Dynamic optimization method of aircraft environment control system based on multi-modal data fusion

Abstract

The invention relates to the technical field of aircraft manufacturing, in particular to a dynamic optimization method of an aircraft environment control system based on multi-modal data fusion, which comprises the following steps of: drawing an environment risk coefficient curve of each precise instrument in a cargo hold; continuously monitoring multi-modal sensing data of each precise instrument in the cargo hold, and performing execution judgment on a first-level environment control strategy; working state data of each precise instrument and multi-field coupling data of a cargo hold environment are obtained, and the environmental fitness of each precise instrument in a flight state is evaluated; performing execution judgment on a second-level environmental control strategy according to the environmental fitness, obtained through evaluation, of each precise instrument in the flight state; and an environmental risk coefficient curve of each precise instrument in the cargo hold is called, and regulation and control parameters of the secondary environmental control strategy are adjusted according to the execution judgment result. The problems that in the prior art, compared with land transportation, air transportation of precise instruments has the advantage of guaranteeing transportation timeliness, and meanwhile low failure rate and damage rate are difficult to guarantee are solved.

Description

Technical Field

[0001] This invention relates to the field of aircraft manufacturing technology, and is a dynamic optimization method for aircraft environmental control systems based on multimodal data fusion. Background Technology

[0002] In the field of transporting precision aviation instruments, existing environmental control systems have the following technical problems: traditional methods rely too much on single-dimensional monitoring of temperature and humidity, and cannot perceive the multi-physics coupling risks unique to the aviation environment. First, the identification of structural resonance is based solely on vibration acceleration thresholds, ignoring the spectral distortion characteristics of airflow disturbances and airframe resonance. This leads to the transfer of resonance energy to the precision instrument mounting frame during transonic flight, inducing microcrack propagation. Second, electrostatic protection relies on fixed humidity thresholds and fails to quantify the local charge accumulation caused by friction of non-uniform packaging materials in a dry cargo hold environment, resulting in electrostatic imbalance discharge voltages frequently exceeding the 5kV safety limit. Third, the pressure change response uses single-point monitoring of the cabin pressure change rate, failing to capture the three-dimensional spatial gradient field around the instrument, leading to a risk of bursting due to stress differences >2MPa in the sealed cavity during takeoff and landing. Furthermore, the existing system uses a static threshold triggering mechanism and has not established a dynamic coupling model of resonance distortion rate, electrostatic imbalance, and pressure gradient. During turbulent gusts or rapid ascent, the false alarm rate is as high as 34%, making it difficult to guarantee a low failure rate and damage rate while ensuring the timeliness advantage of air transport compared to land transport for precision instruments. Summary of the Invention

[0003] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0004] The technical problem to be solved by this invention is that, in the prior art, it is difficult to ensure a low failure rate and damage rate while ensuring the advantage of transportation timeliness in air transport of precision instruments compared with land transport. The invention proposes a dynamic optimization method for aircraft environmental control systems based on multimodal data fusion.

[0005] To achieve the above objectives, the technical solution of the dynamic optimization method for aircraft environmental control systems based on multimodal data fusion of the present invention includes the following steps: S1: Obtain historical environmental control data of the aircraft cargo hold, and plot the environmental risk coefficient curves of each precision instrument in the cargo hold based on the historical environmental control data; S2: Continuously monitor the multimodal sensor data of various precision instruments in the cargo hold and make judgments on the execution of the first-level environmental control strategy; S3: By deploying a multi-physics field sensor network in the cargo hold, the working status data of each precision instrument and the multi-field coupling data of the cargo hold environment are obtained, and the working status data is imported into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions. S4: Based on the environmental adaptability of each precision instrument under the flight status obtained from the assessment, make a judgment on the execution of the second-level environmental control strategy; S5: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and adjust the control parameters of the secondary environmental control strategy according to the execution judgment results.

[0006] Specifically, S1 includes: S11: Multi-axis sensor arrays are configured on each instrument mounting rack in the cargo hold, wherein the sensor arrays include n sets of heterogeneous sensor nodes distributed on the instrument bearing surface. S12: Collect structural resonant frequency data and electrostatic accumulation gradient data of the instrument bearing surface during historical flights through a sensor array to form resonant frequency dataset A and electrostatic gradient dataset B. S13: Import the resonant frequency dataset A into the resonant distortion rate calculation strategy to calculate the structural resonant distortion rate. ; S14: Simultaneously import the electrostatic gradient dataset B into the electrostatic imbalance assessment strategy to calculate the surface electrostatic imbalance degree. ; S15: Based on the structural resonance distortion rate output in step S13 and the surface electrostatic imbalance output in step S14, plot the environmental risk coefficient curves for each precision instrument. The functional expression for this curve is: ; in, The environmental risk coefficient of instrument i. These are the coupling weight parameters for resonant distortion and electrostatic imbalance, respectively.

[0007] Specifically, S2 includes the following steps: S21: Deploy m sets of microenvironment monitoring units on the instrument surface to acquire vibration spectrum coherence data in real time. and local pressure change rate ; S22: Based on vibrational spectrum coherence data and local pressure change rate Calculate the composite disturbance index of each instrument; S23: When the composite disturbance index of an instrument is greater than the preset composite disturbance threshold, the first-level environmental control strategy of the aircraft environmental control system is activated, the composite disturbance indices of each instrument are arranged in descending order, and active vibration reduction compensation is implemented for the instruments according to the descending order sequence.

[0008] Specifically, S3 includes the following steps: S31: By deploying a multi-physics sensing network within the cargo hold, operational status data of various precision instruments and multi-field coupling data of the cargo hold environment are acquired. The operational status data of each precision instrument includes: the cumulative material fatigue of each instrument. and packaging stress distribution ; S32: Import the working status data into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions.

[0009] Specifically, S4 includes the following steps: S41: Based on the data on the types of precision instruments, extract the environmental tolerance range of the precision instruments under preset aircraft flight conditions. ; S42: Based on the environmental adaptability of each precision instrument under the flight conditions obtained from the assessment, determine the execution of the second-level environmental control strategy, specifically including: when Maintain the first-level environmental control strategy; when or At that time, the second-level environmental control strategy parameter adjustment is triggered.

[0010] Specifically, S5 includes: S51: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and extract the real-time structural resonance distortion rate and real-time surface electrostatic imbalance from the real-time environmental risk coefficient curves. S52: When the real-time structural resonance distortion rate When the distortion exceeds the structural resonance threshold, adjust the damping ratio of the active frequency reduction device; When the real-time surface electrostatic imbalance When the charge neutralization rate Q of the ion curtain is greater than the surface electrostatic balance threshold, adjust the charge neutralization rate Q. In addition, the aircraft environmental control system based on multimodal data fusion of the present invention includes the following modules: Multiphysics sensing module, environmental risk assessment module, multimodal decision-making module, adaptive optimization module, and main control module; The multiphysics sensing module includes a resonant detection array sensing unit and a non-contact electrostatic imaging unit. The environmental risk assessment module is used to calculate the structural resonance distortion rate and surface electrostatic imbalance in real time. The multimodal decision-making module generates control commands based on the composite disturbance index and environmental adaptability; The adaptive optimization module includes a frequency-adjustable electromagnetic damping unit and a gradient-adjustable ion curtain unit. The main control module is used to control the operation of each module.

[0011] A storage medium storing instructions that, when read by a computer, cause the computer to execute the dynamic optimization method for an aircraft environmental control system based on multimodal data fusion.

[0012] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described dynamic optimization method for an aircraft environmental control system based on multimodal data fusion.

[0013] Compared with the prior art, the technical effects of the present invention are as follows: This invention achieves the following technical effects through a multimodal data fusion mechanism: Firstly, it constructs a dynamic risk perception network based on structural resonance distortion rate and surface electrostatic imbalance, overcoming the limitations of traditional single-point temperature and humidity monitoring. This network accurately captures the unique coupling risks of airframe resonance, electrostatic pulse, and sudden pressure changes in the aviation environment. Specifically, it quantifies the spectral distortion characteristics under high-frequency airflow disturbances through resonance distortion rate, analyzes the triboelectric charge distribution of non-uniform packaging materials in the dry cabin through electrostatic imbalance, and models a three-dimensional spatial abrupt change field using vector pressure gradient, thus solving the false alarm defects of existing systems in scenarios such as transonic flutter and takeoff and landing seal failure. Secondly, this invention employs an environmental adaptability-driven hierarchical environmental control strategy, through the synergistic evaluation of fatigue accumulation rate and packaging stress... The dynamic coupling of the estimated risk field strength and pressure gradient enables a paradigm shift from passive threshold response to active damage suppression. In an 8-hour long-duration test, it effectively suppressed 87% of microcrack propagation and reduced the neutralization efficiency attenuation rate of the ion curtain from 42% to 9%. Simultaneously, the tunable electromagnetic damping unit reduces the resonance amplitude of precision instruments through a logarithmic smoothing adjustment mechanism. This invention establishes an adaptive optimization chain for the entire flight, triggering directional compensation by ranking composite disturbance exponents. Combined with real-time calibration of material conductivity correction factors and attenuation coefficients, it significantly extends the lifespan of precision instruments, effectively solving the problem that air transport of precision instruments, while maintaining transportation timeliness advantages compared to land transport, struggles to guarantee low failure and damage rates. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart illustrating the dynamic optimization method for an aircraft environmental control system based on multimodal data fusion according to the present invention. Figure 2 This is a schematic diagram of the structure of the aircraft environmental control system based on multimodal data fusion according to the present invention. Detailed Implementation

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0017] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0018] Example 1: like Figure 1 As shown in the embodiment of the present invention, the dynamic optimization method for an aircraft environmental control system based on multimodal data fusion is as follows: Figure 1 As shown, the specific steps include the following: S1: Obtain historical environmental control data of the aircraft cargo hold, and plot the environmental risk coefficient curves of each precision instrument in the cargo hold based on the historical environmental control data; S1 includes: S11: Multi-axis sensor arrays are configured on each instrument mounting rack in the cargo hold, wherein the sensor arrays include n sets of heterogeneous sensor nodes distributed on the instrument bearing surface. S12: Collect structural resonant frequency data and electrostatic accumulation gradient data of the instrument bearing surface during historical flights through a sensor array to form resonant frequency dataset A and electrostatic gradient dataset B. S13: Import the resonant frequency dataset A into the resonant distortion rate calculation strategy to calculate the structural resonant distortion rate. ; For example, in this embodiment, a resonant distortion rate calculation strategy is provided, specifically as follows: ; Where x is the sensor node number and n is the total number of sensor nodes; The fundamental frequency offset collected at node x. These represent the maximum and minimum values ​​of the fundamental frequency offset for all nodes, respectively. Let x be the rate of micro-deformation of the instrument casing at node x. The maximum micro-deformation rate of all m surface monitoring points; m is the total number of instrument surface monitoring points. It should be noted that in the aviation environment, due to airflow disturbances and airframe vibrations, the mounting brackets of precision instruments can resonate, leading to instrument damage. This embodiment provides a resonance distortion rate calculation strategy designed to capture the non-uniformity of this resonance (through the distribution of frequency offset) and the severity of deformation (through deformation rate). It combines the relative degree of frequency offset and the relative degree of deformation rate, effectively quantifying the degree of structural resonance distortion. It should also be noted that the frequency offset reflects the relative position of the node's frequency offset within the overall offset. For the deformation rate, a smaller deformation rate indicates greater stiffness, making it easier to transmit vibrations and thus having a greater impact on the overall distortion. The deformation rate is calculated as the ratio of the maximum deformation rate to the current node's deformation rate; the smaller the current node's deformation rate, the larger this ratio, indicating a greater contribution of that node to the overall distortion.

[0019] S14: Simultaneously import the electrostatic gradient dataset B into the electrostatic imbalance assessment strategy to calculate the surface electrostatic imbalance degree. ; For example, in this embodiment, an electrostatic imbalance assessment strategy is provided, specifically as follows: ; in, Let x be the electrostatic potential energy value at node x. The mean of n nodes. Standard deviation; It should be noted that in the aviation environment, static electricity is easily generated due to the dryness (low humidity) inside the cargo hold and the friction between the instruments and packaging materials. Uneven distribution of static electricity can lead to partial discharge and damage to precision instruments. In this embodiment, in order to prevent the accumulation and discharge of charge on the circuit boards of precision instruments, a static imbalance assessment strategy is provided. The degree of static imbalance is assessed by calculating the degree of deviation of the potential at each point from the overall distribution (mean and standard deviation).

[0020] S15: Based on the structural resonance distortion rate output in step S13 and the surface electrostatic imbalance output in step S14, plot the environmental risk coefficient curves for each precision instrument. The functional expression for this curve is: ; in, The environmental risk coefficient of instrument i. These are the coupling weight parameters for resonant distortion and electrostatic imbalance, respectively.

[0021] It should be noted that in the aviation environment, both resonance distortion and electrostatic imbalance are risk factors for the failure of precision instruments, and they can influence each other. For the first term in the environmental risk coefficient curves of various precision instruments, the exponential form emphasizes the amplification effect of electrostatic imbalance. The second term reflects the rate of deterioration of electrostatic imbalance through the rate of change of electrostatic imbalance; the greater the rate of change, the higher the risk.

[0022] S2: Continuously monitor the multimodal sensor data of various precision instruments in the cargo hold and make judgments on the execution of the first-level environmental control strategy; S2 includes the following specific steps: S21: Deploy m sets of microenvironment monitoring units on the instrument surface to acquire vibration spectrum coherence data in real time. and local pressure change rate ; S22: Based on vibrational spectrum coherence data and local pressure change rate Calculate the composite disturbance index of each instrument; For example, in this embodiment, taking the i-th instrument as an example, a strategy for obtaining the composite disturbance index is provided, specifically as follows: ; in, Let be the composite perturbation index of the i-th instrument; The maximum coherence is preset; These are the weighting coefficients. The critical pressure gradient of the material; It should be noted that vibration spectrum coherence reflects the degree of coherence between the vibration signal and a reference signal (such as engine vibration), and is used to determine the source of vibration. The higher the coherence, the greater the likelihood that the vibration originates from the aircraft itself (such as the engine), and the more regular the impact on precision instruments, the greater the potential harm. Regarding the sudden changes in local air pressure within the cargo hold caused by the aircraft's altitude climb, the local air pressure change rate reflects the severity of the pressure change. It should also be noted that sudden pressure changes can lead to a sharp increase in the pressure difference between the inside and outside of the instrument's sealed cavity, causing seal failure or structural damage. Normalization using the critical pressure gradient, followed by saturation processing using the arctan function, can prevent excessively large values ​​from dominating.

[0023] S23: When the composite disturbance index of an instrument is greater than the preset composite disturbance threshold, the first-level environmental control strategy of the aircraft environmental control system is activated, the composite disturbance indices of each instrument are arranged in descending order, and active vibration reduction compensation is implemented for the instruments according to the descending order sequence.

[0024] S3: By deploying a multi-physics field sensor network in the cargo hold, the working status data of each precision instrument and the multi-field coupling data of the cargo hold environment are obtained, and the working status data is imported into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions. S3 includes the following steps: S31: By deploying a multi-physics sensing network within the cargo hold, operational status data of various precision instruments and multi-field coupling data of the cargo hold environment are acquired. The operational status data of each precision instrument includes: the cumulative material fatigue of each instrument. and packaging stress distribution ; S32: Import the working status data into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions.

[0025] For example, in this embodiment, an environmental adaptability assessment strategy is provided, specifically as follows: in, Let i be the environmental adaptability of the i-th instrument; For material property coefficients, Let be the air pressure gradient around the i-th instrument.

[0026] It should be noted that, regarding the first item, Indicates fatigue accumulation rate and packaging stress distribution. It reflects the stress difference between the internal cavity and the outer shell of the instrument. The product of the two terms aims to capture the stress-fatigue synergistic effect, that is, fatigue damage will be accelerated under high stress environment; the first term can effectively capture the propagation of microcracks caused by vibration in precision instruments during aircraft transportation. The second item It is used to quantify the spatial drasticness of air pressure changes and characterize the amplification effect of environmental risks under sudden air pressure changes. The air pressure gradient is taken as a modulus to ensure that the direction of the air pressure gradient does not affect the risk intensity assessment (i.e., there is danger regardless of whether the aircraft is climbing or descending).

[0027] S4: Based on the environmental adaptability of each precision instrument under the flight status obtained from the assessment, make a judgment on the execution of the second-level environmental control strategy; S4 includes the following steps: S41: Based on the data on the types of precision instruments, extract the environmental tolerance range of the precision instruments under preset aircraft flight conditions. ; S42: Based on the environmental adaptability of each precision instrument under the flight conditions obtained from the assessment, determine the execution of the second-level environmental control strategy, specifically including: when Maintain the first-level environmental control strategy; when or At that time, the second-level environmental control strategy parameter adjustment is triggered.

[0028] S5: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and adjust the control parameters of the secondary environmental control strategy according to the execution judgment results.

[0029] S5 includes: S51: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and extract the real-time structural resonance distortion rate and real-time surface electrostatic imbalance from the real-time environmental risk coefficient curves. S52: When the real-time structural resonance distortion rate When the distortion exceeds the structural resonance threshold, adjust the damping ratio of the active frequency reduction device; For example, in this embodiment, a damping ratio adjustment strategy for an active frequency reduction device is provided, specifically as follows: ; in, The adjusted damping ratio; The original damping ratio; For adjustment coefficients, The baseline distortion rate; It should be noted that the damping ratio adjustment is proportional to the logarithm of the resonant distortion rate. When the real-time structural resonant distortion rate... When the value is greater than the reference value, the logarithmic term is positive, and the damping ratio increases; when the value is less than the reference value, the logarithmic term is negative, and the damping ratio decreases. Increasing the damping ratio can reduce the resonance amplitude, so when the resonance distortion rate is high, an adjustment strategy of increasing the damping ratio is adopted, while using a logarithmic function for smooth adjustment to avoid drastic changes.

[0030] When the real-time surface electrostatic imbalance When the charge neutralization rate Q of the ion curtain is greater than the surface electrostatic balance threshold, adjust the charge neutralization rate Q. For example, in this embodiment, a strategy for adjusting the charge neutralization rate Q of an ion curtain is provided, specifically: ; Where Q is the charge neutralization rate. This is a correction factor for the material's electrical conductivity. The attenuation coefficient characterizes the rate at which the neutralization efficiency of the ion curtain decreases over time. It's important to note that the charge neutralization rate is directly proportional to the square of the electrostatic imbalance, and decays exponentially over time. The square relationship indicates that the more severe the electrostatic imbalance, the faster the neutralization rate needs to be. The exponential decay means that even if the electrostatic imbalance remains constant, the neutralization rate will decrease over time (because the charge decreases during neutralization). In aerospace environments, rapid neutralization is necessary to avoid damage to precision instruments; therefore, the initial neutralization rate is high, gradually decreasing thereafter.

[0031] Example 2: like Figure 2 As shown, the aircraft environmental control system based on multimodal data fusion in this embodiment of the invention is as follows: Figure 2 As shown, it includes the following modules: Multiphysics sensing module, environmental risk assessment module, multimodal decision-making module, adaptive optimization module, and main control module; The multiphysics sensing module includes a resonant detection array sensing unit and a non-contact electrostatic imaging unit. The environmental risk assessment module is used to calculate the structural resonance distortion rate and surface electrostatic imbalance in real time. The multimodal decision-making module generates control commands based on the composite disturbance index and environmental adaptability; The adaptive optimization module includes a frequency-adjustable electromagnetic damping unit and a gradient-adjustable ion curtain unit. The main control module is used to control the operation of each module.

[0032] Example 3: This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the aforementioned dynamic optimization method for the aircraft environmental control system based on multimodal data fusion by calling the computer program stored in memory.

[0033] The electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program, which is loaded and executed by the processor to implement the dynamic optimization method for the aircraft environmental control system based on multimodal data fusion provided in the above-described embodiment. The electronic device may also include other components for implementing its functions; for example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0034] Example 4: This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When the computer program runs on the computer device, it causes the computer device to execute the dynamic optimization method of the aircraft environmental control system based on multimodal data fusion.

[0035] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0036] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0037] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0038] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the flow or function according to the embodiments of the present invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives (SSDs).

[0039] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0040] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0041] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only one method, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0042] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0043] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0044] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A dynamic optimization method for aircraft environmental control systems based on multimodal data fusion, characterized in that, The method includes: S1: Obtain historical environmental control data of the aircraft cargo hold, and plot the environmental risk coefficient curves of each precision instrument in the cargo hold based on the historical environmental control data; S2: Continuously monitor the multimodal sensor data of various precision instruments in the cargo hold and make judgments on the execution of the first-level environmental control strategy; S3: By deploying a multi-physics field sensor network in the cargo hold, the working status data of each precision instrument and the multi-field coupling data of the cargo hold environment are obtained, and the working status data is imported into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions. S4: Based on the environmental adaptability of each precision instrument under the flight status obtained from the assessment, make a judgment on the execution of the second-level environmental control strategy; S5: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and adjust the control parameters of the secondary environmental control strategy according to the execution judgment results.

2. The dynamic optimization method for aircraft environmental control systems based on multimodal data fusion according to claim 1, characterized in that, S1 includes: S11: Multi-axis sensor arrays are configured on each instrument mounting rack in the cargo hold, wherein the sensor arrays include n sets of heterogeneous sensor nodes distributed on the instrument bearing surface. S12: Collect structural resonant frequency data and electrostatic accumulation gradient data of the instrument bearing surface during historical flights through a sensor array to form resonant frequency dataset A and electrostatic gradient dataset B. S13: Import the resonant frequency dataset A into the resonant distortion rate calculation strategy to calculate the structural resonant distortion rate. ; S14: Simultaneously import the electrostatic gradient dataset B into the electrostatic imbalance assessment strategy to calculate the surface electrostatic imbalance degree. .

3. The dynamic optimization method for aircraft environmental control systems based on multimodal data fusion according to claim 2, characterized in that, S1 also includes: S15: Based on the structural resonance distortion rate output in step S13 and the surface electrostatic imbalance output in step S14, plot the environmental risk coefficient curves for each precision instrument. The functional expression for this curve is: ； in, The environmental risk coefficient of instrument i. These are the coupling weight parameters for resonant distortion and electrostatic imbalance, respectively.

4. The dynamic optimization method for aircraft environmental control systems based on multimodal data fusion according to claim 3, characterized in that, S2 includes the following specific steps: S21: Deploy m sets of microenvironment monitoring units on the instrument surface to acquire vibration spectrum coherence data in real time. and local pressure change rate ; S22: Based on vibrational spectrum coherence data and local pressure change rate Calculate the composite disturbance index of each instrument; S23: When the composite disturbance index of an instrument is greater than the preset composite disturbance threshold, the first-level environmental control strategy of the aircraft environmental control system is activated, the composite disturbance indices of each instrument are arranged in descending order, and active vibration reduction compensation is implemented for the instruments according to the descending order sequence.

5. The dynamic optimization method for an aircraft environmental control system based on multimodal data fusion according to claim 4, characterized in that, S3 includes the following steps: S31: By deploying a multi-physics sensing network within the cargo hold, operational status data of various precision instruments and multi-field coupling data of the cargo hold environment are acquired. The operational status data of each precision instrument includes: the cumulative material fatigue of each instrument. and packaging stress distribution ; S32: Import the working status data into the environmental stability assessment strategy to evaluate the environmental adaptability of each precision instrument under flight conditions.

6. The dynamic optimization method for an aircraft environmental control system based on multimodal data fusion according to claim 5, characterized in that, S4 includes the following steps: S41: Based on the data on the types of precision instruments, extract the environmental tolerance range of the precision instruments under preset aircraft flight conditions. ; S42: Based on the environmental adaptability of each precision instrument under the flight conditions obtained from the assessment, determine the execution of the second-level environmental control strategy, specifically including: when Maintain the first-level environmental control strategy; when or At that time, the second-level environmental control strategy parameter adjustment is triggered.

7. The dynamic optimization method for an aircraft environmental control system based on multimodal data fusion according to claim 6, characterized in that, S5 include: S51: Return to S1, retrieve the environmental risk coefficient curves of each precision instrument in the cargo hold, and extract the real-time structural resonance distortion rate and real-time surface electrostatic imbalance from the real-time environmental risk coefficient curves. S52: When the real-time structural resonance distortion rate When the distortion exceeds the structural resonance threshold, adjust the damping ratio of the active frequency reduction device; When the real-time surface electrostatic imbalance When the charge neutralization rate Q of the ion curtain is greater than the surface electrostatic balance threshold, adjust the charge neutralization rate Q.

8. An aircraft environmental control system based on multimodal data fusion, used to implement the dynamic optimization method for the aircraft environmental control system based on multimodal data fusion as described in any one of claims 1-7, characterized in that, The system includes the following modules: Multiphysics sensing module, environmental risk assessment module, multimodal decision-making module, adaptive optimization module, and main control module; The multiphysics sensing module includes a resonant detection array sensing unit and a non-contact electrostatic imaging unit. The environmental risk assessment module is used to calculate the structural resonance distortion rate and surface electrostatic imbalance in real time. The multimodal decision-making module generates control commands based on the composite disturbance index and environmental adaptability; The adaptive optimization module includes a frequency-adjustable electromagnetic damping unit and a gradient-adjustable ion curtain unit. The main control module is used to control the operation of each module.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic optimization method for an aircraft environmental control system based on multimodal data fusion as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to execute the instructions, causing the device to perform operations that implement the dynamic optimization method for an aircraft environmental control system based on multimodal data fusion as described in any one of claims 1-7.

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