Airplane ground deicing configuration parameter collaborative optimization method, system and computer device

Abstract

The present application belongs to the technical field of aircraft ground deicing, and discloses a method and system for collaborative optimization of aircraft ground deicing configuration parameters, and a computer device. The method comprises: obtaining a correlation topology result of the aircraft ground deicing configuration parameters by analyzing the distribution fitting law of multi-scale correlation coupling of the aircraft ground deicing configuration parameters and the quantitative deicing efficiency relationship, and constructing a correlation coupling graph model of the deicing configuration parameters based on the result; establishing a collaborative optimization model of the deicing configuration parameters with the objectives of maximum safety margin of the retention time, shortest deicing time, most balanced configuration parameters, and least deicing fluid consumption; simplifying the complex constraints of the deicing configuration parameters, and solving the collaborative optimization model of the deicing configuration parameters after relaxation. The average absolute error between the measured result and the calculated value meets the airport operation requirements, and the collaborative optimization and solving method of the deicing configuration parameters reduces the deicing fluid consumption on the basis of ensuring absolute safety and efficient operation in combination with the actual deicing configuration parameters of the airport.

Description

Technical Field

[0001] This invention belongs to the field of aircraft ground de-icing technology, and particularly relates to a method, system, and computer equipment for collaborative optimization of aircraft ground de-icing configuration parameters. Background Technology

[0002] Aircraft ground de-icing is a crucial aspect of ensuring operational safety at high-latitude, high-altitude airports. Coordinated optimization of aircraft ground de-icing parameters under high-density aircraft operation is essential for improving airport management capabilities. Furthermore, domestic and international analysts have employed multi-objective optimization, heuristic algorithms, and integer programming to analyze optimization methods for single or multiple de-icing configuration parameters. While these methods have shown some effectiveness, they have not yet achieved coordinated optimization among multiple de-icing configuration parameters.

[0003] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0004] (1) The factors constraining the holding time only include the outside temperature, without considering the influence of the de-icing fluid's own temperature, spray flow rate, and spray pressure, and without considering the influence of the overall operating mode and external environment on the de-icing time, which may lead to the waste of de-icing resources and environmental pollution.

[0005] (2) All parameters are set manually, which can easily lead to insensitivity to changes in the external environment, waste of de-icing fluid and residual ice, seriously threatening the safety of aircraft operation.

[0006] The difficulty in solving the above problems and defects is as follows:

[0007] (1) Based on the basic characteristics and de-icing mechanism of aircraft de-icing fluid, it can be seen that the de-icing and de-icing times of de-icing fluids with different parameters are inconsistent when sprayed onto the aircraft surface, and the failure time of de-icing fluids also varies. Therefore, it is necessary to carry out collaborative optimization based on the configuration parameters of de-icing fluids and establish a collaborative optimization model of de-icing configuration parameters under multi-dimensional constraints to improve the operational support capability of airports under adverse conditions.

[0008] (2) According to the systematic analysis of de-icing parameters and the relevant analysis of safety performance envelope, the parameters related to de-icing fluid configuration include four parameters: de-icing fluid concentration, de-icing fluid temperature, injection flow rate, and injection pressure. These parameters are constrained by ambient temperature, type of ice accumulation model, and thickness. Therefore, it is necessary to clarify the basic characteristics of each parameter.

[0009] (3) By analyzing the de-icing mechanism, it can be basically known that the de-icing process rate is mainly related to the de-icing fluid concentration, de-icing fluid temperature and jet flow rate. The de-icing fluid retention process is mainly determined by the thickness, viscosity and freezing point of the liquid film covering the aircraft surface. Therefore, the relevant parameters for the de-icing fluid retention time are the de-icing fluid concentration and jet pressure. At the same time, the temperature change in the external environment affects the viscosity of the de-icing fluid and the fluidity of the liquid film, which are also important parameters to be considered for the retention time.

[0010] The significance of solving the above problems and defects is as follows:

[0011] (1) The constructed de-icing configuration parameter correlation coupling graph model can capture the influence of different parameter combinations on the de-icing effect, and the attribute parameters of the graph can meet the quantitative requirements of the collaborative optimization objective function;

[0012] (2) The established aircraft ground de-icing configuration parameter collaborative optimization model can consider four objectives and corresponding constraints: de-icing effectiveness (de-icing time and holding time), parameter balance, and operation economy. It also has the influence of configuration parameter correlation on the overall de-icing process of the collaborative optimization model and practical application value.

[0013] (3) The proposed L 2 The M-algorithm can effectively solve the problem of solving the multi-objective collaborative optimization model of de-icing configuration parameters. The results show that the average absolute error between the experimental measurement results and the calculated values ​​of de-icing effectiveness meets the requirements of airport operation specifications. Combined with the comparative analysis of the actual airport de-icing configuration parameters, it can be seen that the proposed collaborative optimization and solution method of de-icing configuration parameters can reduce the amount of de-icing fluid used while ensuring absolute safety and efficient operation.

[0014] To address the problem that current discrete, fixed de-icing parameter configuration tables are no longer suitable for de-icing parameter optimization under multi-scale factors and multi-dimensional constraints, this invention proposes a collaborative optimization method and system for aircraft ground de-icing configuration parameters, considering the correlation of de-icing fluid configuration at the microscale and its multiple impacts on de-icing time and holding time. Summary of the Invention

[0015] To overcome the problems existing in related technologies, the present invention discloses a method, system, and computer equipment for collaborative optimization of aircraft ground de-icing configuration parameters.

[0016] The technical solution is as follows: A method for collaborative optimization of aircraft ground de-icing configuration parameters, comprising the following steps:

[0017] S1. By analyzing the distribution fitting law of multi-scale correlation and coupling of aircraft ground de-icing configuration parameters and quantifying the de-icing efficiency relationship, the correlation topology results of aircraft ground de-icing configuration parameters are obtained, and a correlation coupling graph model of de-icing configuration parameters is constructed based on the correlation topology results.

[0018] S2. Referring to the constructed de-icing configuration parameter correlation and coupling diagram model, establish a collaborative optimization model for de-icing configuration parameters with the objectives of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid used.

[0019] S3 simplifies the complex constraints of the de-icing configuration parameters and solves the relaxed de-icing configuration parameter collaborative optimization model.

[0020] Another object of the present invention is to provide a system for implementing the aforementioned method for collaborative optimization of aircraft ground de-icing configuration parameters. This airport runway airworthiness assessment system under icy and snowy conditions includes:

[0021] The de-icing efficiency energy conversion module will be used at different ambient temperatures T E Icing type I M and icing thickness I D The de-icing efficiency parameter that affects the safety and stability of the operation process under certain conditions is quantified as de-icing time. and holding time t H ;

[0022] The parameter correlation fitting module, based on the given limits for different parameter definitions, systematically analyzes the de-icing time under different de-icing parameters. and holding time t H The correlation between parameters was analyzed, and the fit distribution among the parameters was examined.

[0023] The parameter correlation coupling graph model construction module, based on the correlation relationship and fitting distribution obtained by the parameter correlation fitting module, reasonably determines the de-icing configuration parameters according to the real-time evolution process perception of the snow and ice environment and the icing model, and constructs the de-icing configuration parameter correlation coupling graph model based on the correlation topology results;

[0024] The parameter collaborative optimization model design module, referring to the parameter correlation coupling graph model construction module, establishes a collaborative optimization model for de-icing configuration parameters with the objectives of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid used.

[0025] The collaborative optimization model solving module introduces the Lagrange relaxation method to simplify the complex constraints of the de-icing configuration parameters in the collaborative optimization model design module, and uses the Levenberg-Marquardt algorithm to solve the relaxed collaborative optimization model of the de-icing configuration parameters.

[0026] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the aircraft ground de-icing configuration parameter collaborative optimization method.

[0027] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the method for collaborative optimization of aircraft ground de-icing configuration parameters.

[0028] Another objective of this invention is to provide an aircraft ground de-icing robot that performs the aircraft ground de-icing configuration parameter collaborative optimization method.

[0029] Combining all the above technical solutions, the advantages and positive effects of this invention are as follows:

[0030] First, in view of the technical problems existing in the prior art and the difficulty of solving these problems, and closely combining the technical solution to be protected by this invention with the results and data during the research and development process, this paper analyzes in detail how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about after solving the problems, as described in detail below:

[0031] This invention provides a collaborative optimization method and system for aircraft ground de-icing configuration parameters. By analyzing the distribution fitting law of multi-scale correlation and coupling of aircraft ground de-icing configuration parameters and quantifying the de-icing efficiency relationship, it systematically analyzes the correlation topology results of aircraft ground de-icing configuration parameters and constructs a correlation and coupling graph model of de-icing configuration parameters. A collaborative optimization model for de-icing configuration parameters is established with the objectives of maximizing the safety margin of hold time, minimizing de-icing time, achieving the most balanced configuration parameters, and minimizing de-icing fluid consumption. The Lagrange relaxation method is introduced to simplify the complex constraints of the de-icing configuration parameters, and the Levenberg-Marquardt algorithm is used to solve the relaxed collaborative optimization model of de-icing configuration parameters (L...). 2 M). The collaborative optimization method for aircraft ground de-icing configuration parameters provided by this invention has advantages such as fast response, adaptability to environmental changes, and ease of implementation, and has good application prospects.

[0032] Compared to existing technologies, the advantages and inventive technical effects of this invention also include: This invention provides a collaborative optimization method for aircraft ground de-icing configuration parameters. Considering the influence of de-icing configuration parameters on the transient process and overall effectiveness of de-icing at the microscale, it constructs a coupled graph model of de-icing configuration parameters based on a systematic analysis of parameter correlation and fitting, designs a multi-objective collaborative optimization model for de-icing configuration parameters, and proposes a Lagrange relaxation-based Levenberg-Marquardt algorithm (L...). 2The M algorithm is used to solve the model. (1) The constructed de-icing configuration parameter association coupling graph model can capture the influence of different parameter combinations on the de-icing effect. The attribute parameters of the graph can meet the quantitative requirements of the collaborative optimization objective function. (2) The established aircraft ground de-icing configuration parameter collaborative optimization model can consider four objectives and corresponding constraints: de-icing effect (de-icing time and holding time), parameter balance, and operation economy. It also has the influence of configuration parameter association on the overall de-icing process of the collaborative optimization model and practical application value. (3) The proposed L 2 The M-algorithm can effectively solve the problem of solving the multi-objective collaborative optimization model of de-icing configuration parameters. The results show that the average absolute error between the experimental measurement results and the calculated values ​​of de-icing effectiveness meets the requirements of airport operation specifications. Combined with the comparison of actual airport de-icing configuration parameters, it can be seen that the proposed collaborative optimization and solution method of de-icing configuration parameters can reduce the amount of de-icing fluid used while ensuring absolute safety and efficient operation.

[0033] Secondly, considering the technical solution as a whole or from the perspective of the product, the technical effects and advantages of the technical solution to be protected by this invention are specifically described as follows:

[0034] The present invention provides a collaborative optimization method for aircraft ground de-icing configuration parameters. This method is based on the distribution fitting law of multi-scale correlation and coupling of aircraft ground de-icing configuration parameters and the quantification relationship of de-icing efficiency. It systematically analyzes the correlation topology results of aircraft ground de-icing configuration parameters and constructs a correlation and coupling graph model of de-icing configuration parameters. A collaborative optimization model for de-icing configuration parameters is established with the objectives of maximizing the safety margin of hold time, minimizing de-icing time, achieving the most balanced configuration parameters, and minimizing de-icing fluid consumption. The Lagrange relaxation method is introduced to simplify the complex constraints of the de-icing configuration parameters, and the Levenberg-Marquardt algorithm is used to solve the relaxed collaborative optimization model of de-icing configuration parameters (L...). 2 M). This invention has advantages such as fast response, adaptability to environmental changes, and ease of implementation, and has good application prospects.

[0035] Third, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0036] (1) The airport de-icing department can replace the original fixed parameter configuration method with the aircraft ground de-icing configuration parameter system after the transformation of the technical solution, which greatly avoids resource waste, reduces de-icing costs, and ensures the safe operation of aircraft. The technical solution of the present invention has high commercial value after transformation.

[0037] (2) At present, there are only studies on the configuration optimization of a single parameter for aircraft ground de-icing, and there is no research on the collaborative optimization of multiple parameters for aircraft ground de-icing. This invention fills the technological gap in this field.

[0038] (3) The existing aircraft ground de-icing parameter configuration model does not take into account the four objectives of de-icing time, holding time, parameter balance and operation economy. However, the collaborative optimization model provided by this invention fully considers the above objectives and optimizes them, thus solving the technical problem that people have been eager to solve but have never been able to achieve.

[0039] (4) The parameter co-optimization method provided by the present invention overcomes the problem that the existing de-icing parameter configuration table relies on subjective experience accumulated over a long period of time to be superior to the objective parameter model, and makes up for the shortcomings of the current discretized and fixed de-icing parameter configuration table, which can no longer adapt to the optimization of de-icing parameters under the coupling of multi-scale factors and multi-dimensional constraints. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0041] Figure 1 This is a flowchart of the collaborative optimization method for aircraft ground de-icing configuration parameters provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the collaborative optimization method for aircraft ground de-icing configuration parameters provided in an embodiment of the present invention.

[0043] Figure 3(a) shows the fitting results of the de-icing fluid concentration and de-icing time provided in the embodiment of the present invention. M =1, I D =1.0-2.0mm, T ADF =50℃ Schematic diagram;

[0044] Figure 3(b) shows the fitting results of the de-icing fluid concentration and de-icing time provided in the embodiment of the present invention. M =1, I D =1.0-2.0mm, f ADF =0.3L / min schematic diagram;

[0045] Figure 3(c) shows the fitting results of the de-icing fluid concentration and de-icing time provided in the embodiment of the present invention. M =1,T ADF =50℃, f ADF =0.3L / min schematic diagram;

[0046] Figure 3(d) shows the fitting results of the de-icing fluid concentration and de-icing time provided in the embodiment of the present invention. D =1.0-2.0mm, T ADF =50℃, f ADF =0.3L / min schematic diagram;

[0047] Figure 4(a) shows the fitting results of the de-icing fluid concentration and holding time provided in the embodiment of the present invention, where T... E Schematic diagram of -15℃;

[0048] Figure 4(b) shows the fitting results of the de-icing fluid concentration and holding time provided in the embodiment of the present invention, where p ADF =0.05MPa schematic diagram;

[0049] Figure 5 This is a correlation and coupling diagram of the de-icing configuration parameters provided in the embodiments of the present invention;

[0050] Figure 6 The ground de-icing parameter L provided in this embodiment of the invention. 2 Basic flowchart of the M-solution algorithm;

[0051] Figure 7 This is a schematic diagram of the airport runway airworthiness assessment system under icy and snowy conditions provided in an embodiment of the present invention;

[0052] Figure 8(a) is a schematic diagram comparing the de-icing fluid concentration in the utility parameters solved by different optimization methods provided in the embodiments of the present invention.

[0053] Figure 8(b) shows the overall balance of de-icing parameters in the comparison of utility parameters solved by different optimization methods provided in the embodiments of the present invention. ) Schematic diagram;

[0054] Figure 8(c) is a schematic diagram of de-icing time in the comparison of utility parameters solved by different optimization methods provided in the embodiments of the present invention.

[0055] Figure 8(d) is a schematic diagram of the retention time in the comparison of utility parameters solved by different optimization methods provided in the embodiments of the present invention.

[0056] In the diagram: 1. De-icing efficiency quantification module; 2. Parameter correlation fitting module; 3. Parameter correlation coupling graph model construction module; 4. Parameter collaborative optimization model design module; 5. Collaborative optimization model solution module. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] I. Explanation of the Implementation Example:

[0059] like Figure 1As shown, the aircraft ground de-icing configuration parameter collaborative optimization method provided in this embodiment of the invention includes the following steps:

[0060] S101, by analyzing the distribution fitting law of multi-scale correlation and coupling of aircraft ground de-icing configuration parameters and quantifying the de-icing efficiency relationship, and then systematically analyzing the correlation topology results of aircraft ground de-icing configuration parameters and constructing a correlation and coupling graph model of de-icing configuration parameters;

[0061] S102, referring to the constructed de-icing configuration parameter correlation coupling diagram model, establish a de-icing configuration parameter collaborative optimization model with the goal of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid used;

[0062] S103 introduces the Lagrange relaxation method to simplify the complex constraints of the de-icing configuration parameters, and employs the Levenberg-Marquardt algorithm to solve the relaxed de-icing configuration parameter co-optimization model (L...). 2 M).

[0063] like Figure 2 As shown in the figure, this embodiment of the invention provides a schematic diagram of a collaborative optimization method for aircraft ground de-icing configuration parameters.

[0064] Example 1

[0065] Based on the above embodiments of the present invention Figure 1 The recorded method for collaborative optimization of aircraft ground de-icing configuration parameters is further preferably described in step S101, in which the method for quantifying de-icing efficiency specifically includes the following steps:

[0066] (1) After the de-icing operation begins, if there is no obvious ice formation on the surface of the ice model and the overall average temperature is greater than 0℃ and maintained for more than 5~10s, the de-icing is considered to be completed. This period of time can be expressed as the de-icing time, denoted as t. z .

[0067] (2) Regarding the residual temperature effect of the waste liquid, after the de-icing operation is completed, the temperature of the model surface decreases with the external environment. When the surface temperature is all below 0℃ and at least 20% of the surface can be observed to have ice crystals in the field of view of the electron microscope, this period of time is called the holding time, denoted as t. H .

[0068] (3) According to the airport's operating specifications under icy and snowy conditions, the operating conditions under different ambient temperatures T E Icing type I M and icing thickness I D Under certain conditions, the de-icing efficiency parameter that primarily affects the safety and stability of the operation process is quantified as de-icing time t. z and holding time t H Factors affecting defrosting time include the concentration of the defrosting fluid, c. ADFDe-icing fluid temperature T ADF Jet flow rate f ADF Factors affecting the holding time include the concentration of the de-icing fluid, c. ADF Injection pressure p ADF Among them, the types of ice formation are generally divided into three categories: clear ice, frost ice, and mixed ice, which can be quantified as follows:

[0069] (1)

[0070] Example 2

[0071] Based on the above embodiments of the present invention Figure 1 The recorded method for collaborative optimization of aircraft ground de-icing configuration parameters is further preferably described in step S101, where the multi-scale correlation coupling distribution fitting method for the de-icing configuration parameters is shown in Figures 3(a) to 3(d), and specifically includes the following steps:

[0072] (I) Fitting characteristics of de-icing time and de-icing configuration parameters. De-icing time refers to the continuous process of hot fluid injection into the icing model undergoing solid-liquid / gas phase change. Combining the attributes of the de-icing operation module, the correlation formula for the average heat transfer characteristics of a single circular nozzle jet is given, namely:

[0073] (2)

[0074] In the formula: (Nu) ADF is the Nusselt coefficient of the de-icing fluid crossing the convection boundary under the current ratio, and its physical meaning can be understood macroscopically as the rate of ice melting; H is the straight-line distance between the de-icing fluid nozzle orifice and the surface of the icing model, D is the diameter of the de-icing fluid nozzle, and r is the radius of the jet impact stagnation zone on the surface. The Reynolds coefficient of the de-icing fluid at the current ratio is given by the specific heat velocity of the de-icing fluid (i.e., the injection flow rate f). ADF Pr is Prandtl's constant, which is related to the dynamic viscosity coefficient of the de-icing fluid (i.e., the concentration of the de-icing fluid, c). ADF ).

[0075] (II) As shown in Figures 4(a) to 4(b), the fitting characteristics of the holding time and de-icing configuration parameters are obtained. After de-icing, a liquid film of de-icing fluid is formed on the surface of the icing model. Essentially, this reduces the probability of secondary icing of the aircraft by lowering the surface freezing point and viscosity. The correlation formula for the icing characteristics of the aircraft surface is given, namely:

[0076] (3)

[0077] In the formula: f ice P represents the icing rate of the aircraft surface after being covered by a de-icing liquid film. sat R represents the saturated absolute humidity of the moist air on the surface of the de-icing liquid film. v h is the gas constant of water vapor;w The heat transfer coefficient of the de-icing liquid film surface is related to the thickness of the viscous liquid film (i.e., the injection pressure p). ADF Related to S c Schmidt constant, generally refers to the diffusivity of the mass flow rate at the surface of the liquid film. This represents the icing rate coefficient of the aircraft surface after being covered by a de-icing liquid film. This is the current temperature value; This represents the pressure value.

[0078] Example 3

[0079] Based on the above embodiments of the present invention Figure 1 The recorded method for collaborative optimization of aircraft ground de-icing configuration parameters is further preferably described in step S101, such as... Figure 5 As shown, the construction of the de-icing configuration parameter correlation coupling graph model specifically includes the following steps: De-icing configuration parameter correlation coupling graph model

[0080] (a) Construct a graph model G=(V,E) for the dynamic evolution attributes of each de-icing microprocess; where V={v i Let |i=1,2,3,4}, V be the set of nodes containing all de-icing parameters, v i These are the set values ​​for each configuration parameter; E={e ij =(v i, v j ):v i ,v j ∈V;i≠j}, E is the set of edges that exist due to the correlation between each de-icing parameter and the efficiency parameter, and the connection weights of each edge are different. The adjacency matrix A=[a ij ] 7×7 This indicates the relationship between the parameter nodes, i.e.:

[0081] (4)

[0082] (b) Calculate the anti-adjacency matrix B of the de-icing parameter association coupling graph G, i.e.:

[0083] B=JA(5)

[0084] In the formula: J is a matrix with the same dimension as the adjacency matrix A and all elements are 1; in the relevant theory of graph theory, the Hamiltonian path represents the path that passes through all nodes in the graph. Its physical meaning can be interpreted as the existence of correlation between all de-icing configuration parameters and the mutual influence of the values ​​of each parameter.

[0085] (c) Calculate whether the result of the anti-adjacency matrix as a determinant is 1 to determine whether there is a Hamiltonian path in the graph model that passes through all nodes.

[0086] (d) If det B=1 is obtained from the de-icing configuration parameter coupling diagram G, it indicates that there is a coupling relationship between the de-icing configuration parameters. Environmental parameters are added, and parameter coupling diagrams G are constructed with the de-icing time and holding time as endpoints. Z =(V Z E Z ) and G H =(V H E H The composition of vertices and directed edges in each graph model is as follows:

[0087] (6)

[0088] In the formula: and These are parameter correlation coupling diagrams. Elements and sets of vertices. and These are parameter correlation coupling diagrams. Elements and sets of directed edges; and These are parameter correlation coupling diagrams. Elements and sets of vertices. and These are parameter correlation coupling diagrams. Elements and sets of directed edges.

[0089] (e) Calculate the ratio of the number of Hamiltonian paths to the number of directed paths with two or more nodes to characterize the coupled graph model G of the aircraft ground de-icing configuration parameters. z (or G) H The correlation between the de-icing parameter efficiency λ Z (or λ) H ).

[0090] Let G z and G H Let B be a directed acyclic graph with vertices representing de-icing configuration, environment, and performance parameters, and B... Z and B H Figure G z and G H Let the anti-adjacency matrix be:

[0091] (7)

[0092] but , ,and and The figures are respectively and The number of directed paths with k vertices; where I is the identity matrix, N Z and N H G Z and G H The number of vertices, where x is the independent variable;

[0093] In summary, the correlation between the parameters and their effectiveness λ Z and λ H The calculation formula is:

[0094] (8)

[0095] (9)

[0096] In the formula, For the diagram There is N Z The number of directed paths to each vertex; In order to be in There is N H The number of directed paths to each vertex;

[0097] (f) Using the parametric coupling graph G Z and G H Weight of the middle edge and This indicates the degree of correlation in the performance parameter transformation between parameters, i.e.:

[0098] (10)

[0099] (11)

[0100] In the formula, and These are parameter correlation coupling diagrams. and The number of Hamiltonian paths between two vertices in the equation; and These represent the number of vertices within the path. and These are the independent variables for the vertex parameters within this path; and These are the de-icing parameter coupling functions; for and The coefficients of the coupling function associated with the de-icing parameters; This represents the correlation value between performance parameter conversions between parameters;

[0101] (g) Regarding the above weights and Normalization yields the parameter correlation coupling graph and weight matrix and And calculate the parameter correlation coupling graph. and The in-degree diagonal matrix of each node and ,Right now:

[0102] (12)

[0103] (13)

[0104] (h) Obtain the parameter correlation coupling diagram G from step (g). Z and G H Normalized and normalized Laplace matrix L Z and L H ,Right now:

[0105] (14)

[0106] (15).

[0107] Example 4

[0108] Based on the above embodiments of the present invention Figure 1 The described method for collaborative optimization of aircraft ground de-icing configuration parameters is further preferably described in step S102, in which the method for establishing the collaborative optimization model of de-icing configuration parameters specifically includes the following steps:

[0109] (A) From the perspective of airport operation safety assurance, referring to the aircraft ground de-icing parameter correlation coupling diagram G Z and G H The configuration and properties were designed with four synergistic optimization objectives: maximizing the safety margin of the de-icing time, minimizing the de-icing time, achieving the most balanced configuration parameters, and minimizing the de-icing fluid consumption. Based on the properties of the Laplace matrix, the final fitted values ​​of the de-icing efficiency parameters can be obtained by the following matrix calculation:

[0110] (16)

[0111] In the formula, Ft z The figure shows the fitting estimation results of the de-icing efficiency parameters under the influence of multiple factors. ζ and ξ are the parameter correlation coupling diagrams G and G, respectively. Z and G H A vector consisting of all parameter vertex elements. For parameter performance correlation, For parameter correlation coupling graph G Z The normalized and normalized Laplace matrix, For parameter performance correlation, For parameter correlation coupling graph G H The normalized and normalized Laplace matrix.

[0112] (B) Regarding maximizing the safety margin of the holding time, this mainly refers to obtaining the fitted value of the holding time under the combined influence of multiple external environmental factors and de-icing parameter configuration factors, which are coupled together. The largest discrepancy exists between this value and the expected holding time determined by considering departing flight density and de-icing resources.

[0113] (17)

[0114] In the formula, The expected hold time value is determined under the combined constraints of the density of aircraft undergoing de-icing operations and the configuration of de-icing vehicles. It is generally determined by the de-icing operation control decision-making department. To maintain the maximum expected time difference;

[0115] (C) For the shortest de-icing time, it mainly refers to the de-icing time correlation coupling value Ft obtained by the combined effect of external environment and operation configuration parameters. z The error between the expected value and the actual value is minimized, that is:

[0116] (18)

[0117] In the formula: The estimated de-icing time is determined based on the combined constraints of departure de-icing operation resource allocation and external environment, and is generally provided by the airport de-icing operations and support departments; Ft is the de-icing time-related coupling value. z The minimum error between the expected value and the actual value;

[0118] (D) To ensure the optimal balance of configuration parameters, the ratio of the difference between the lower limit and the actual value of each configuration parameter to its upper limit is defined as the balance index of the de-icing parameters. The calculation formula is:

[0119] (19)

[0120] In the formula: and These represent the upper and lower limits of the de-icing configuration parameters, respectively. Based on the balance index of each parameter, the optimization objective function can be derived from... The minimum root mean square error is represented as:

[0121] (20)

[0122] In the formula: This represents the average of the balance indices for each parameter.

[0123] (E) To minimize the amount of de-icing fluid used, construct a coupling graph G showing the relationship between de-icing fluid concentration and de-icing parameters. Z and G H The ratio of the difference in parameter performance correlation is the smallest, that is:

[0124] (twenty one)

[0125] For the de-icing parameter correlation coupling diagram G H The independent variables of the vertex parameters within the third path;

[0126] (F) Based on the actual situation of the de-icing operation and the airport de-icing operation standards and specifications, the primary optimization goal is to maximize the safety margin of the holding time after de-icing Ω1. At the same time, in order to improve operational efficiency and ensure the balance of parameter settings, the secondary goals are to minimize the de-icing time Ω2 and the most balanced configuration parameters Ω3. On the basis of the above, the optimization of the economic efficiency of parameter configuration, i.e., minimizing the amount of de-icing fluid used Ω4, is also considered.

[0127] (G) By designing the objective function for the collaborative optimization of de-icing parameters, the solution space of the de-icing parameters can be basically limited. Combining the evolution law of environmental parameters and real-time status, and referring to the limits of the de-icing configuration parameters, the following constraints can be obtained. Equation (22) is the tolerance constraint of the aircraft ground de-icing configuration parameters, indicating that the configuration of any parameter cannot exceed the set range; Equation (23) is the parameter balance constraint, indicating that the balance range of parameter optimization needs to be controlled according to the environmental perception results; Equation (24) is the hold time safety constraint, indicating that the hold time fitting result after parameter optimization must be greater than the expected result, and an absolute safety margin must be guaranteed within a certain range; Equation (25) is the de-icing correlation coupling constraint, indicating that the parameter correlation coupling degree oriented towards hold time is guaranteed to be greater than the basic setting oriented towards de-icing time, and the parameter correlation coupling diagram G is used. Z and G H The F-norm representation of the Laplacian matrix;

[0128] (twenty two)

[0129] (twenty three)

[0130] (twenty four)

[0131] (25)

[0132] (26)

[0133] In the formula: and These are the upper and lower bounds of the limit threshold for the balance index of de-icing configuration parameters, set according to the external ice and snow environment conditions and evolution results. To ensure an absolute safety margin for de-icing time, a 5-minute time limit is set based on aircraft de-icing operation specifications. The efficiency threshold for de-icing operations is determined by the configuration of the de-icing mechanism and the size parameters of the ice accumulation model, based on the established de-icing platform. Take 0.5 minutes; De-icing parameter correlation coupling diagram G Z The independent variable of the vertex parameter in the i-th path;

[0134] (H) In summary, by setting the objective function and constraints, a collaborative optimization model for aircraft ground de-icing configuration parameters can be obtained.

[0135] Example 5

[0136] Based on the above embodiments of the present invention Figure 1 The described method for collaborative optimization of aircraft ground de-icing configuration parameters is further preferably described in step S103, such as... Figure 6 As shown, the relaxed de-icing configuration parameter co-optimization model (L) is solved based on the Levenberg-Marquardt algorithm under Lagrange relaxation. 2 The method of M) specifically includes the following steps:

[0137] 1) First, based on the objective function and constraints of the collaborative optimization of aircraft ground de-icing configuration parameters, it can be seen that it is a nonlinear programming model with complex parameter relationships. To simplify the solution complexity, the four optimization objectives are combined by weighting them according to their importance and the decision-maker's preferences, resulting in:

[0138] (27)

[0139] In the formula: Ω is the overall objective of the collaborative optimization of aircraft ground de-icing configuration parameters, a1, a2, a3, and a4 are the weight coefficients of each optimization objective, and according to the complexity of the constraints, equations (22), (23), and (25) are regarded as simple constraints of the collaborative optimization model, while equations (24) and (26) are complex constraints; from equation (26), it can be seen that the optimization objective can be transformed into a quadratic function Y of unknown de-icing configuration parameter vectors ξ and ζ. LP ,Right now:

[0140] (28)

[0141] In the formula: The quadratic function is formed by the vector of parameters related to de-icing time, and is calculated by combining the target Ω2 and target Ω3. To preserve the quadratic function formed by the time-dependent parameter vectors, the objective Ω1 and objective Ω4 are calculated together.

[0142] 2) Considering the unsolvability caused by the complex constraints of the de-icing configuration parameters, Y... LP Treating this as the dual problem of the original de-icing configuration parameter co-optimization objective, a Lagrange multiplier factor is introduced to address the complex constraints. and The original problem of co-optimizing de-icing parameters is transformed into Y LR ,Right now:

[0143] (29)

[0144] In the formula: for The quadratic function after adding Lagrange relaxation; for The quadratic function after adding Lagrange relaxation, at the same time and All are greater than 0; Record The Lagrange relaxation dual problem corresponding to the problem of configuring parameters for de-icing is as follows:

[0145] (30)

[0146] (3) Obtaining ambient temperature T based on environmental information acquisition sensors E Icing type I M and icing thickness I D Parameters, substitute them into the model to get Y LD Simplified to:

[0147] (31)

[0148] In the formula: For the simplified dual optimization model, To substitute into the simplified quadratic function, x is the input vector consisting of the independent variables of all nodes in the de-icing configuration parameter associated coupled graph model.

[0149] 4) The LM algorithm is used to iteratively solve the collaborative optimization objective of the de-icing configuration parameters after Lagrange relaxation transformation. First, the subgradient step size of the Lagrange multipliers in the dual optimization model is constructed. ,Right now:

[0150] (32)

[0151] In the formula: This is the subgradient iteration step size adjustment factor. For the number of iterations, This represents the limit of de-icing parameters under the current environmental conditions. After introducing the Lagrange multipliers, the first The result of the next iteration;

[0152] (5) Adjust the subgradient step size Substitute into equation (30) to calculate the gradient expression of the de-icing parameters. With Hessian matrix ,Right now:

[0153] (33)

[0154] (34)

[0155] In the formula: x1-x4 are the independent variables of each de-icing configuration parameter, and the initial values ​​of the de-icing configuration parameters are set according to the external environment information. 0 Then its iterative formula is:

[0156] (35)

[0157] In the formula: I is a 4×4 identity matrix. The step size adjustment factor for the de-icing parameters is changed during the iterative process of collaborative optimization. To improve the accuracy of the optimization, let:

[0158] (36)

[0159] In the formula: To adjust The constant set for the search step size of the de-icing parameters, where ;

[0160] (6) During the iterative process of de-icing parameters, if This indicates that the optimization process does not converge, so the value is reduced. A large-scale search for de-icing parameters is performed, and the Hessian matrix is ​​recalculated. The Lagrange multiplier factor and de-icing parameters are iteratively adjusted until the optimized de-icing parameters meet the allowable error. (a minimum value), and output the collaboratively optimized de-icing configuration parameters and estimated performance parameters;

[0161] (7) For L 2 -M solution algorithm convergence analysis is performed; if A feasible solution for the collaborative optimization model of parameters for aircraft ground de-icing configuration is found, and it satisfies... If a constant exists , so that:

[0162] (37)

[0163] In the formula: for arrive The distance, then the de-icing configuration parameter collaborative optimization model There is a local error bound within N; if the first... Jacobian matrix of the next iteration In solution If there are non-singularities in the data, then the de-icing configuration parameters are... This is an isolated solution.

[0164] Example 6

[0165] like Figure 7 As shown, based on the above embodiments of the present invention Figure 1 The invention provides a method for collaborative optimization of aircraft ground de-icing configuration parameters, and an embodiment of the invention provides an airport runway airworthiness assessment system for implementing the aforementioned ice and snow conditions, comprising:

[0166] De-icing efficiency energy conversion module 1 will be used at different ambient temperatures T E Icing type I M and icing thickness I D The de-icing efficiency parameter that affects the safety and stability of the operation process under certain conditions is quantified as de-icing time. and holding time t H ;

[0167] Parameter correlation fitting module 2, based on the given limits for different parameter definitions, systematically analyzes the de-icing time under different de-icing parameters. and holding time t H The correlation between parameters was analyzed, and the fit distribution among the parameters was examined.

[0168] The parameter correlation coupling graph model construction module 3, based on the correlation relationship and fitting distribution obtained by the parameter correlation fitting module 2, reasonably determines the de-icing configuration parameters according to the real-time evolution process perception of the snow and ice environment and the icing model, and constructs the de-icing configuration parameter correlation coupling graph model based on the correlation topology results;

[0169] Parameter Co-optimization Model Design Module 4, referring to the de-icing configuration parameter association coupling graph model constructed by Parameter Association Coupling Graph Model Construction Module 3, establishes a de-icing configuration parameter co-optimization model with the objectives of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid used.

[0170] The collaborative optimization model solving module 5 introduces the Lagrange relaxation method to simplify the complex constraints of the de-icing configuration parameters in the collaborative optimization model of the parameter collaborative optimization model design module 4, and uses the Levenberg-Marquardt algorithm to solve the relaxed collaborative optimization model of the de-icing configuration parameters.

[0171] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0172] The information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0173] Those skilled in the art will understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the functions described above can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this invention. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0174] II. Application Examples:

[0175] Application examples

[0176] This invention also provides a computer device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0177] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps described in the various method embodiments above.

[0178] This invention also provides an information data processing terminal, which, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments, forming a complete evaluation system. The information data processing terminal is not limited to mobile phones, computers, or switches.

[0179] This invention also provides a server that, when executed on an electronic device, provides a user input interface to implement the steps described in the above method embodiments.

[0180] This invention provides a computer program product that, when run on an electronic device, enables the electronic device to implement the steps described in the various method embodiments above.

[0181] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0182] III. Evidence of the relevant effects of the embodiments:

[0183] As shown in Figures 8(a) to 8(d), the collaborative optimization results comparing the traditional quasi-Newton method and the genetic algorithm solution model are as follows: Figures 8(a) to 8(d) show that, compared with the traditional quasi-Newton method, the proposed collaborative optimization model for aircraft ground de-icing configuration parameters and the Levonberg-Marquardt solution algorithm under Lagrange relaxation reduce the average concentration and average de-icing time of the aircraft de-icing fluid by 7.29% and 0.265 min, respectively, while increasing the average holding time by 0.928 min. Compared with the genetic algorithm, the average concentration and average de-icing time reduce by 4.43% and 0.085 min, respectively, while increasing the average holding time by 1.106 min. Furthermore, the proposed method improves the overall balance performance of de-icing parameters by a maximum of 0.052. These results fully demonstrate that the proposed collaborative optimization method for de-icing fluid configuration parameters balances operational economy and balance while ensuring the safety of airport de-icing operations.

[0184] To verify the scientific validity and rationality of the proposed collaborative optimization model for aircraft ground de-icing configuration parameters and its solution method, the corresponding aircraft ground de-icing fluid configuration parameters were solved using the traditional quasi-Newton method and the genetic algorithm, respectively. The effectiveness of the designed solution algorithm was compared with that of the collaborative optimization results obtained by the traditional quasi-Newton method and the genetic algorithm.

[0185] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for collaborative optimization of aircraft ground de-icing configuration parameters, characterized in that, The method includes the following steps: S1 will be at different ambient temperatures T E Icing type I M and icing thickness I D The de-icing efficiency parameter that affects the safety and stability of the operation process under certain conditions is quantified as de-icing time. and holding time t H ; Based on the defined limits of different parameters, the de-icing time under different de-icing parameters is systematically analyzed. and holding time t H The correlation between parameters was analyzed, and the fit distribution among the parameters was examined. Based on the acquired correlations and fitted distributions, the de-icing configuration parameters are reasonably determined according to the real-time evolution of the snow and ice environment and the icing model. Based on the correlation topology results, a correlation coupling graph model of the de-icing configuration parameters is constructed. S2. Based on the constructed de-icing configuration parameter correlation coupling diagram model, establish a collaborative optimization model for de-icing configuration parameters with the objectives of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid used. S3 introduces the Lagrange relaxation method to simplify the complex constraints of the de-icing configuration parameters into the established collaborative optimization model of de-icing configuration parameters, and uses the Levenberg-Marquardt algorithm to solve the relaxed collaborative optimization model of de-icing configuration parameters.

2. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 1, characterized in that, In step S1, based on the given limits for different parameter definitions, the de-icing time for different de-icing parameters is systematically analyzed. and holding time t H The correlation between parameters and the analysis of the fit distribution among them specifically include the following steps: (I) Fitting characteristics of de-icing time and de-icing configuration parameters. De-icing time is the continuous process of hot fluid injection into the icing model undergoing solid-liquid / gas phase change. Combining the attributes of the de-icing operation module, the correlation formula for the average heat transfer characteristics of a single circular nozzle jet is given: ； In the formula: (Nu) ADF denoted as the Nusselt coefficient for the de-icing fluid crossing the convection boundary under the current ratio, H is the straight-line distance between the de-icing fluid nozzle orifice and the surface of the icing model, D is the diameter of the de-icing fluid nozzle, and r is the radius of the jet impact stagnation zone on the surface. Pr is the Reynolds coefficient of the de-icing fluid at the current ratio; Pr is Prandtl's constant. (II) By maintaining the fitting characteristics of time and de-icing configuration parameters, a liquid film of de-icing fluid is formed on the surface of the icing model after de-icing. By reducing the surface freezing point and viscosity, the probability of secondary icing of the aircraft is reduced, and the correlation formula for the icing characteristics of the aircraft surface is given: ； In the formula: f ice This refers to the icing rate on the aircraft surface after being covered by a de-icing liquid film. p is the icing rate coefficient of the aircraft surface after being covered by a de-icing liquid film. sat R represents the saturated absolute humidity of the moist air on the surface of the de-icing liquid film. v Let be the gas constant of water vapor. h represents the current temperature value. w The heat transfer coefficient of the de-icing liquid film surface. is the pressure value, and Sc is the Schmidt constant.

3. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 1, characterized in that, Step S1 also includes the following steps: (1) After the de-icing operation begins, if there is no obvious ice formation on the surface of the ice model, and the overall average temperature is greater than or equal to 0℃, and the de-icing time is 5s to 10s, then the de-icing is considered complete. The de-icing time is recorded as t. Z ; (2) After the de-icing operation is completed, the temperature of the surface of the ice model decreases with the external environment. When the surface temperature is less than 0℃ and the holding time is t H Inside, ice crystals appearing on more than 20% of the surface are obtained by electron microscopy within the field of view.

4. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 3, characterized in that, The de-icing time t Z Influencing factors include: de-icing fluid concentration c ADF De-icing fluid temperature T ADF Jet flow rate f ADF ; The holding time t H Influencing factors include: de-icing fluid concentration c ADF Injection pressure p ADF ; The icing type I M There are three types of ice: clear ice, frost ice, and mixed ice. Quantified as follows: 。 5. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 1, characterized in that, In step S1, the construction of the de-icing configuration parameter correlation coupling graph model specifically includes the following steps: (a) Construct a graph model G=(V,E) for the dynamic evolution attributes of each de-icing microprocess; where V={v i Let |i=1,2,3,4}, V be the set of nodes containing all de-icing parameters, v i These are the set values ​​for each configuration parameter; E={e ij =(v i, v j ):v i ,v j ∈V;i≠j}, E is the set of edges that exist due to the correlation between each de-icing parameter and the efficiency parameter, and the connection weights of each edge are different. The adjacency matrix A=[a ij ] 7×7 This indicates the relationships between the parameter nodes: （4） (b) Calculate the anti-adjacency matrix B of the de-icing parameter correlation coupling graph G: B=JA(5) In the formula: J is a matrix with the same dimensions as the adjacency matrix A and all elements are 1; (c) Calculate whether the result of the determinant of the anti-adjacency matrix is ​​1 to determine whether there is a Hamiltonian path that passes through all nodes in the graph model; (d) From the de-icing configuration parameter coupling diagram G, we find that det B=1, indicating that there is a coupling relationship between the de-icing configuration parameters. We then add environmental parameters and construct parameter coupling diagrams G with the two de-icing efficiency parameters of de-icing time and holding time as endpoints. Z =(V Z E Z ) and G H =(V H E H The composition of vertices and directed edges in each graph model is as follows: ； In the formula: and These are parameter correlation coupling diagrams. Elements and sets of vertices. and These are parameter correlation coupling diagrams. Elements and sets of directed edges; and These are parameter correlation coupling diagrams. Elements and sets of vertices. and These are parameter correlation coupling diagrams. Elements and sets of directed edges; (e) Calculate the ratio of the number of Hamiltonian paths to the number of directed paths with two or more nodes to characterize the coupling graph G of the aircraft ground de-icing configuration parameters. Z Or parameter correlation coupling graph G H Correlation between de-icing parameter efficiency λ Z Or the correlation between the de-icing parameter efficiency λ H ; Let the parameter correlation coupling graph G be... Z And parameter-related coupling graph G H Let B be a directed acyclic graph with vertices representing de-icing configuration, environment, and performance parameters. Z For parameter correlation coupling graph G Z The anti-adjacency matrix, B H For parameter correlation coupling graph G H Let the anti-adjacency matrix be: ； but , ,and and These are parameter correlation coupling diagrams. Coupling diagram with parameters The number of directed paths with k vertices; In the formula, I is the identity matrix, and N is the identity matrix. Z and N H The parameter correlation coupling graph G is shown below. Z And parameter-related coupling graph G H The number of vertices, where x is the independent variable; De-icing parameter efficiency correlation λ Z and λ H The calculation formula is: ； ； In the formula, For parameter correlation coupling graph There is N Z The number of directed paths to each vertex; In order to be in There is N H The number of directed paths to each vertex; (f) Using the parametric coupling graph G Z And parameter-related coupling graph G H Weight of the middle edge and This indicates the degree of correlation in the performance parameter transformation between parameters, i.e.: ； ； In the formula, and These are parameter correlation coupling diagrams. Coupling diagram with parameters The number of Hamiltonian paths between the two vertices in the equation; and These represent the number of parameter vertices within the Hamiltonian path between the two parameter vertices; and These are the independent variables of the vertex parameters within the Hamiltonian path between the two parameter vertices; and These are the de-icing parameter coupling functions; for and The coefficients of the coupling function associated with the de-icing parameters; This represents the correlation value between performance parameters in the conversion process. (g) Weights and Normalization yields the parameter correlation coupling graph Coupling diagram with parameters weight matrix and And calculate the parameter correlation coupling graph. Coupling diagram with parameters The in-degree diagonal matrix of each node and ,Right now: ； ； (h) Obtain the parameter correlation coupling diagram G from step (g). Z And parameter-related coupling graph G H Normalized and normalized Laplace matrix L Z and Laplace matrix L H ,Right now: ； 。 6. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 5, characterized in that, In step S2, the method for establishing the collaborative optimization model for de-icing configuration parameters specifically includes the following steps: (A) Refer to the aircraft ground de-icing parameter correlation coupling diagram G Z And parameter-related coupling graph G H The configuration and properties are established to maintain four synergistic optimization objectives: maximizing the safety margin of time, minimizing de-icing time, achieving the most balanced configuration parameters, and minimizing de-icing fluid consumption. Based on the properties of the Laplace matrix, the final fitted values ​​of the de-icing efficiency parameters can be obtained through the following matrix operations: ； In the formula, Ft z The figure shows the fitting estimation results of the de-icing efficiency parameters under the influence of multiple factors. ζ and ξ are the parameter correlation coupling diagrams G and G, respectively. Z And parameter-related coupling graph G H A vector consisting of all parameter vertex elements; For parameter performance correlation, For parameter correlation coupling graph G Z The normalized and normalized Laplace matrix, For parameter performance correlation, For parameter correlation coupling graph G H The normalized and normalized Laplace matrix; (B) To maximize the safety margin of the holding time, and considering the combined effects of multiple external environmental factors and de-icing parameter configuration factors, the fitted value Ft of the holding time under the associated coupling is obtained. H The largest discrepancy exists between this value and the expected holding time determined by considering departing flight density and de-icing resources. ； In the formula, The expected hold time value is determined under the combined constraints of aircraft density and de-icing vehicle configuration for de-icing operations at departure ports. To maintain the maximum expected time difference; (C) For the shortest de-icing time, the de-icing time correlation coupling value Ft is obtained by the combined effect of external environment and operation configuration parameters. z Minimize the error between the expected value and the actual value: ； In the formula: The estimated de-icing time value is determined based on the combined constraints of resource allocation for de-icing operations at the port and the external environment. Ft is the de-icing time-related coupling value. z The minimum error between the expected value and the actual value; (D) To ensure the optimal balance of configuration parameters, the ratio of the difference between the lower limit and the actual value of each configuration parameter to its upper limit is defined as the balance index of the de-icing parameters. The calculation formula is: ； In the formula: and These represent the upper and lower limits of the de-icing configuration parameters, respectively. Based on the balance index of each parameter, the optimization objective function can be derived from... The minimum root mean square error is expressed as: ； In the formula: This represents the average of the balance indices for each parameter. (E) To minimize the amount of de-icing fluid used, construct a coupling graph G showing the relationship between de-icing fluid concentration and de-icing parameters. Z and G H The ratio of the difference in parameter performance correlation is the smallest: （21） For the de-icing parameter correlation coupling diagram G H The independent variables of the vertex parameters within the third path; (F) Based on the actual situation of the de-icing operation and the airport de-icing operation standards and specifications, the primary optimization goal is to maximize the safety margin of the holding time after de-icing Ω1, while the secondary goals are to minimize the de-icing time Ω2 and balance the configuration parameters Ω3. Based on the above description, the economic optimization of the parameter configuration Ω4 is carried out to minimize the amount of de-icing fluid used. (G) By establishing an objective function for the collaborative optimization of de-icing parameters, the solution space of de-icing parameters is limited, and the evolution law of environmental parameters and real-time status are combined with reference to the limits of de-icing configuration parameters; (H) By setting the objective function and constraints, a collaborative optimization model for aircraft ground de-icing configuration parameters is obtained.

7. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 6, characterized in that, In step (G), the constraint condition of the limit is: Equation (22) represents the tolerance constraint for the aircraft ground de-icing configuration parameters, indicating that the configuration of any parameter cannot exceed the set range; Equation (23) represents the parameter balance constraint, indicating that the balance range of parameter optimization needs to be controlled based on the environmental perception results; Equation (24) represents the hold time safety constraint, indicating that the fit result of the hold time after parameter optimization is greater than the expected result, and there is an absolute safety margin within a certain range; Equation (25) represents the de-icing correlation coupling constraint, indicating that the parameter correlation coupling degree oriented towards hold time is greater than the basic setting oriented towards de-icing time, and is represented by the parameter correlation coupling diagram G. Z And parameter-related coupling graph G H The F-norm representation of the Laplacian matrix; （22） （23） （24） （25） （26） In the formula: and These are the upper and lower bounds of the limit threshold for the balance index of de-icing configuration parameters, respectively. To ensure an absolute safety margin in the de-icing time, To limit the efficiency threshold of the de-icing operation process, For the de-icing parameter correlation coupling diagram G Z The independent variable of the vertex parameter in the i-th path.

8. The method for collaborative optimization of aircraft ground de-icing configuration parameters according to claim 7, characterized in that, In step S3, the collaborative optimization model for the de-icing configuration parameters is solved based on the Levenberg-Marquardt algorithm under Lagrange relaxation, specifically including the following steps: (1) Based on the simplified solution complexity of the objective function and constraints for the collaborative optimization of aircraft ground de-icing configuration parameters, and by weighting the four optimization objectives according to their importance and decision-maker preferences, we obtain: ； In the formula: Ω is the overall objective of the collaborative optimization of aircraft ground de-icing configuration parameters, a1, a2, a3, and a4 are the weight coefficients of each optimization objective, and according to the complexity of the constraints, equations (22), (23), and (25) are regarded as simple constraints of the collaborative optimization model, while equations (24) and (26) are complex constraints; from equation (26), it can be seen that the optimization objective can be transformed into a quadratic function Y of unknown de-icing configuration parameter vectors ξ and ζ. LP ,Right now: ； In the formula: The quadratic function is formed by the vector of parameters related to de-icing time, and is calculated by combining the target Ω2 and target Ω3. To preserve the quadratic function formed by the time-dependent parameter vectors, the target Ω1 and target Ω4 are calculated together; (2) Y LP Treating this as the dual problem of the original de-icing configuration parameter co-optimization objective, a Lagrange multiplier factor is introduced to address the complex constraints. and The original de-icing parameter co-optimization problem is transformed into Y LR : ； In the formula: for The quadratic function after adding Lagrange relaxation; for The quadratic function after adding Lagrange relaxation, at the same time and All are greater than 0; Record The Lagrange relaxation dual problem corresponding to the problem of configuring parameters for de-icing is as follows: ； (3) Obtaining ambient temperature T based on environmental information acquisition sensors E Icing type I M and icing thickness I D Parameters, substitute them into the model to get Y LD Simplified to: ； In the formula: For the simplified dual optimization model, To substitute the simplified quadratic function, x is the input vector consisting of the independent variables of all nodes in the de-icing configuration parameter associated coupling graph model; (4) The LM algorithm is used to iteratively solve the objective of the collaborative optimization of the de-icing configuration parameters after the Lagrange relaxation transformation. First, the subgradient step size of the dual optimization model Lagrange multipliers is constructed. : ； In the formula: This is the subgradient iteration step size adjustment factor. For the number of iterations, This represents the limit of de-icing parameters under the current environmental conditions. After introducing the Lagrange multipliers, the first The result of the next iteration; (5) Adjust the subgradient step size Substitute into equation (30) to calculate the gradient expression of the de-icing parameters. With Hessian matrix : ； ； In the formula: x1-x4 are the independent variables of each de-icing configuration parameter, and the initial values ​​of the de-icing configuration parameters are set according to the external environment information. 0 Then its iterative formula is: ； In the formula: I is a 4×4 identity matrix. The step size adjustment factor for the de-icing parameters is changed during the iterative process of collaborative optimization. Improve optimization accuracy: ； In the formula: To adjust The constant set for the search step size of the de-icing parameters, where ; (6) During the iterative process of de-icing parameters, if This indicates that the optimization process does not converge, so the value is reduced. A large-scale search for de-icing parameters is performed, and the Hessian matrix is ​​recalculated. The Lagrange multiplier factor and de-icing parameters are iteratively adjusted until the optimized de-icing parameters meet the allowable error. It outputs the collaboratively optimized de-icing configuration parameters and estimated performance parameters; (7) For L 2 -M solution algorithm convergence analysis is performed; if A feasible solution for the collaborative optimization model of parameters for aircraft ground de-icing configuration is found, and it satisfies... If a constant exists , so that: ； In the formula: for arrive The distance, then the de-icing configuration parameter collaborative optimization model There is a local error bound within N; if the first... Jacobian matrix of the next iteration In solution If there are non-singularities in the data, then the de-icing configuration parameters are... This is an isolated solution.

9. An airport runway airworthiness assessment system under icy and snowy conditions, employing the collaborative optimization method for aircraft ground de-icing configuration parameters as described in any one of claims 1-8, characterized in that, This airport runway airworthiness assessment system under icy and snowy conditions includes: The de-icing efficiency energy conversion module (1) will be used to measure different ambient temperatures T E Icing type I M and icing thickness I D The de-icing efficiency parameter that affects the safety and stability of the operation process under certain conditions is quantified as de-icing time. and holding time t H ; The parameter correlation fitting module (2) systematically analyzes the de-icing time of different de-icing parameters based on the given limits of different parameter definitions. and holding time t H The correlation between parameters was analyzed, and the fit distribution among the parameters was examined. The parameter correlation coupling graph model construction module (3) is based on the correlation relationship and fitting distribution obtained by the parameter correlation fitting module (2). Based on the real-time evolution process perception of the snow and ice environment and the icing model, the de-icing configuration parameters are reasonably determined, and the de-icing configuration parameter correlation coupling graph model is constructed based on the correlation topology results. The parameter collaborative optimization model design module (4) refers to the de-icing configuration parameter association coupling graph model constructed by the parameter association coupling graph model construction module (3) to establish a de-icing configuration parameter collaborative optimization model with the goal of maximizing the safety margin of the maintenance time, minimizing the de-icing time, maximizing the balance of configuration parameters, and minimizing the amount of de-icing fluid. The collaborative optimization model solving module (5) introduces the Lagrange relaxation method to simplify the complex constraints of the de-icing configuration parameters in the collaborative optimization model of the parameter collaborative optimization model design module (4), and uses the Levenberg-Marquardt algorithm to solve the relaxed collaborative optimization model of the de-icing configuration parameters.

10. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, causes the processor to perform the collaborative optimization method for aircraft ground de-icing configuration parameters according to any one of claims 1-8.

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