Civil aviation operation safety assessment platform and method

Abstract

The invention discloses a civil aviation operation safety assessment platform and method, and relates to the technical field of civil aviation operation safety assessment, WiFi signal interference risk assessment and hierarchical regulation are realized through cooperation of multiple modules, an electronic interference atlas module acquires key parameters, quantifies crosstalk probability and preliminarily judges the risk in combination with a threshold value and a sensor state, and the risk is evaluated by a WiFi signal. The signal coupling entropy analysis module calculates dynamic coupling entropy based on signal variables and monitors interaction risks in real time, the digital twin simulation module fuses historical data to evaluate the jump probability of a display and early warns potential threats in advance, and the safety margin optimization module implements three-level regulation and control according to risk levels. According to the method, an acquisition-analysis-simulation-regulation closed-loop system is constructed, interference can be accurately quantified, abnormity can be captured in real time, a strategy can be dynamically adjusted, civil aviation operation safety and regulation efficiency in a complex electromagnetic environment are improved, system fault risks caused by signal crosstalk are reduced, and an efficient scheme is provided for intelligent safety assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of civil aviation operation safety assessment, and in particular to a civil aviation operation safety assessment platform and method. Background Art

[0002] With the acceleration of the digitalization of civil aviation and the popularization of wireless devices such as in-flight WiFi, their signal crosstalk poses a potential threat to the safety of avionics systems. Traditional assessment methods are difficult to accurately quantify dynamic interference risks and lack multi-dimensional coupling analysis and real-time control mechanisms. Against this background, a civil aviation operation safety assessment platform based on electronic interference maps, signal coupling entropy analysis, digital twin simulation and other technologies has emerged, aiming to solve the problems of operation safety assessment and hierarchical control in complex electromagnetic environments.

[0003] The existing civil aviation operation safety assessment platforms and methods have the following technical problems: 1. In terms of signal interference assessment, traditional civil aviation operation safety assessment methods often rely on experience or single parameter judgment and lack systematic quantitative analysis. For example, when evaluating the interference of WiFi signals on avionics systems, they simply monitor the WiFi signal strength without considering key parameters such as the signal transmission antenna gain and the reflection path length. For example, during a certain flight, because a passenger used a portable WiFi device, the traditional assessment system only determined that there was no risk based on the signal strength not exceeding the threshold, but ignored the signal reflection superposition caused by the cabin structure, which ultimately caused a brief abnormality in the avionics system, exposing the one-sidedness and inaccuracy of the traditional method in evaluating complex interference scenarios.

[0004] 2. In the dynamic monitoring and risk prediction stages, traditional civil aviation operational safety assessment methods mostly use static monitoring of a single point or a few parameters, which makes it difficult to capture the dynamic changes in signal interactions. Taking the interaction monitoring between a certain aircraft model's avionics system and onboard WiFi as an example, traditional methods only record the instantaneous values ​​of signal frequency and amplitude. When intermittent, time-varying interference occurs, such as frequency drift of the WiFi signal due to device channel switching, traditional methods are unable to promptly identify such subtle changes, resulting in the failure to provide early warning of interference risks. Passive responses are not made until the avionics system displays an abnormality, seriously affecting the timeliness and effectiveness of risk prevention and control.

[0005] 3. At the safety control execution level, traditional assessment methods lack a refined, hierarchical control mechanism. For example, when suspected signal interference is detected, traditional methods may directly shut down all onboard Wi-Fi devices, which not only seriously affects the passenger experience but also leads to idle flight communication system resources due to excessive control, resulting in resource waste. Alternatively, when faced with high-risk interference, only minor measures such as reducing Wi-Fi power are taken, which cannot effectively eliminate the threat posed by interference to the avionics system. This makes safety control mismatched with the actual risk level and makes it difficult to strike a balance between ensuring safety and maintaining system performance. Summary of the Invention

[0006] The purpose of the present invention is to provide a civil aviation operation safety assessment platform and method, which solve the problems existing in the background technology.

[0007] To solve the above technical problems, the present invention adopts the following technical solution: The present invention provides a civil aviation operation safety assessment platform, including: an electronic interference spectrum construction module for collecting WiFi signal crosstalk information of a specified civil aircraft during operation, and then quantifying the probability of WiFi signal crosstalk on the avionics system, thereby evaluating whether the probability of crosstalk occurrence meets operational safety requirements and the display status of onboard sensors.

[0008] The signal coupling entropy analysis module is used to evaluate the dynamic coupling between WiFi data streams and the corresponding avionics signals of a specified civil aircraft.

[0009] The digital twin multi-state simulation module is used to evaluate the jump risk probability of the corresponding primary flight display of a specified civil aircraft in the superposition state.

[0010] The safety margin optimization module is used to implement hierarchical safety control within a specified civil aircraft.

[0011] In a second aspect, the present invention provides a civil aviation operation safety assessment method, including: Step 1, constructing an electronic interference map: collecting WiFi signal crosstalk information of a specified civil aircraft during operation, and then quantifying the probability of WiFi signal crosstalk on the avionics system, thereby evaluating whether the probability of crosstalk meets operational safety requirements and the display of onboard sensors.

[0012] Step 2: Signal Coupling Entropy Analysis: Evaluate the dynamic coupling between the WiFi data stream and the corresponding avionics signal of a designated civil aircraft.

[0013] Step 3. Digital twin multi-state simulation: Evaluate the jump risk probability of the corresponding primary flight display of a specified civil aircraft in the superposition state.

[0014] Step 4: Safety margin optimization: Implement hierarchical safety control within designated civil aviation aircraft.

[0015] The beneficial effects of the present invention are as follows: 1. The civil aviation operation safety assessment platform and method provided by the present invention, in the process of Wi-Fi signal crosstalk risk assessment, comprehensively collects multiple types of key information such as Wi-Fi signal transmitting antenna gain and designated avionics system receiving antenna gain, uses a crosstalk probability quantification formula to perform scientific calculations, and compares them with pre-set safety thresholds. Combined with the judgment of the display situation of onboard sensors, it is conducive to converting the originally vague signal crosstalk risk into a quantifiable and evaluable indicator, avoiding subjective judgment, providing a solid data foundation for subsequent safety decision-making, and improving the accuracy and objectivity of risk assessment.

[0016] 2. During the dynamic monitoring of the interaction between WiFi data streams and avionics signals, the present invention collects the occurrence probabilities and signal change rates of various key signal variables, calculates the dynamic coupling entropy of the interaction between the two using a dynamic coupling entropy calculation formula, compares it with a preset coupling entropy threshold, and uses a rule engine to determine the severity level of abnormal events. This helps accurately capture subtle changes in the interaction between WiFi data streams and avionics signals, and promptly identifies potential risks. Compared with traditional monitoring methods, this method improves the sensitivity and timeliness of monitoring complex signal interaction risks.

[0017] 3. In the primary flight display transition risk assessment process, the embodiment of the present invention queries historical flight data, calculates the dynamic weight values ​​of electromagnetic interference intensity, GPS spoofing attack intensity, and dynamic coupling entropy, and uses a specific calculation formula to evaluate the primary flight display transition risk probability in a superposition state. This is conducive to fully utilizing historical experience data, combining it with current real-time monitoring data, and comprehensively considering multiple influencing factors to predict the possible primary flight display transition risks in advance, thereby gaining more preparation time for crew members to respond to risks and enhancing the foresight of risk prevention and control.

[0018] 4. In the security control execution process, the embodiment of the present invention divides hierarchical security control into three levels. According to different risk assessment results and abnormal situations, targeted operations such as limiting the power of WiFi transmitting equipment, starting the electromagnetic shielding layer to operate at full power, and shutting down all non-critical systems are performed respectively. This is conducive to accurately matching the corresponding security control strategy according to the severity of the risk, avoiding excessive control causing waste of resources or insufficient control causing safety hazards, achieving a balance between safety and efficiency, and improving resource utilization efficiency and the effectiveness of security control.

[0019] 5. In the entire process of civil aviation operation safety assessment, the embodiment of the present invention closely coordinates multiple modules such as electronic interference map construction, signal coupling entropy analysis, digital twin polymorphic simulation, and safety margin optimization to form a complete closed-loop management system of "acquisition-analysis-simulation-control". This is conducive to breaking information silos and realizing efficient flow and sharing of data among modules. It conducts comprehensive and systematic assessment and control of civil aviation operation safety from multiple dimensions, significantly improving the overall efficiency and reliability of civil aviation operation safety assessment, and providing innovative and practical solutions for intelligent civil aviation safety assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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 of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0021] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0022] Figure 2 Schematic diagram of the implementation steps of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides a civil aviation operation safety assessment platform, which includes: an electronic interference spectrum construction module, a signal coupling entropy analysis module, a digital twin polymorphic simulation module, a safety margin optimization module and a database.

[0025] The electronic interference map construction module is connected to the signal coupling entropy analysis module and the database respectively, the signal coupling entropy analysis module is connected to the digital twin polymorphic simulation module and the database respectively, and the digital twin polymorphic simulation module is connected to the safety margin optimization module and the database respectively.

[0026] The electronic interference map construction module is used to collect WiFi signal crosstalk information from a specified civil aircraft during operation, and then quantify the probability of WiFi signal crosstalk on the avionics system, thereby evaluating whether the probability of crosstalk meets operational safety requirements and the display of onboard sensors.

[0027] In a specific embodiment, the WiFi signal crosstalk information of a specified civil airliner during operation is collected, and the specific process is as follows: the WiFi signal crosstalk information includes the WiFi signal transmitting antenna gain, the specified avionics system receiving antenna gain, the WiFi signal wavelength, the direct path length, the reflection path length and the cabin medium attenuation coefficient.

[0028] By querying the equipment technical manual and avionics system equipment specifications of the specified civil aircraft, the WiFi signal transmitting antenna gain and the specified avionics system receiving antenna gain are obtained. The frequency band of the WiFi signal is queried from the corresponding control platform of the specified civil aircraft. Then, the WiFi signal wavelength is calculated using the wavelength calculation formula based on the speed of light.

[0029] Use 3D modeling software to build a 3D model of the cabin structure of a designated civil airliner. Locate the coordinates of the WiFi access point and designated avionics equipment in the 3D model. Then, use the distance measurement function of the 3D software to measure the spatial distance between the WiFi access point and the designated avionics equipment, which is the direct path length. Use the ray tracing method to construct the mirror point of the WiFi access point in the 3D model. Then, use the Euclidean distance formula to calculate the straight-line distance from the mirror point to the designated avionics equipment, which is the reflected path length.

[0030] An experiment was conducted in a microwave oven to determine the cabin dielectric attenuation coefficient. A composite material sample made of the same material as that of a designated civil airliner cabin was prepared, and its thickness was consistent with that of the designated civil airliner cabin bulkhead. The transmitting antenna emitted a signal of a set frequency band, and the receiving antenna measured the signal strength after passing through the composite material sample. The difference was taken to obtain the signal strength attenuation, which was then divided by the thickness of the composite material sample to obtain the cabin dielectric attenuation coefficient.

[0031] It should be noted that the WiFi signal transmitting antenna gain refers to the transmitting antenna's ability to concentrate signal energy and radiate in a specific direction. The unit is usually expressed in dBi. For example, the WiFi transmitting antenna gain of a passenger aircraft is 5dBi, which means that the signal radiation intensity in the specified direction is about 3.16 times higher than that of an ideal antenna. The receiving antenna gain of a specified avionics system is the receiving antenna's ability to capture signal energy from a specific direction. It is also measured in dBi. For example, the receiving antenna gain of an avionics device is 3dBi, which means that the receiving capability of the signal in that direction is about 2 times stronger than that of an ideal antenna.

[0032] It should also be noted that the wavelength calculation formula is specifically formulated by dividing the speed of light by the frequency band of the WiFi signal; the result is the wavelength. Designated avionics equipment refers to specific electronic equipment on an aircraft, such as a communication and navigation system, which requires receiving WiFi signals for data exchange. The specific application process of the ray tracing method is as follows: In the three-dimensional cabin model, determine the WiFi access point location A and the avionics equipment location B, as well as a reflective surface, such as a metal bulkhead. With the reflective surface as the axis of symmetry, generate the mirror image point A' of access point A. The straight-line distance from A' to B is then calculated using the Euclidean formula. The Euclidean distance formula is a mature mathematical technique for calculating the straight-line distance between two points in multidimensional space or a plane, and will not be elaborated on here.

[0033] In a specific embodiment, the crosstalk probability of the WiFi signal to the avionics system is quantified as follows: using the crosstalk probability quantification formula:

[0034] The crosstalk probability β of the WiFi signal to the avionics system is obtained, where i is 1, d i Expressed as the direct path length, when i is 2, d iExpressed as the reflection path length, Dbwi f i, Db′, λ, and κ represent the WiFi signal transmitting antenna gain, the specified avionics system receiving antenna gain, the WiFi signal wavelength, and the cabin dielectric attenuation coefficient, respectively. η is the coupling coefficient obtained by fitting historical data.

[0035] It should be noted that π and e are both mathematical constants, with values ​​of 3.14 and 2.718, respectively. The process of fitting the coupling coefficient using historical data is as follows: First, collect data on multiple sets of in-flight Wi-Fi signal strengths on civil aircraft, such as 23 dBm and 20 dBm, and the corresponding data on the interference levels of the avionics systems. Then, select a fitting model, such as the linear regression model y = k * x + q, with signal strength as the independent variable x and interference level as the dependent variable y. Use the least squares method to calculate the parameter k, or coupling coefficient, that minimizes the sum of squared errors. For example, if the error rate is 0.1% at a signal strength of 23 dBm and 0.05% at a signal strength of 20 dBm, substituting this into the model yields k ≈ 0.167% / dBm, meaning a coupling coefficient of 0.0167% per dBm. This means that for every 1 dBm decrease in signal strength, the interference level decreases by approximately 0.0167%.

[0036] In a specific embodiment, the evaluation of whether the crosstalk probability meets operational safety requirements and the display of the onboard sensor is performed as follows: a preset safety threshold of the crosstalk probability of the WiFi signal to the avionics system is queried from the database; if β is less than the preset safety threshold of the crosstalk probability of the WiFi signal to the avionics system, it indicates that the crosstalk probability meets the operational safety requirements, and the event in which the crosstalk probability meets the operational safety requirements is recorded as a; if β is greater than or equal to the preset safety threshold of the crosstalk probability of the WiFi signal to the avionics system, it indicates that the crosstalk probability does not meet the operational safety requirements, and the event in which the crosstalk probability does not meet the operational safety requirements is recorded as b.

[0037] If event a is established and the onboard sensors show no abnormality, the safety margin release performance is relaxed. If event a is established but the onboard sensors show an abnormality, the software signal coupling entropy analysis module is activated for dynamic coupling analysis. If event b is established but the onboard sensors show no abnormality, the operating parameter verification in the designated civil airliner is started. If event b is established and the onboard sensors show an abnormality, the safety margin dynamic optimization module is directly executed for emergency regulation.

[0038] It should be noted that the safety threshold for the probability of crosstalk between WiFi signals and avionics systems is used as a basis for evaluating whether the probability of crosstalk occurrence meets operational safety requirements. The process for setting the safety threshold for the probability of crosstalk between WiFi signals and avionics systems is the same as the interference threshold setting process for electromagnetic compatibility testing of electronic equipment in the prior art, and will not be elaborated on here.

[0039] It should be noted that the specific verification contents of the operating parameter verification initiated in designated civil aviation passenger aircraft include, for example, the civil aviation operation safety assessment system automatically comparing the flight speed and heading data of the satellite navigation system and the inertial navigation system to verify whether the two are consistent, while monitoring the signal reception strength of the radio communication equipment to check whether the signal quality is degraded and data transmission errors are caused by WiFi crosstalk, so as to ensure the normal operation of the avionics system.

[0040] During the dynamic monitoring process of the interaction between WiFi data streams and avionics signals, the embodiments of the present invention collect the occurrence probabilities and signal change rates of various key signal variables, use the dynamic coupling entropy calculation formula to obtain the dynamic coupling entropy of the interaction between the two, compare it with a preset coupling entropy threshold, and combine it with a rule engine to determine the severity level of abnormal events. This helps to accurately capture subtle changes in the interaction between WiFi data streams and avionics signals and promptly identify potential risks. Compared with traditional monitoring methods, this improves the sensitivity and timeliness of monitoring complex signal interaction risks.

[0041] The signal coupling entropy analysis module is used to evaluate the dynamic coupling between WiFi data streams and the corresponding avionics signals of a specified civil aircraft.

[0042] In a specific embodiment, the dynamic coupling of the interaction between the WiFi data stream and the corresponding avionics signal of the designated civil airliner is evaluated as follows: first, the occurrence probability and signal change rate corresponding to each key signal variable in the designated civil airliner are collected, and the occurrence frequency ratio of each key signal variable within the set time window is statistically calculated, that is, the occurrence probability corresponding to each key signal variable. A fixed time period is set, and time difference calculations are performed on each key signal variable to obtain the signal change rate of each key signal, and then the dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the designated civil airliner is calculated.

[0043] A preset coupling entropy threshold is queried from the database, and the dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft is compared with the preset coupling entropy threshold. The severity level of the abnormal event displayed by the mechanical sensor is determined by the rule engine. If the dynamic coupling entropy is greater than or equal to the set coupling entropy threshold, the jump risk probability of the corresponding primary flight display of the specified civil aircraft in the superposition state is simulated. If the dynamic coupling entropy is less than the set coupling entropy threshold, if the severity level of the abnormal event is high, the simulation is conservatively triggered. If the dynamic coupling entropy is greater than or equal to the set coupling entropy threshold and the severity level of the abnormal event is medium or low, the flight log is recorded.

[0044] It should be noted that the pre-set coupling entropy threshold is used as the basis for evaluating the dynamic coupling between the WiFi data stream and the corresponding avionics signal of a specified civil aircraft. The process of setting the coupling entropy threshold is the same as the process of setting the crosstalk probability safety threshold of the WiFi signal to the avionics system, and will not be elaborated on here.

[0045] It should be noted that various key signal variables include avionics system signals, cabin equipment signals, and environmental status signals. When calculating the signal change rate of various key signal variables, first set a fixed time period, such as 1 second, and divide the signal acquisition process into time points according to this time period. At each time point, the value of the signal variable is recorded. For example, taking the calculation of the change rate of WiFi signal strength as an example, assuming that at the 0th second, the recorded WiFi signal strength is 23dBm, dBm is the unit of absolute power, and at the 1st second, the recorded signal strength becomes 22.5dBm. When calculating the change rate , subtract the signal strength value at the 0th second from the signal strength value at the 1st second, which is -0.5dBm. The obtained -0.5dBm is the change in signal strength within this 1 second. Then divide this change by the set time period of 1 second, which is -0.5dBm / s. This -0.5dBm / s is the signal change rate of the WiFi signal strength within this 1 second, which means that the WiFi signal strength decreases by 0.5dBm per second. In this way, by subtracting the signal variable values ​​at each adjacent time point and dividing them by the time period, we can get the signal change rate of various key signals in each time period.

[0046] In a specific embodiment, the dynamic coupling entropy of the interaction between the WiFi data stream and the avionics signal corresponding to the designated civil aircraft is calculated by the following process: The dynamic coupling entropy is calculated by the formula:

[0047] The dynamic coupling entropy χ of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft is obtained, where j is the number of each key signal variable, j = 1, 2, ..., n, n is a positive integer, p j , Δγ j They are respectively expressed as the occurrence probability and signal change rate corresponding to the j-th key signal variable.

[0048] In the primary flight display jump risk assessment process, the embodiment of the present invention queries historical flight data, calculates the dynamic weight values ​​of electromagnetic interference intensity, GPS spoofing attack intensity and dynamic coupling entropy, and uses a specific calculation formula to evaluate the primary flight display jump risk probability in the superposition state. This is conducive to making full use of historical experience data, combining current real-time monitoring data, and comprehensively considering multiple influencing factors. It can predict the possible jump risks of the primary flight display in advance, gain more preparation time for the crew to deal with risks, and enhance the foresight of risk prevention and control.

[0049] The digital twin multi-state simulation module is used to evaluate the jump risk probability of the corresponding primary flight display of a specified civil aircraft in the superposition state.

[0050] In a specific embodiment, the evaluation of the jump risk probability of the primary flight display corresponding to the specified civil aircraft in the superposition state is performed as follows: querying the database for the actual cabin electromagnetic interference intensity E and GPS spoofing attack intensity Q corresponding to the dynamic coupling entropy greater than or equal to the set coupling entropy threshold in the historical flight of the specified civil aircraft, and calculating the dynamic weight values ​​of the electromagnetic interference intensity, GPS spoofing attack intensity and dynamic coupling entropy respectively. Based on the dynamic coupling entropy of the interaction between the WiFi data stream and the avionics signal corresponding to the specified civil aircraft, the calculation formula is further used:

[0051] Risk = λ1*E+λ2*χ+λ3*Q, and the jump risk probability Risk of the primary flight display corresponding to the specified civil aircraft in the superposition state is obtained, where the weight factors corresponding to the electromagnetic interference intensity of λ1, λ2, and λ3, the weight factor of the GPS spoofing attack intensity, and the dynamic weight factor of the dynamic coupling entropy are obtained. Emergency regulation is then performed based on the jump risk probability value of the primary flight display corresponding to the specified civil aircraft in the superposition state.

[0052] It should be noted that in historical flights, the electromagnetic interference intensity was collected in real time by airborne electromagnetic probes on the fuselage surface and in various areas of the cabin, such as the electric field intensity and magnetic field intensity. After amplification and filtering by the signal conditioning module, the data acquisition card converted it into a digital signal at a frequency of 100 times per second, stored in combination with a timestamp in the airborne quick access recorder, and then synchronized to the database through the ground data export system. For example, at 2:32:15 pm on a certain flight, the electromagnetic field intensity on the right side of the cockpit was collected as 15 volts per meter, corresponding to a normalized electromagnetic interference intensity value of 0.03.

[0053] It should also be noted that in historical flights, the onboard GPS receiver continuously monitors key indicators such as the carrier-to-noise ratio, pseudorange residuals, and carrier phase mutation values. When the carrier-to-noise ratio is lower than 25dBHz and the pseudorange residual exceeds 5 meters, a deception warning algorithm based on multi-feature fusion is triggered. dBHz is the unit of the carrier-to-noise ratio. The algorithm calculates the time rate of change of the carrier-to-noise ratio. If it drops by more than 2dBHz per second, it is marked as a signal deterioration feature. At the same time, the pseudorange data of multiple satellites are compared. If the pseudorange residuals of more than 3 satellites exceed the limit at the same time and are non-randomly distributed, it is determined to be deception. Deception feature matching. Finally, based on the deception intensity model trained by the support vector machine, parameters such as carrier-to-noise ratio, pseudorange residual, and phase mutation value are input, and the normalized deception intensity value is output. The deception intensity value ranges from 0 to 1. For example, at 14:35:40 on June 9, 2025, a satellite carrier-to-noise ratio of 22dBHz and a pseudorange residual of 8 meters were recorded. The calculated deception intensity is 0.65. These original monitoring data and calculation results are stored in the "GPS interference record" table in the database with "flight number + GPS week number + second count" as the time index.

[0054] It should also be noted that the electromagnetic interference intensity weight consists of a base value and an incremental humidity effect. The base value of 0.3 is derived from historical flight data statistics. In a large number of avionics system operation records, the average contribution of electromagnetic interference to system risk is assessed to be 30% when the ambient humidity is between 40% and 60%. Therefore, this is set as the base weight. The coefficient of 0.2 is obtained through electromagnetic compatibility testing. In a laboratory environment, the humidity is gradually adjusted from 0% to 100%. The maximum increase in the measured electromagnetic interference intensity is approximately 66% of the base value. Therefore, 0.2 is used as the weight increment coefficient for each 100% change in humidity. The specific calculation steps are: divide the real-time ambient humidity by 100 to obtain the humidity normalized value, multiply it by 0.2 to obtain the humidity contribution weight increment, and finally add this to the base value of 0.3. For example, if the current humidity is 60% RH, the calculation process is: 60 ÷ 100 = 0.6, 0.6 × 0.2 = 0.12, 0.3 + 0.12 = 0.42, resulting in a weight of 0.42.

[0055] The GPS spoofing attack intensity weighting factor is calculated by multiplying the theoretical maximum attack threat by the CPU load attenuation factor. The coefficient of 0.5 represents the maximum attack threat weight based on the risk priority of the civil aviation system. In avionics system design, GPS spoofing attacks are considered one of the highest-level risks, so their impact weight under ideal conditions is preset to 50%. The linear term is derived from the system resource allocation logic. Higher CPU load means fewer resources available to execute the attack detection algorithm, reducing attack response capability. When the load reaches 100%, attack events are theoretically impossible to handle. The specific calculation steps are to first divide the real-time CPU load rate by 100 to obtain the load-normalized value. This value is then subtracted from 1 to obtain the attack response capability factor, which is then multiplied by 0.5 to obtain the final weight. For example, if the CPU load is 30%, the calculation process is: 30 ÷ 100 = 0.3, 1 - 0.3 = 0.7, 0.7 × 0.5 = 0.35, resulting in a weight of 0.35. The calculation process for the dynamic weighting factor of dynamic coupling entropy is the same as that for the electromagnetic interference intensity weighting factor and is not further elaborated here.

[0056] It should also be noted that the values ​​of λ1, λ2, and λ3 are all greater than 0 and less than 1. The process of setting the weight factor corresponding to the electromagnetic interference intensity, the GPS spoofing attack intensity weight factor, and the dynamic weight factor of the dynamic coupling entropy is the same as the weight setting process of the multi-factor risk assessment model based on the hierarchical analysis method in the prior art. The relative importance of each risk factor is determined by steps such as constructing a judgment matrix, calculating eigenvectors, and performing consistency tests. It is also consistent with the feature weight training process of the supervised learning model in machine learning, and the weight parameters are iteratively optimized through historical data to fit the risk scenario. Therefore, it will not be elaborated here.

[0057] In a specific embodiment, the emergency control is performed according to the jump risk probability value of the main flight display corresponding to the specified civil aircraft in the superposition state. The specific process is as follows: the emergency control trigger condition threshold is queried from the database, and the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is compared with the emergency control trigger condition threshold. If the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is greater than or equal to the emergency control trigger condition threshold, the second and third levels of control of the safety margin optimization module are started. If the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is less than the emergency control trigger condition threshold, but the severity level of the abnormal event is high, the first level of control of the safety margin optimization module is executed.

[0058] It should be noted that the emergency control trigger condition threshold is used as the basis for evaluating the level of control to be executed. The process of setting the emergency control trigger condition threshold is the same as the process of setting the crosstalk probability safety threshold of the WiFi signal to the avionics system, and will not be elaborated here.

[0059] During the security control execution process, the embodiment of the present invention divides hierarchical security control into three levels. Based on different risk assessment results and abnormal situations, targeted operations such as limiting the power of WiFi transmitting devices, starting the electromagnetic shielding layer at full power, and shutting down all non-critical systems are performed respectively. This is conducive to accurately matching corresponding security control strategies according to the severity of the risk, avoiding excessive control causing waste of resources or insufficient control causing safety hazards, achieving a balance between safety and efficiency, and improving resource utilization efficiency and the effectiveness of security control.

[0060] The safety margin optimization module is used to implement hierarchical safety control within a specified civil aircraft.

[0061] In a specific embodiment, the specific process of executing hierarchical safety control in a designated civil airliner is as follows: the hierarchical safety control is divided into first-level control, second-level control, and third-level control. The operation of the first-level control is to limit the power of the WiFi transmitting device on the civil airliner to 90% of the nominal value, and at the same time increase the avionics system data verification frequency by 10 times. The operation of the second-level control is to start the electromagnetic shielding layer to operate at full power and reduce the avionics computer load to 60%. The operation of the third-level control is to shut down all non-flight critical systems. If event b is established and the onboard sensor shows an abnormality, the first-level control is executed.

[0062] It should be noted that, assuming that a civil airliner encounters the following scenario during flight: During the cruise phase, the cabin WiFi device fails, causing the transmission power to increase abnormally. The onboard monitoring system shows that the WiFi signal crosstalk probability reaches 80% of the safety threshold, which does not exceed the threshold. However, the avionics system data verification finds that the bus transmission error rate has increased slightly. At this time, the first level of regulation is triggered: the WiFi device power is reduced from the nominal value of 23dBm to 20dBm, and the avionics data verification frequency is increased from 10 times per second to 100 times per second to reduce interference energy and enhance data error correction capabilities. If occasional jumps are detected in the main flight display later, the digital twin simulation will predict If the measured jump risk probability reaches 70% of the emergency threshold, the second level of control will be initiated: the full power operation of the fuselage electromagnetic shielding layer will be activated, such as the shielding effectiveness will be increased from 20dB to 40dB, and the avionics computer load will be forced to reduce from 85% to 60% through the task scheduling algorithm, giving priority to core flight data processing. If a sudden strong electromagnetic pulse interference causes the crosstalk probability to exceed the safety threshold and the sensor shows that the engine control signal is abnormal, the third level of control will be immediately executed: non-critical equipment such as the cabin entertainment system and catering control will be instantly shut down, and their power and radio frequency links will be cut off, leaving only core systems such as navigation and engine control running, while switching to hardware redundant links.

[0063] In the entire process of civil aviation operation safety assessment, the embodiment of the present invention closely coordinates multiple modules such as electronic interference map construction, signal coupling entropy analysis, digital twin polymorphic simulation, and safety margin optimization to form a complete closed-loop management system of "acquisition-analysis-simulation-control", which is conducive to breaking information silos and realizing efficient circulation and sharing of data among modules. It conducts comprehensive and systematic assessment and control of civil aviation operation safety from multiple dimensions, significantly improves the overall efficiency and reliability of civil aviation operation safety assessment, and provides innovative and practical solutions for intelligent civil aviation safety assessment.

[0064] The database is used to store preset safety thresholds for the probability of crosstalk between WiFi signals and avionics systems, preset coupling entropy thresholds, and actual cabin electromagnetic interference intensity and GPS spoofing attack intensity corresponding to historical flight events of a specified civil airliner when the dynamic coupling entropy was greater than or equal to the preset coupling entropy threshold.

[0065] See also Figure 2 As shown, the civil aviation operation safety assessment method includes the following steps: Step 1, electronic interference map construction: collecting WiFi signal crosstalk information of a specified civil aircraft during operation, and then quantifying the crosstalk probability of the WiFi signal to the avionics system, so as to evaluate whether the probability of crosstalk meets the operational safety requirements and the display of the onboard sensors.

[0066] Step 2: Signal Coupling Entropy Analysis: Evaluate the dynamic coupling between the WiFi data stream and the corresponding avionics signal of a designated civil aircraft.

[0067] Step 3. Digital twin multi-state simulation: Evaluate the jump risk probability of the corresponding primary flight display of a specified civil aircraft in the superposition state.

[0068] Step 4: Safety margin optimization: Implement hierarchical safety control within designated civil aviation aircraft.

[0069] The civil aviation operations safety assessment platform and method provided by the present invention comprehensively collect multiple types of key information, such as the WiFi signal transmission antenna gain and the receiving antenna gain of the designated avionics system, during the WiFi signal crosstalk risk assessment process. This information is then scientifically calculated using a crosstalk probability quantification formula. This information is then compared with a pre-set safety threshold and combined with the information displayed by onboard sensors. This helps transform the originally ambiguous signal crosstalk risk into a quantifiable and evaluable indicator, avoiding subjective judgments. This provides a solid data foundation for subsequent safety decision-making and improves the accuracy and objectivity of risk assessments.

[0070] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. Civil Aviation Operation Safety Assessment Platform, characterized by: include: The electronic interference map construction module is used to collect Wi-Fi signal crosstalk information from a specific civil aircraft during operation, and then quantify the probability of Wi-Fi signal crosstalk on the avionics system, thereby evaluating whether the crosstalk probability meets operational safety requirements and the display of onboard sensors; Signal coupling entropy analysis module, used to evaluate the dynamic coupling between WiFi data streams and the corresponding avionics signals of a designated civil aircraft; A digital twin multi-state simulation module is used to evaluate the probability of jump risk of the corresponding primary flight display of a specified civil aircraft in the superposition state; The safety margin optimization module is used to implement hierarchical safety control within a specified civil aircraft.

2. The civil aviation operation safety assessment platform according to claim 1, characterized in that: The specific process of collecting WiFi signal crosstalk information of a designated civil aircraft during operation is as follows: WiFi signal crosstalk information includes WiFi signal transmitting antenna gain, designated avionics system receiving antenna gain, WiFi signal wavelength, direct path length, reflected path length, and cabin dielectric attenuation coefficient; By consulting the equipment technical manual and avionics system equipment specifications of a specific civil aircraft, the WiFi signal transmitting antenna gain and the specified avionics system receiving antenna gain are obtained. The WiFi signal frequency band is then found from the corresponding control platform of the specific civil aircraft. The WiFi signal wavelength is then calculated using the wavelength calculation formula based on the speed of light. Use 3D modeling software to create a 3D model of the cabin structure of a designated civil aircraft. Locate the coordinates of the WiFi access point and designated avionics equipment in the 3D model. Use the 3D software's distance measurement function to measure the spatial distance between the WiFi access point and the designated avionics equipment, which is the direct path length. Use ray tracing to construct a mirror point of the WiFi access point in the 3D model. Use the Euclidean distance formula to calculate the straight-line distance from the mirror point to the designated avionics equipment, which is the reflected path length. An experiment was conducted in a microwave oven to determine the cabin dielectric attenuation coefficient. A composite material sample made of the same material as that of a designated civil airliner cabin was prepared, and its thickness was consistent with that of the designated civil airliner cabin bulkhead. The transmitting antenna emitted a signal of a set frequency band, and the receiving antenna measured the signal strength after passing through the composite material sample. The difference was taken to obtain the signal strength attenuation, which was then divided by the thickness of the composite material sample to obtain the cabin dielectric attenuation coefficient.

3. The civil aviation operation safety assessment platform according to claim 2, characterized in that: The specific process of quantifying the crosstalk probability of WiFi signals to the avionics system is as follows: The crosstalk probability is quantified by the formula: The crosstalk probability β of the WiFi signal to the avionics system is obtained, where i is 1, d i It is expressed as the direct path length. When i is 2, di is expressed as the reflected path length. f i, Db′, λ, and κ represent the WiFi signal transmitting antenna gain, the specified avionics system receiving antenna gain, the WiFi signal wavelength, and the cabin dielectric attenuation coefficient, respectively. η is the coupling coefficient obtained by fitting historical data.

4. The civil aviation operation safety assessment platform according to claim 3, characterized in that: The specific process of evaluating whether the probability of crosstalk occurrence complies with operational safety and the display of onboard sensors is as follows: A preset safety threshold for the probability of crosstalk between WiFi signals and the avionics system is retrieved from the database. If β is less than the preset safety threshold for the probability of crosstalk between WiFi signals and the avionics system, it indicates that the probability of crosstalk meets the operational safety requirements. The event in which the probability of crosstalk meets the operational safety requirements is recorded as a. If β is greater than or equal to the preset safety threshold for the probability of crosstalk between WiFi signals and the avionics system, it indicates that the probability of crosstalk does not meet the operational safety requirements. The event in which the probability of crosstalk does not meet the operational safety requirements is recorded as b. If event a is established and the onboard sensors show no abnormality, the safety margin release performance is relaxed. If event a is established but the onboard sensors show an abnormality, the software signal coupling entropy analysis module is activated for dynamic coupling analysis. If event b is established but the onboard sensors show no abnormality, the operating parameter verification in the designated civil airliner is started. If event b is established and the onboard sensors show an abnormality, the safety margin dynamic optimization module is directly executed for emergency regulation.

5. The civil aviation operation safety assessment platform according to claim 4, characterized in that: The specific process of evaluating the dynamic coupling between the WiFi data stream and the corresponding avionics signal of a designated civil aircraft is as follows: First, the occurrence probability and signal change rate of various key signal variables in a specified civil aircraft are collected. The occurrence frequency ratio of each key signal variable within a set time window is calculated, which is the occurrence probability corresponding to each key signal variable. A fixed time period is set and time difference calculations are performed on each key signal variable to obtain the signal change rate of each key signal. The dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft is then calculated. A preset coupling entropy threshold is queried from the database, and the dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft is compared with the preset coupling entropy threshold. The severity level of the abnormal event displayed by the mechanical sensor is determined by the rule engine. If the dynamic coupling entropy is greater than or equal to the set coupling entropy threshold, the jump risk probability of the corresponding primary flight display of the specified civil aircraft in the superposition state is simulated. If the dynamic coupling entropy is less than the set coupling entropy threshold, if the severity level of the abnormal event is high, the simulation is conservatively triggered. If the dynamic coupling entropy is greater than or equal to the set coupling entropy threshold and the severity level of the abnormal event is medium or low, the flight log is recorded.

6. The civil aviation operation safety assessment platform according to claim 5, characterized in that: The calculation obtains the dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the designated civil aircraft. The specific process is as follows: The dynamic coupling entropy is calculated by the formula: The dynamic coupling entropy χ of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft is obtained, where j is the number of each key signal variable, j = 1, 2, ..., n, n is a positive integer, p j , Δγ j They are respectively expressed as the occurrence probability and signal change rate corresponding to the j-th key signal variable.

7. The civil aviation operation safety assessment platform according to claim 6, characterized in that: The evaluation specifies the jump risk probability of the primary flight display of a civil aircraft in the superposition state. The specific process is as follows: The database is retrieved for the actual in-cabin electromagnetic interference intensity E and GPS spoofing attack intensity Q corresponding to historical flight events of a specified civil aircraft when the dynamic coupling entropy was greater than or equal to the set coupling entropy threshold. The dynamic weights of the electromagnetic interference intensity, GPS spoofing attack intensity, and dynamic coupling entropy are calculated, respectively. Based on the dynamic coupling entropy of the interaction between the WiFi data stream and the corresponding avionics signal of the specified civil aircraft, the trip risk probability (Risk) of the corresponding primary flight display of the specified civil aircraft in the superposition state is obtained using the formula: Risk = λ1*E+λ2*χ+λ3*Q. This formula includes the weight factors corresponding to the electromagnetic interference intensities λ1, λ2, and λ3, the GPS spoofing attack intensity weight factor, and the dynamic coupling entropy dynamic weight factor. Emergency adjustments are then made based on the trip risk probability of the corresponding primary flight display of the specified civil aircraft in the superposition state.

8. The civil aviation operation safety assessment platform according to claim 7, characterized in that: The specific process of performing emergency control based on the jump risk probability value of the primary flight display corresponding to the designated civil aircraft in the superposition state is as follows: The emergency control trigger condition threshold is queried from the database, and the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is compared with the emergency control trigger condition threshold. If the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is greater than or equal to the emergency control trigger condition threshold, the second and third levels of control of the safety margin optimization module are started. If the jump risk probability of the main flight display corresponding to the specified civil aircraft in the superposition state is less than the emergency control trigger condition threshold, but the severity level of the abnormal event is high, the first level of control of the safety margin optimization module is executed.

9. The civil aviation operation safety assessment platform according to claim 8, characterized in that: The specific process of executing hierarchical safety control in a designated civil aircraft is as follows: The hierarchical safety control is divided into first-level control, second-level control and third-level control. The operation of the first-level control is to limit the power of the WiFi transmitting equipment on civil aviation passenger aircraft to 90% of the nominal value, and at the same time increase the data verification frequency of the avionics system by 10 times. The operation of the second-level control is to start the electromagnetic shielding layer to run at full power and reduce the avionics computer load to 60%. The operation of the third-level control is to shut down all non-flight critical systems. If event b is established and the onboard sensors show an abnormality, the first-level control will be executed.

10. A civil aviation operation safety assessment method for executing the civil aviation operation safety assessment platform according to any one of claims 1 to 9, characterized in that: The steps include: Step 1: Construct an electronic interference map: Collect WiFi signal crosstalk information from a specific civil aircraft during operation, and then quantify the probability of WiFi signal crosstalk on the avionics system. This allows us to assess whether the crosstalk probability meets operational safety requirements and the indications of onboard sensors. Step 2: Signal Coupling Entropy Analysis: Evaluate the dynamic coupling between the WiFi data stream and the corresponding avionics signal of a designated civil aircraft; Step 3: Digital twin multi-state simulation: Evaluate the jump risk probability of the primary flight display corresponding to the specified civil aircraft in the superposition state; Step 4: Safety margin optimization: Implement hierarchical safety control within designated civil aviation aircraft.

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