference table, and the measured field strength values, overload values, and altitude values ​​are mapped to their corresponding design limit limits and mission access lower limits, respectively. For example, the measured overload values ​​are linearly scaled and mapped to the maximum available overload of the aircraft structural design to ensure that physical quantities of different dimensions have comparable weight proportions under the same dimension. The processed values ​​are arranged according to a preset dimensional order (such as electromagnetic, overload, height, delay, etc.), encapsulated into a multi-dimensional floating-point array, forming a collaborative task profile vector that represents the global constraints of the current collaborative task, and stored in a high-speed shared memory for subsequent evaluation logic calls. The collaborative task profile vector also includes a boundary warning coefficient, which is defined according to the security level in the task instruction packet and is used to set the deduction benchmark for the overlap of the configuration envelope in subsequent steps, thereby realizing physical intervention on the task execution permission by changing the control bit state of the output instruction.

[0027] By employing multi-frequency excitation impedance analysis and high-frequency transient fingerprint extraction, quantitative perception of the physical integrity of airborne hardware was achieved, overcoming the limitations of traditional configuration management in real-time monitoring of physical evolution. Combined with bus monitoring and high-precision timestamp synchronization, the accurate correspondence between the software and hardware logic versions and their physical states was ensured. This normalized aggregation of multi-source heterogeneous data provides a high-fidelity, computable data foundation for the digital twin map.

[0028] Furthermore, the process of constructing a real-time configuration dynamic evolution map for each aircraft includes: extracting bill of materials data from the initial design phase of the aircraft, establishing a configuration baseline map containing equipment nodes and logical connection relationships, and defining the nominal electrical characteristic parameters and standard version parameters of each equipment node in the design state; mapping the acquired airborne bus protocol data and hardware / software logical version codes to the logical attribute layer of the baseline map, and updating the real-time version status and communication topology of each equipment node; mapping real-time impedance spectrum data and electrical response frequency feature fingerprints to the physical performance layer of the configuration baseline map, and calculating the physical performance integrity data of each equipment node by comparing the nominal electrical characteristic parameters; using the phase shift in the real-time impedance spectrum data and the checksum changes in the hardware / software logical version codes, comparing the initial configuration baseline topology, and identifying the parameter offset of each equipment node from the initial design state to the current real-time state; combining the parameter offset, the data of the logical attribute layer, and the physical performance layer, and using a time axis association algorithm to sequentially connect the configuration states at different time points to generate a dynamic evolution map.

[0029] The digital twin model refers to a digital simulation model that uses a configuration baseline map constructed from the bill of materials data during the initial design phase of an aircraft as a static base. It maps airborne bus protocol data and hardware / software logic version codes to the logic attribute layer, and real-time impedance spectrum data and electrical response frequency characteristic fingerprints to the physical performance layer. Combined with a parameter offset and time axis correlation algorithm, it sequentially concatenates the configuration states at different time points, forming a model that reflects the aircraft's hardware / software configuration and its evolution path over service life. This model is used in subsequent steps to generate a real-time configuration dynamic evolution map and support mission adaptability assessment.

[0030] When establishing the configuration baseline graph, the system parses the XML or JSON format bill of materials data from the initial aircraft design phase and uses a graph database (such as Neo4j) or adjacency matrix to construct a topology network containing all equipment nodes of the aircraft and their logical connections. The system defines an attribute class object for each equipment node, which includes the nominal electrical characteristic parameters preset during the design phase (such as the reference impedance magnitude and resonant frequency) and the standard version string; the physical cable connections and logical protocol call relationships between nodes are defined as edge attributes in the graph, thus forming a static configuration baseline graph.

[0031] When updating the logical attribute layer, the data processing unit extracts the hardware and software logical version code (including part number, serial number, and checksum) obtained through bus parsing and matches it with nodes in the baseline graph based on the device's unique identifier. Upon successful matching, the system writes the real-time version information into the logical attribute field of the node object. Simultaneously, the system parses the topology heartbeat signal in the bus control message and compares it with the logical connection relationships in the baseline graph. If a new node or logical link interruption is detected, the system dynamically updates the edge status bits in the graph, achieving real-time correction of the communication topology. Specifically, the system establishes a polling mechanism based on a node address routing table. When the topology heartbeat signal of a specific node is lost for more than a preset period (e.g., 3 sampling periods), the system traverses all edge objects associated with that node using a breadth-first search algorithm and changes its status bit from active to disconnected or high-latency, thereby redrawing the communication topology in the graph database in real time.

[0032] When updating the physical performance layer, the system maps the collected real-time impedance spectrum multidimensional vector and electrical response frequency characteristic fingerprint vector to the physical attribute fields of the corresponding nodes in the form of key-value pairs. The calculation logic calls the preset deviation calculation module to perform vector subtraction operation between the measured impedance spectrum vector and the nominal electrical characteristic parameters to obtain the amplitude deviation at each frequency point. Subsequently, the deviation at each frequency point is processed using a weighted average algorithm to generate physical performance integrity percentage data reflecting the health of the equipment, and it is marked on the real-time status bit of the node. The weighted average algorithm adopts a weight allocation strategy based on frequency band sensitivity: high weights (e.g., 0.6) are assigned to impedance points within the operating frequency range of the equipment, and low weights (e.g., 0.4) are assigned to resonant points outside the operating frequency band. The system calculates the ratio of the measured deviation to the upper limit of the design tolerance at the frequency point, and accumulates it in combination with the weight matrix, and finally normalizes the calculation result into a percentage health value.

[0033] When identifying parameter offsets, the system comparison module scans the differences between the current state node and the initial reference node in real time. For physical characteristics, the system extracts phase data from the impedance spectrum and calculates its offset angle relative to the reference phase. For logical characteristics, the system performs a logical comparison of the extracted checksums to identify the version number change level. Specifically, the system pre-sets a version number mapping matrix, defining checksum changes into three levels: patch level (small change), functional level (medium change), and kernel level (large change), and assigning corresponding numerical identifiers (e.g., 0.1, 0.5, and 1.0). This value directly serves as the logical dimension parameter of the offset feature vector, participating in the calculation of subsequent evolution paths. The identified phase offset and version change information are encapsulated into an offset feature vector to characterize the degree of physical and logical drift of the node from the nominal design state to the operational state, and serve as the dynamic feature marker of the node.

[0034] When generating the dynamic evolution map, the system uses a time-axis association algorithm to concatenate configuration state snapshots from different sampling periods according to a unified airborne timestamp order. Since the impedance spectrum sampling frequency is lower than the bus monitoring frequency, the system employs a zero-order hold or linear interpolation algorithm, using the microsecond-level timestamps of the bus data as a benchmark, to align the lower-frequency physical performance layer data. At the end of each sampling window, the system extracts the last physical sample value within the window and combines it with the real-time logical version code to form a joint snapshot, ensuring that the full-aircraft configuration vector at each time-series node is synchronized in the time dimension. The system establishes a pointer index for each aircraft in the time-series database, with each index node pointing to a full-aircraft configuration state vector containing the logical attribute layer, physical performance layer, and parameter offsets. By topologically linking these state vectors along the time dimension, a dynamic evolution map reflecting the continuous change of aircraft configuration status over service time is formed, enabling the visualization, recording, and data storage of configuration status drift paths. At the underlying implementation of data storage, the system adopts an event-triggered differential storage strategy: the time series database does not redundantly store the full vector for each sampling period, but instead the comparison module calculates the spatial Euclidean distance or logical XOR difference between the current state vector and the vector of the previous storage node in real time.

[0035] The system only performs a full data entry operation when the parameter offset exceeds a preset significance threshold (e.g., impedance deviation exceeds 5% or version checksum changes) and is identified as a state abrupt change. For non-abrupt periods during stable service, the system only records the timestamp interval and state retention instructions. Through this incremental recording logic, the system can achieve high-rate compression and lightweight storage of evolution data while maintaining the integrity of the configuration drift path throughout the entire lifecycle, thereby supporting second-level backtracking and visual reconstruction of the configuration state at any historical time point.

[0036] By constructing a dynamic evolution map with both physical and logical mappings, the system achieves a faithful transformation of aircraft configuration status from a static bill of materials to dynamic service characteristics. Utilizing parameter offset identification and time-series correlation algorithms, the system can accurately capture the microscopic drift paths of hardware and software configurations over a long lifecycle. Combined with a lightweight storage strategy for state mutation points, it provides a time-consistent digital foundation for precise dispatching and common-mode risk prediction in fleet collaboration.

[0037] Furthermore, the process of calculating the overlap of the configuration envelope between the attribute parameters of the graph nodes and the collaborative task profile vector includes: inputting real-time impedance spectrum data into the electromagnetic shielding effectiveness mapping matrix to calculate the electromagnetic coupling attenuation coefficient under different frequency nodes; using the electrical response frequency feature fingerprint and the pre-stored standard feature template to perform cross-correlation similarity calculation to determine the hardware integrity confidence; retrieving the software and hardware dependency table to determine the protocol compatibility parameters; using the electromagnetic coupling attenuation coefficient, hardware integrity confidence, and protocol compatibility parameters as feature vectors of coordinate axis dimensions to construct a feature space lattice in multidimensional Euclidean space; extracting electromagnetic environment effect level requirements, flight mission envelope constraints, and collaborative communication constraint parameters from the collaborative task profile vector, and fitting and generating electromagnetic safety boundary surfaces, dynamic load limiting surfaces, and logical interoperability boundary surfaces in multidimensional Euclidean space respectively, and enclosing them to form a closed hyperspace; calculating the lattice density distribution of the configuration feature space lattice inside the closed hyperspace in multidimensional Euclidean space, and performing a ratio calculation with the total density of the configuration feature space lattice to obtain the configuration envelope overlap.

[0038] When quantifying electromagnetic protection performance, the system inputs the acquired real-time impedance spectrum data into a pre-stored electromagnetic shielding effectiveness mapping matrix. This matrix stores experimental correlation data between impedance physical quantities and electromagnetic loss in decibels. The mapping matrix pre-sets frequency domain reference impedance curves for different shielding materials under standard healthy conditions. By calculating the difference between the real-time impedance magnitude and the reference value, it maps to the corresponding shielding effectiveness attenuation gradient; for example, for every preset unit shift in impedance, the corresponding shielding effectiveness decreases by a specific decibel. Through numerical lookup and linear interpolation, the system converts the impedance magnitude sequence in the frequency domain into electromagnetic coupling attenuation coefficients at different frequency nodes, thus completing the quantification mapping of physical electrical signals to electromagnetic protection capabilities.

[0039] When evaluating the hardware and protocol status, the system utilizes cross-correlation similarity calculation logic to perform time-frequency domain alignment and comparison between the real-time acquired electrical response frequency feature fingerprint and the standard feature template of the design baseline state. By calculating the cross-correlation peak values ​​of the two sets of sequences, the hardware integrity confidence score is determined. This is specifically implemented through piecewise function mapping logic: the cross-correlation peak coefficient is divided into multiple confidence intervals, and each interval is assigned a corresponding weight score. In this embodiment, when the peak coefficient is higher than a first preset threshold, the confidence score is linearly mapped to a high segment; when it is lower than a second preset threshold, the score decays exponentially to amplify the impact of hardware damage on the final overlap. Simultaneously, the system retrieves a preset hardware and software dependency table and uses a logical matching algorithm to check whether the calling interfaces and communication protocols of the current hardware and software version code meet preset compatibility constraints, outputting discretized protocol compatibility parameters.

[0040] When constructing the feature space lattice, the system uses the aforementioned electromagnetic coupling attenuation coefficient, hardware integrity confidence level, and protocol compatibility parameters as coordinate axes in a three-dimensional Euclidean space. The system employs Monte Carlo sampling to generate a large number of discrete coordinate points following a normal distribution within the error range of the measured parameters. This error range is defined by the measurement accuracy tolerance of the airborne sensing unit. The system uses the mean of the real-time measured feature vectors and the nominal measurement standard deviation of the sensing unit as the normal distribution parameter to generate a cluster of points characterizing measurement uncertainty. These coordinate points form a feature space lattice in multidimensional space, characterizing the distribution of the aircraft's integrated configuration capabilities, and are used to reflect the probabilistic statistical characteristics of the configuration state.

[0041] When fitting the closed hyperspace of the task requirements, the system extracts the field strength requirement, overload limit, and communication delay parameters from the collaborative task profile vector. A radial basis function fitting algorithm is used, with the Gaussian radial basis function as the core operator. The system normalizes the physical constraint values ​​in the task vector to align them with the range of the feature space coordinate system. Using multiple constraint boundary sampling points as support centers, the system determines the weight coefficients by solving a system of linear equations, thereby fitting a continuous boundary surface in Euclidean space. The system calculates the electromagnetic safety boundary surface, dynamic load limiting surface, and logical interoperability boundary surface that meet the task requirements in the same multidimensional Euclidean space. Subsequently, through geometric Boolean intersection logic, the system introduces a coordinate axis limit plane as an auxiliary boundary to close the envelope region formed by the electromagnetic, load, and logical boundary surfaces and the origin of the coordinate axes. This ensures that the generated hyperspace has a finite and computable volume, enclosing the aforementioned surfaces to form a closed hyperspace characterizing the task admission requirements.

[0042] When calculating the overlap value, the system invokes the logic for determining whether a point lies within the polyhedron. It iterates through each discrete coordinate point in the feature space lattice and checks whether it lies within the boundary of the closed hyperspace. The system counts the number of points falling within the space and the total number of points, then calculates the ratio between the two to determine the distribution density ratio of the configuration feature space lattice in the task requirement space. This density ratio is defined as the configuration envelope overlap, and is stored in memory as the quantitative basis for the final dispatch decision.

[0043] By mapping physical performance, logical version, and task constraints to a unified multidimensional Euclidean space, quantitative alignment of heterogeneous configuration states and complex task requirements is achieved. Utilizing the overlap calculation of feature point distribution and closed hyperspace, the uncertainty of manual qualitative judgment in traditional assessments is effectively avoided. This objectively reflects the limiting effect of physical performance degradation on task execution boundaries, providing scientific mathematical support and decision-making criteria for precise aircraft dispatch in complex collaborative environments.

[0044] Further, the process of matching common-mode risk impact factors in a preset risk matrix based on fleet configuration entropy values ​​includes: aggregating the dynamic evolution map of all aircraft in the fleet, extracting multi-dimensional configuration parameters including hardware production batches, software patch verification, and normalized physical performance deviations, and constructing configuration feature vectors representing the technical status of individual aircraft; within the multi-dimensional feature distribution space, using feature topological similarity recognition logic, classifying the configuration feature vectors, identifying aircraft groups with common technical characteristics, and calculating the distribution probability values ​​of each aircraft group in the total fleet sample; introducing information theory metric logic, performing weighted product operations on each distribution probability value and its corresponding logarithmic value, and taking the negative of the sum of each product to generate fleet configuration distribution entropy values; if the entropy value is lower than a preset low threshold, it is determined to be a common-mode failure risk caused by configuration homogenization, and the corresponding first risk correction coefficient is extracted from the logic table; if the entropy value is higher than a preset high threshold, the corresponding second risk correction coefficient is extracted from the logic table; using the first risk correction coefficient or the second risk correction coefficient as a common-mode risk impact factor, the specific process is as follows: Figure 3 As shown.

[0045] When constructing configuration feature vectors, the data processing module first retrieves the real-time dynamic evolution map of all aircraft in the fleet and performs deterministic data alignment preprocessing. For discrete labels such as hardware production batches, the system uses one-hot encoding to convert them into fixed-length binary sparse vectors, ensuring equal distances between different batches in Euclidean space. For continuous values ​​such as physical performance deviations, the system uses a max-min normalization method to map them to a closed interval between 0 and 1. For software patch checksums, the system extracts the last four hexadecimal digits of the hash value and converts it to a decimal value. The system then normalizes this decimal value by dividing it by the theoretical maximum value, mapping it to the interval between 0 and 1. This step eliminates significant differences in numerical magnitude between features of different dimensions, preventing large numerical features from masking the weight of small numerical features in Euclidean distance calculations. The system concatenates the processed values ​​from the three dimensions to generate a configuration feature vector for each aircraft with uniform dimension and aligned dimensions.

[0046] When identifying aircraft groups, the system executes a density-based noise-based spatial clustering algorithm within a multi-dimensional feature space. The system first defines two core hyperparameters: a preset airworthiness tolerance radius is set as the neighborhood scan radius, and the minimum statistical significance number of the group (e.g., 3 aircraft) is set as the core object threshold. Subsequently, the system calculates the Euclidean distance between any two configuration feature vectors. The algorithm iterates through all sample points. If the neighborhood scan radius of a sample point minus the number of samples within its neighborhood is greater than or equal to the core object threshold, it is marked as a core object, and a new technical commonality cluster is created. Through density reachability propagation logic, the system aggregates all density-connected core objects and their boundary points into the same cluster, forming an aircraft group with technical commonality. For free-floating sample points that cannot be assigned to any cluster, the system marks them as independent discrete configurations. When calculating the probability distribution, the system counts the number of aircraft in each technical commonality cluster and treats all samples marked as independent discrete configurations as independent single-sample clusters for calculation (or merges them into a heterogeneous group). Finally, the system divides the number of samples in each cluster by the total number of samples in the fleet to obtain the probability distribution values ​​of each aircraft family and discrete configuration in the total sample.

[0047] When calculating the entropy value of the fleet configuration distribution, the processor executes the normalized information entropy calculation logic. The system traverses all identified aircraft groups, calculates the product of the distribution probability value of each group and its base-2 logarithmic value, sums all the product results and takes the opposite number to obtain the original entropy value. In order to eliminate the impact of fleet size fluctuations (such as the increase or decrease of the number of aircraft) on the risk assessment benchmark, the system calculates the base-2 logarithmic value of the total number of currently identified groups as the theoretical maximum entropy value. The system uses the original entropy value to divide the theoretical maximum entropy value and outputs the normalized fleet configuration distribution entropy value, which is constant in the range of 0 to 1.

[0048] When matching common-mode risk impact factors, the decision engine uses a piecewise linear interpolation algorithm to call a pre-set risk matrix logic table. This table uses normalized entropy as the index axis and presets multiple discrete gradient nodes corresponding to low-entropy homogeneity risk and high-entropy heterogeneity risk. The system first determines the interval of the current entropy value: if the entropy value is lower than 0.3, the system locks the homogeneity risk interval and retrieves the preset coefficients at both ends of the interval. The preset coefficients for the homogeneity risk interval show a negative correlation trend (i.e., the lower the entropy value, the higher the risk coefficient, up to a maximum of 2.0); the preset coefficients for the heterogeneity risk interval show a positive correlation trend (i.e., the higher the entropy value, the higher the synergistic loss coefficient, up to a maximum of 1.5). Using the linear proportion of the current entropy value between the two ends, the first risk correction coefficient is calculated; if the entropy value is higher than 0.7, the system locks the heterogeneity risk interval and uses the same interpolation logic to calculate the second risk correction coefficient; if the entropy value is between 0.3 and 0.7, the system outputs a default unit coefficient of 1.0.

[0049] When generating the common-mode risk impact factor, the first risk correction coefficient or the second risk correction coefficient calculated above is directly defined as the common-mode risk impact factor. This factor is formatted as a double-precision floating-point number and written into the memory register of the task readiness calculation module in real time.

[0050] By performing one-hot encoding and normalization preprocessing, the dimensional differences between discrete batch labels and continuous physical deviations are eliminated, ensuring equal-weighted fusion of heterogeneous features in Euclidean space. Employing the density-based DBSCAN clustering algorithm, the system can adaptively divide technical clusters and identify discrete configurations based on the airworthiness tolerance radius, overcoming the prior limitation of traditional algorithms requiring a pre-set number of classifications. Combining normalized entropy calculation with smooth interpolation retrieval logic effectively mitigates computational oscillations caused by fleet size fluctuations and threshold edge jumps.

[0051] Furthermore, the process of obtaining the fleet collaborative dispatch decision instruction set includes: comparing the acquired common-mode risk impact factors with a pre-set risk level discrete mapping table, identifying the numerical range to which the impact factors belong and extracting the corresponding gradient deduction weights; using the gradient deduction weights to perform proportional reduction calculations on the configuration envelope overlap to obtain a quantitative task readiness score; real-time retrieval of a pre-set minimum equipment list database and airworthiness directive database, performing a logical XOR operation between the dynamic evolution map features and the prohibition dispatch criteria in the database; if the calculation result shows a logical conflict, then the corresponding task readiness score is set to zero, and the corresponding aircraft access is blocked. The process involves: identifying aircraft; combining the task readiness scores of all aircraft in the fleet with the task urgency parameters in the collaborative task profile vector; using conflict resolution logic to rank candidate aircraft in multiple dimensions and generating an aircraft scheduling candidate sequence; extracting hardware batches and software checksums from the real-time configuration dynamic evolution map of selected aircraft to generate configuration access feature labels; calculating physical access control bit parameters based on the deviation between the configuration envelope overlap and the physical boundary; encapsulating the aircraft access identifier, configuration access feature labels, and physical access control bit parameters to generate and issue a fleet collaborative dispatch decision instruction set; and releasing the aircraft by executing instructions through the airborne physical interface.

[0052] When calculating the quantized task readiness score, the processor first reads the common-mode risk impact factor (floating-point format) stored in memory. The system then calls the risk level discrete mapping table pre-installed in read-only memory. This table divides the risk factor values ​​into several continuous closed intervals and uniquely associates each interval with a preset gradient subtraction weight constant. The system uses a numerical comparison algorithm to locate the interval index to which the current risk factor belongs and extracts the corresponding weight constant. Subsequently, the arithmetic logic unit performs a scaling operation: first, it calculates the difference between the unit value and the gradient subtraction weight; then, it multiplies this difference with the configuration envelope overlap value calculated in the previous step. The final product is the quantized task readiness score, which is stored in a temporary decision cache.

[0053] During airworthiness audits, the system loads the minimum equipment list database and airworthiness directive database from the airborne maintenance system in real time via the data bus. The audit module converts the status bits of key hardware nodes and software version checksums in the current aircraft's dynamic evolution graph into a fixed-length binary feature sequence. Simultaneously, the system converts the prohibition criteria defined in the database for the current mission into a standard compliance status template of the same length. This template defines the ideal binary state that all key hardware nodes should have under the current mission (e.g., all zeros indicate no fault). The processor performs a bitwise XOR operation on the real-time feature sequence and the standard mask sequence. If the operation result shows a logical conflict at a key position (e.g., a status bit that should be closed is shown as a difference after the XOR operation), the system immediately triggers a high-priority interrupt logic, forcibly overwrites the mission readiness score in the buffer to zero, and sets the aircraft's access identification variable to a "locked" state.

[0054] When generating the scheduling candidate sequence, the system constructs a multidimensional array containing data on all aircraft in the fleet. The sorting engine extracts the task readiness score for each aircraft and the task urgency parameter parsed from the collaborative task profile vector. The system executes a weighted summation logic, multiplying the readiness score and urgency parameter by preset weight coefficients and then summing them to obtain the comprehensive scheduling index. Subsequently, the system uses a quicksort algorithm to arrange the aircraft identifiers in descending order of the comprehensive scheduling index, generating a linear aircraft scheduling candidate sequence and automatically removing entries with the "blocked" admission status.

[0055] When generating control parameters, the system extracts the hardware production batch code and software version checksum from the selected aircraft atlas, and combines them using a hash algorithm to generate a unique configuration access feature label. Simultaneously, the system calculates the numerical deviation between the real-time configuration envelope overlap and the mission physical boundary. Using a preset step quantization logic, the system maps this continuous deviation floating-point value to discrete level index integers (in this embodiment, deviation values ​​of 0-5% are mapped to index 0, and 5%-10% are mapped to index 1). The system uses this deviation value as an index to look up the corresponding binary control code (e.g., a 16-bit or 32-bit integer) in a preset control logic truth table. Each bit of this code strictly corresponds to the hardware enable register state of a specific subsystem (such as fire control radar or encrypted data link) in the airborne mission computer. This binary code is defined as the physical access control bit parameter.

[0056] During the command issuance phase, the system, in accordance with avionics data bus protocol specifications (such as ARINC 429), packages the aircraft access identifier, configuration access feature label, and physical access control bit parameters into a fleet collaborative dispatch decision command set data frame. This data frame is uploaded to the onboard mission management computer via a wireless data link. During the system boot and loading phase, the onboard computer parses this command set and directly writes the "physical access control bit parameters" into the control register of the underlying driver. For subsystems whose control bits are displayed as "locked," the driver intercepts and releases unauthorized physical-level commands by cutting off the relay control signals of their physical power supply circuits or blocking their bus communication ports.

[0057] By establishing a quantitative deduction mechanism for risk gradients and an XOR audit mechanism for airworthiness characteristics, and by directly mapping physical access control bit parameters to the underlying airborne drive, the system can automatically cut off the physical power supply or communication circuit of the subsystem through discrete interfaces when the configuration state or risk indicators fail to meet the standards. This mathematical logic-based forced blocking strategy effectively eliminates flight safety hazards caused by human error in dispatching or operating with defects. While ensuring the physical compliance of collaborative mission execution, it achieves optimal allocation of fleet resources under complex constraints through multi-dimensional priority ranking.

[0058] Furthermore, it also includes: during and after the aircraft performs a mission, synchronously collecting the measured impedance variation sequence and the measured profile of the mission load through the airborne maintenance system; calculating the deviation residuals of the measured feature feedback data and the predicted features in the dynamic evolution map; constructing a parameter sensitivity matrix using the residual values ​​and flight mission envelope constraints; performing reverse calibration compensation on the physical performance aging reference parameters according to the parameter sensitivity matrix; if the corrected deviation residuals exceed the preset health warning threshold, automatically triggering the preventive maintenance recommendation label for the corresponding node in the real-time configuration dynamic evolution map, and synchronously updating the configuration risk weight of the aircraft in subsequent missions; extracting the reverse calibration compensation parameters of a single aircraft as common evolution features and synchronizing them to the dynamic evolution map of all aircraft in the fleet.

[0059] During the aircraft's mission, the onboard maintenance system initiates a parallel data acquisition process. On one hand, it records the measured impedance variation sequence throughout the entire flight using non-contact sensors distributed in the cable shielding at a preset high-frequency sampling rate (e.g., 1kHz). On the other hand, it reads the real-time status words from the flight control computer via the ARINC 429 bus, recording the measured profile of the mission load, including instantaneous overload, vibration amplitude, and cabin temperature. After the mission, the data processing unit uses the unified timestamp provided by the Global Positioning System to perform millisecond-level time-domain alignment of the impedance and load data, generating a joint dataset containing three-dimensional attributes of "time-load-impedance".

[0060] When calculating the deviation residuals, the processor retrieves the dynamic evolution map of the aircraft at the previous time node from local memory and extracts the predicted feature vector (i.e., the nominal impedance curve) generated based on the theoretical model. Using a point-to-point differential algorithm, the measured impedance variation sequence and the predicted feature vector are subtracted at the corresponding time points to generate the original residual sequence. To eliminate sensor noise interference, the system performs a moving average filtering process on the original residual sequence to extract the steady-state residual value that can characterize the true drift trend of physical performance.

[0061] When constructing the parameter sensitivity matrix, the system establishes a multidimensional Jacobian matrix model. The row vectors of this matrix are defined as environmental stress variables (such as maximum overload and cumulative high temperature duration) in the flight mission envelope constraints, and the column vectors are defined as physical performance aging benchmark parameters (such as insulation aging rate and contact resistance growth rate). The system quantifies the contribution of specific load conditions to physical performance degradation by calculating the partial derivatives of the steady-state residual values ​​with respect to each environmental stress variable. These partial derivative values ​​are then filled into the matrix elements to form a parameter sensitivity matrix that describes the nonlinear mapping relationship between "environmental stress and physical degradation".

[0062] During reverse calibration, the calibration module uses the parameter sensitivity matrix as a gain operator, applying it to the physical performance aging baseline parameters in the dynamic evolution graph. Based on the positive or negative polarity of the residuals, proportional-integral control logic dynamically corrects the aging slope: if the measured decay is faster than the predicted value, the aging rate coefficient is increased according to the sensitivity weight. After correction, the system compares the updated absolute value of the residuals with a pre-stored health warning threshold. If the threshold is exceeded, the system writes a "preventive maintenance required" status code to the metadata of the corresponding node in the graph and automatically increases the configuration risk weight value of that node in the next task cycle.

[0063] During the fleet synchronization phase, the ground server receives the reverse calibration compensation parameters uploaded by individual aircraft. The server runs a statistical significance test algorithm (such as a T-test) to analyze whether the compensation parameter has statistical universality in other aircraft of the same batch or model. If it is determined to be a common evolutionary feature, the server encapsulates the parameter into a fleet configuration update patch package. This patch package is broadcast to all aircraft in the fleet via the air-to-ground data link or ground maintenance terminal. After receiving the patch package, each airborne system parses it and synchronously corrects the basic aging model parameters in the local dynamic evolution map, realizing a closed-loop iteration from individual aircraft experience to the fleet collective knowledge base.

[0064] By establishing a closed-loop calibration mechanism based on measured loads and impedance residuals, the dynamic evolution spectrum was iteratively modified from static theoretical prediction to dynamic adaptive correction. The physical aging benchmark was compensated in reverse using a parameter sensitivity matrix, effectively eliminating prediction biases of the general model under specific service environments. Combined with a synchronous distribution strategy based on fleet common characteristics, the aging samples of individual aircraft were transformed into evolutionary knowledge of the entire fleet, enhancing the fleet's ability to predict potential physical degradation risks during long-term service.

[0065] By constructing a closed-loop technology system from physical-level perception to fleet collaborative decision-making, and utilizing the deep fusion of real-time impedance spectral fingerprinting and multi-dimensional evolutionary maps, the challenge of quantifying and aligning the actual state of software and hardware with task constraints in complex environments has been solved. By introducing fleet configuration entropy and physical access locking mechanisms, systemic common-mode risks are effectively prevented while strengthening the execution rigidity of airworthiness compliance. This data-driven decision-making framework not only significantly improves the accuracy and safety of single-aircraft dispatch but also enhances the overall operational support efficiency and mission responsiveness of the fleet over its long lifecycle through closed-loop calibration and knowledge synchronization across fleets.

[0066] Example 2: Before the mission was issued, the system applied a multi-frequency excitation current of 10Hz to 1MHz to the cable shielding layer of aircraft A-001 through the airborne sensing unit. The measured impedance spectrum data showed a 15% amplitude shift in the high-frequency band, indicating potential fatigue in the shielding layer. At the same time, the system extracted the current flight control software checksum as 0xAF52 by listening to the bus messages and added a microsecond-level timestamp. The mission planning system returned the collaborative mission profile vector, which specified that the peak electromagnetic radiation field strength in the mission airspace was 50V / m and required that the maximum instantaneous overload not exceed 7G.

[0067] The system inputs the aforementioned physical impedance data, software checksum, and hardware fingerprint into a pre-set digital twin model. The model is based on the aircraft's initial design bill of materials and maps the 15% impedance deviation to the physical performance layer. It identifies the offset of the physical integrity parameters of the equipment node relative to the nominal state. Using a time axis correlation algorithm, the system generates a dynamic evolution map of the configuration of aircraft A-001 from its entry into service to the present, clearly showing the trajectory of the shielding effectiveness decaying with the increase of flight hours.

[0068] The system inputs real-time impedance data into the electromagnetic shielding effectiveness mapping matrix and calculates the electromagnetic coupling attenuation coefficient at the node frequency. In the multidimensional Euclidean space, the system constructs a feature space lattice composed of "hardware integrity confidence", "protocol compatibility" and "shielding effectiveness". At the same time, a closed hyperspace is generated according to the mission requirements. The calculation shows that due to impedance offset, only 82% of the feature lattice of unit A-001 falls within the closed hyperspace required by the mission, resulting in a configuration envelope overlap of 0.82.

[0069] The system aggregated the evolution maps of 12 aircraft in the fleet and found that 10 of them used the same batch of shielded cables, resulting in extremely low entropy values ​​in the fleet configuration distribution (severe homogenization). The risk matrix automatically matched the first risk correction coefficient (common mode risk impact factor) to 0.9. The system performed a proportional reduction calculation between the overlap degree of 0.82 and the impact factor of 0.9, and obtained a final mission readiness score of 0.738. Since the score met the standard and the minimum equipment list prohibition criterion was not triggered, the system generated an instruction set and issued physical access control bit parameters to forcibly lock non-essential civilian frequency band interfaces to ensure system safety in a high-intensity electromagnetic environment.

[0070] After the mission, the airborne maintenance system synchronously transmitted back the measured impedance variation sequence and the measured profile under 7G overload during the mission. The system calculated the residual between the measured data and the predicted features of the spectrum and found that high overload accelerated impedance drift. Using this residual, a parameter sensitivity matrix was constructed. The system reverse-calibrated the physical performance aging benchmark parameters and synchronized this "high overload accelerated aging" feature to the entire fleet spectrum, realizing rapid updating of cluster knowledge.

[0071] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital twin-based airborne system full lifecycle configuration and status management system, characterized in that, include: The perception module acquires in real time the airborne bus protocol data, hardware and software logic version codes, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative mission profile vectors for each aircraft in the fleet. The map construction module inputs airborne bus protocol data, real-time impedance spectrum data, electrical response frequency characteristic fingerprints, and software and hardware logic version codes into a pre-set digital twin model to construct a real-time configuration dynamic evolution map for each aircraft. The evaluation module uses a dynamic evolution graph to calculate the configuration envelope overlap between the graph node attribute parameters and the collaborative task profile vector; the configuration envelope overlap is determined based on the physical protection effectiveness mapped from real-time impedance spectrum data, the hardware integrity confidence mapped from electrical response frequency feature fingerprints, and the protocol interoperability matrix corresponding to the software and hardware logic version codes. The decision-making instruction module, based on the dynamic evolution map of all aircraft in the fleet, calculates the fleet configuration distribution entropy value, matches the common mode risk influence factor in the preset risk matrix according to the fleet configuration entropy value, and obtains the fleet collaborative dispatch decision instruction set by combining the configuration envelope overlap degree and the common mode risk influence factor.

2. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, The process of acquiring data for each aircraft in the fleet includes: applying multi-frequency excitation current signals to the cable shielding layer using the airborne sensing unit, and generating the real-time impedance spectrum data through fast Fourier transform processing; acquiring the high-frequency transient waveform of the power bus of the replaceable unit at the moment of power-on of the power distribution system, and extracting the power spectral density features to generate the electrical response frequency feature fingerprint; extracting the software and hardware logic version code, including part number, serial number, and software checksum, by listening to the self-test message of the bus, and attaching a timestamp based on the airborne unified clock; receiving multi-dimensional mission instruction packets from the mission planning system, parsing the electromagnetic radiation field strength distribution and interference frequency range of the mission airspace as electromagnetic environment effect level requirements; parsing the maximum instantaneous overload, extreme flight altitude, and minimum turning radius parameters required by the mission as flight mission envelope constraints; and converging the obtained electromagnetic environment effect level requirements, flight mission envelope constraints, and cooperative communication parameters, including the end-to-end delay requirements of the cooperative communication link, to construct a cooperative mission profile vector.

3. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, The process of constructing a real-time configuration dynamic evolution map for each aircraft includes: extracting bill of materials data from the initial design phase of the aircraft, establishing a configuration baseline map containing equipment nodes and logical connection relationships, and defining the nominal electrical characteristic parameters and standard version parameters of each equipment node in the design state; mapping the acquired airborne bus protocol data and hardware / software logical version codes to the logical attribute layer of the baseline map, and updating the real-time version status and communication topology of each equipment node; mapping real-time impedance spectrum data and electrical response frequency characteristic fingerprints to the physical performance layer of the configuration baseline map, and calculating the physical performance integrity data of each equipment node by comparing the nominal electrical characteristic parameters; using the phase shift in the real-time impedance spectrum data and the checksum changes in the hardware / software logical version codes, comparing the initial configuration baseline topology, and identifying the parameter offset of each equipment node from the initial design state to the current real-time state; combining the parameter offset, the data from the logical attribute layer, and the physical performance layer, and using a time axis association algorithm to sequentially connect the configuration states at different time points to generate a dynamic evolution map.

4. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, The process of calculating the overlap of the configuration envelope between the attribute parameters of the spectrum nodes and the collaborative task profile vector includes: inputting real-time impedance spectrum data into the electromagnetic shielding effectiveness mapping matrix to calculate the electromagnetic coupling attenuation coefficient at different frequency nodes; using the electrical response frequency feature fingerprint and the pre-stored standard feature template to perform cross-correlation similarity calculation to determine the hardware integrity confidence; retrieving the software and hardware dependency table to determine the protocol compatibility parameters; using the electromagnetic coupling attenuation coefficient, hardware integrity confidence, and protocol compatibility parameters as feature vectors of coordinate axis dimensions to construct a feature space lattice in multidimensional Euclidean space; extracting electromagnetic environment effect level requirements, flight mission envelope constraints, and collaborative communication constraint parameters from the collaborative task profile vector, and fitting and generating electromagnetic safety boundary surfaces, dynamic load limitation surfaces, and logical interoperability boundary surfaces in multidimensional Euclidean space respectively, and enclosing them to form a closed hyperspace; calculating the lattice density distribution of the configuration feature space lattice inside the closed hyperspace in multidimensional Euclidean space, and performing a ratio calculation with the total density of the configuration feature space lattice to obtain the configuration envelope overlap.

5. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, The process of matching common-mode risk impact factors in a preset risk matrix based on fleet configuration entropy values ​​includes: aggregating the dynamic evolution map of all aircraft in the fleet, extracting multi-dimensional configuration parameters including hardware production batches, software patch verification, and normalized physical performance deviations, and constructing configuration feature vectors representing the technical status of individual aircraft; within the multi-dimensional feature distribution space, using feature topological similarity recognition logic, classifying the configuration feature vectors, identifying aircraft groups with common technical characteristics, and calculating the distribution probability values ​​of each aircraft group in the total fleet sample; introducing information theory metric logic, performing weighted product operations on each distribution probability value and the corresponding logarithmic value, and taking the negative of the sum of each product to generate fleet configuration distribution entropy values; if the entropy value is lower than a preset low threshold, it is determined to be a common-mode failure risk caused by configuration homogenization, and the corresponding first risk correction coefficient is extracted from the logic table; if the entropy value is higher than a preset high threshold, the corresponding second risk correction coefficient is extracted from the logic table; and the first or second risk correction coefficient is used as a common-mode risk impact factor.

6. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, The process of obtaining the fleet coordinated dispatch decision instruction set includes: comparing the acquired common-mode risk impact factors with a pre-set risk level discrete mapping table, identifying the numerical range to which the impact factors belong and extracting the corresponding gradient deduction weights; using the gradient deduction weights to perform proportional reduction calculations on the configuration envelope overlap to obtain a quantitative task readiness score; real-time retrieval of a pre-set minimum equipment list database and airworthiness directive database, performing a logical XOR operation between the dynamic evolution map features and the prohibition dispatch criteria in the database; if the calculation result shows a logical conflict, the corresponding task readiness score is set to zero and the corresponding aircraft access identifier is blocked. Combining the mission readiness scores of all aircraft in the fleet with the mission urgency parameters in the collaborative mission profile vector, candidate aircraft are sorted in multiple dimensions using conflict resolution logic to generate an aircraft scheduling candidate sequence; hardware batches and software checksums are extracted from the real-time configuration dynamic evolution map of the selected aircraft to generate configuration access feature labels; physical access control bit parameters are calculated based on the deviation between the configuration envelope overlap and the physical boundary; aircraft access identifiers, configuration access feature labels, and physical access control bit parameters are encapsulated to generate a fleet collaborative dispatch decision instruction set and issued; and clearance is granted by executing instructions through the airborne physical interface.

7. The airborne system full lifecycle configuration and status management system based on digital twin as described in claim 1, characterized in that, Also includes: During and after an aircraft performs a mission, the onboard maintenance system synchronously collects the measured impedance variation sequence and the measured profile of the mission load. The measured feature data is then compared with the predicted features in the dynamic evolution graph to calculate the deviation residual. A parameter sensitivity matrix is ​​constructed using the residual values ​​and the flight mission envelope constraints. Based on the parameter sensitivity matrix, reverse calibration compensation is performed on the physical performance aging reference parameters. If the corrected deviation residual exceeds a preset health warning threshold, a preventative maintenance recommendation label is automatically triggered for the corresponding node in the real-time configuration dynamic evolution graph, and the configuration risk weight of the aircraft in subsequent missions is updated synchronously. The reverse calibration compensation parameters of a single aircraft are extracted as common evolutionary features and synchronized to the dynamic evolution map of the entire fleet of aircraft.

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