Complex environment adaptability digital test design method under space-time cooperation of cross-domain aircraft

Abstract

The invention provides a digital test design method for complex environment adaptability under space-time cooperation of a cross-domain aircraft. The digital test design method comprises the following steps: step 1, environmental factor sensitivity pull deviation stepping test design considering season-height difference; step 2, designing a single environment factor limit pull deviation stepping test based on a historical boundary and a maximum probability level under space-time cooperation; 3, complex environment limit pull deviation stepping test design considering combined dimension reduction is carried out; aiming at short-time, wide-area and high-altitude task characteristics of a cross-domain aircraft, environmental factor sensitivity analysis and single / complex environmental factor limit bias digital test scheme design in a task process are developed, and the efficiency and the refinement level of complex environmental adaptability analysis can be remarkably improved; according to the method, environmental factors are divided into a probability type and a non-probability type, combined dimension reduction of complex environmental factors is realized by taking the probability type environmental factors as a breakthrough point, and an idea is provided for solving complex high-dimensional environmental adaptability test and analysis under space-time coordination.

Description

Technical Field

[0001] The present invention provides a complex environment adaptability digital test method for cross - domain aircraft under high - dimensional space - time coordination. It involves a prediction - driven, space - time adaptive, and digital step - by - step scheme design for sensitivity tests and extreme deviation tests of complex environmental factors considering the short - term mobility of cross - domain tasks and the space - time coordination of task environments, belonging to the field of equipment environmental engineering. Background Art

[0002] Cross - domain high - altitude aircraft play an important role in military, civilian and other fields. During the mission process, they usually bear complex environmental loads such as temperature, humidity, wind field (wind direction, wind speed), rain, etc., which have a continuous impact on aspects such as the performance level and functional realization degree of the aircraft. Therefore, conducting an analysis of the adaptability of equipment systems in diverse mission environments has become an important research content, which has important guiding significance for improving their availability, mission success rate, and combat readiness integrity. Environmental adaptability tests aim to study the ability of equipment systems to maintain specified performance / functions in different mission environments, qualitatively / quantitatively analyze the adaptability of the system to environmental factors. Further, based on considering the mission success rate index of the system, reverse - analyze the test result data to construct the boundary of environmental factors, so as to provide effective guidance for aspects such as the start / end decision of the equipment system mission.

[0003] Existing environmental adaptability tests are mainly based on actual equipment tests, and such tests are significantly limited by economic factors and the quantity scale of equipment; in addition, current environmental adaptability tests are mainly based on static tests and environmental cumulative effects. Among them, static tests mean that the space - time coherence constraints during a single mission process of the aircraft are not considered during the test process; environmental cumulative effects mean that environmental adaptability test research mostly considers the storage stage of equipment or the long - term continuous operation period. However, the mission process of cross - domain aircraft usually has strong mobility and short - term characteristics, and the cost of actual equipment tests is very expensive. Therefore, the present invention conducts digital environmental adaptability tests for cross - domain aircraft considering the space - time coherence constraints during the mission process. By classifying "probabilistic" and "non - probabilistic" types of various environmental factors during the mission process, statistical analysis of historical boundaries of environmental factors, extreme deviation boundary expansion, and "dimension reduction" of complex environmental factor combinations, etc., the efficiency of complex environment adaptability tests under space - time coordination is significantly improved, which has important economic and military value, and provides a solution idea for the digital environmental adaptability research of complex equipment under the new situation. Summary of the Invention

[0004] The object of the present invention is to study a digital test design method for the complex environment adaptability of cross-domain aircraft under spatio-temporal coordination, specifically including an environmental factor sensitivity offset step test design considering season-height differences, a single environmental factor extreme offset step test design based on historical boundaries and maximum probability levels under spatio-temporal coordination, and a complex environment extreme offset step test design considering combined dimensionality reduction, so as to assist in the analysis of the influence of complex coupled environmental factors on the performance / function of the aircraft.

[0005] To achieve the above object, the present invention proposes a digital test design method for the complex environment adaptability of cross-domain aircraft under spatio-temporal coordination, including the following steps:

[0006] Step 1: Environmental factor sensitivity offset step test design considering season-height differences; specifically including the following steps:

[0007] The present invention studies the sensitivity levels of different environmental factors in different seasons and different altitude ranges during the mission process of the aircraft. Among them, it is necessary to dynamically and real-time offset the predicted values of environmental factors in the four-dimensional (time, altitude, longitude, latitude) spatio-temporal coordinates during the mission process, and the predicted values are obtained through the environmental prediction model. The "environmental prediction model" refers to the mapping relationship between the environmental factor level values and time, altitude, longitude, and latitude, represented as Le i = f i (t, h, l o , l a ), where t, h, l o , l a represent the values of time, altitude, longitude, and latitude respectively; i represents the i-th type of environmental factor; f i represents the prediction model of the i-th type of environmental factor; Le i represents the predicted level value obtained based on the prediction model of the i-th type of environmental factor. The present invention is based on the environmental factor prediction model that has been determined in advance. The overall idea is: taking the predicted value of the environmental factor at the current spatio-temporal coordinates as the reference value, setting the upper and lower bounds and the offset step (both in percentage) of the offset with respect to the reference value, and combining the characteristics of various environmental factors, respectively carrying out the offset scheme design. When carrying out the sensitivity offset test of a certain type of environmental factor, the level values of the remaining environmental factors remain unchanged at the predicted values.

[0008] a) Classification of environmental factors into "probabilistic" and "non-probabilistic"

[0009] Among the natural environmental factors experienced during the flight mission of the aircraft, factors such as temperature, humidity, and wind speed are environmental loads that must be endured, while environmental factors such as rainfall, snowfall, and hail do not necessarily occur during the flight mission of the aircraft. Therefore, in the present invention, the environmental factors during the mission process are classified into "probabilistic" and "non-probabilistic" according to whether they will definitely occur. During the mission process, the former indicates that the environmental factor may not appear, while the latter indicates that the environmental factor will definitely appear. Therefore, when analyzing a specific aircraft and mission process, it is necessary to classify all specific environmental factors that it may experience.

[0010] b) Determination of the upper and lower deviation bounds of "non-probabilistic" environmental factors

[0011] "Non-probabilistic" environmental factors represent environmental loads that the aircraft will definitely experience during the mission process, mainly including temperature, humidity, wind speed, etc. For each specific environmental factor's variation characteristics with altitude and time, combined with the mission process characteristics of the cross-domain aircraft, the determination of the upper and lower deviation bounds of "non-probabilistic" environmental factors is carried out separately.

[0012] Temperature: The temperature during the mission process of the cross-domain aircraft varies significantly with altitude and time. Therefore, the influence of different altitudes and different seasons is considered, while the influence caused by the change of longitude and latitude is simplified and ignored. Specifically, at the altitude level, a sample altitude h i , i = 1, 2, 3 is selected in the troposphere, stratosphere, and mesosphere respectively; at the season level, the central time point t j , j = 1, 2, 3, 4 of the season is selected; at the longitude and latitude level, the central coordinates (l 1 , l 2 ) of the mission area of the cross-domain aircraft are selected. In the historical data set, calculate the maximum value i ×t j of the temperature factor in the mission area and the minimum value at the same time, and screen the magnitude V of the temperature when h i ×t j ×(l 1 , l 2 ). Then calculate the step-by-step upper deviation bound T,i,j and the lower deviation bound . Further, comprehensively considering the upper and lower deviation bounds of all altitudes and seasons, calculate the unified upper and lower deviation bounds of temperature . Among them, max and min represent the maximum value and minimum value functions respectively.

[0013] Humidity: Similar to the method for determining the upper and lower deviation bounds of temperature, calculate the unified upper and lower deviation bounds of humidity. Specifically, in the historical data set, calculate h i ×t j ​The maximum value of the humidity factor in the lower task area and the minimum value Simultaneously screen h i ×t j ×(l 1 ,l 2 ) The value V of humidity at this time H,i,j , where h i , i = 1, 2, 3 represent the heights of 3 samples, t j , j = 1, 2, 3, 4 represent the central time points of the four seasons, (l 1 ,l 2 ) represents the coordinates of the central point of the longitude and latitude of the task area. Then calculate the upper bound of the humidity step deviation and the lower bound of the deviation Furthermore, comprehensively consider the upper and lower bounds of the humidity deviation and the step size for all heights and seasons, and calculate the unified deviation boundary of humidity Among them, max and min represent the maximum and minimum value functions respectively.

[0014] Wind speed: Similar to the method for determining the upper and lower bounds of temperature deviation, calculate the unified deviation boundary of wind speed. Specifically, in the historical data set, calculate h i ×t j The maximum value of the wind speed factor in the lower task area and the minimum value Simultaneously screen h i ×t j ×(l 1 ,l 2 ) The value V of wind speed at this time W,i,j , where h i , i = 1, 2, 3 represent the heights of 3 samples, t j , j = 1, 2, 3, 4 represent the central time points of the four seasons, (l 1 ,l 2 ) represents the coordinates of the central point of the longitude and latitude of the task area. Then calculate the upper bound of the wind speed step deviation and the lower bound of the deviation Furthermore, comprehensively consider the upper and lower bounds of the wind speed deviation and the step size for all heights and seasons, and calculate the unified deviation boundary of humidity Among them, max and min represent the maximum and minimum value functions respectively.

[0015] Determine the unified deviation boundary of "non-probabilistic" environmental factors: Take the minimum value of the upper and lower bounds of the deviation of all specific "non-probabilistic" environmental factors as the unified deviation boundary of "non-probabilistic" environmental factors; that is, the lower bound of the deviation of "non-probabilistic" environmental factors satisfies The upper bound of the deviation satisfies wherein and respectively represent the lower bounds of deviation of temperature, humidity, and wind speed, and respectively represent the upper bounds of deviation of temperature, humidity, and wind speed.

[0016] c) Determination of the upper and lower bounds of deviation of "probabilistic" environmental factors

[0017] The occurrence of "probabilistic" environmental factors has a certain degree of randomness. Therefore, it is impossible to calculate the boundaries of this type of environmental factor according to the method for determining the upper and lower bounds of deviation of "non-probabilistic" factors. Considering the purpose of the sensitivity deviation test, that is, to analyze the changes in the performance parameters of the aircraft when the levels of each environmental factor change in equal proportion, and then obtain the sensitivity ranking of environmental factors. Therefore, the present invention sets the upper and lower bounds of deviation of "probabilistic" environmental factors as the unified upper and lower bounds of deviation of "non-probabilistic" environmental factors. That is wherein and respectively represent the lower bound and upper bound of deviation of the "probabilistic" environmental factor.

[0018] d) Determination of the unified upper and lower bounds of deviation and step size of various environmental factors

[0019] Based on steps b) and c), the unified lower bound of deviation of all environmental factors and the upper bound of deviation can be obtained. In the link of setting the deviation step size, the determination of the deviation step size Le needs to ensure that the number of sensitivity deviation tests (D U +D L ) / Le of each type of environmental factor at the same altitude and season is no more than 20 times. It should be noted that all the deviation boundaries / step sizes in the present invention are not the absolute level values of environmental factors, but percentages with respect to the predicted values (obtained through the environmental prediction model f i ), which effectively solves the analysis problem caused by the inconsistent dimensions of different environmental factors.

[0020] Step 2: Design of the limit deviation step-by-step test of a single environmental factor based on historical boundaries and the maximum probability level under spatio-temporal coordination; specifically includes the following steps:

[0021] The single environmental factor extreme offset test aims to obtain a corresponding dataset of environmental factor levels - aircraft performance indicators by setting the upper and lower bounds / step sizes of the single extreme offset of each environmental factor differently and inputting them into a pre-determined aircraft digital simulation platform, providing a data basis for subsequent analysis and evaluation. During the mission process of a cross-domain aircraft, for the same environmental factor, the environmental factor level values in different altitude intervals have forward and backward correlations, which are determined by the flight time and spatial coherence of the aircraft, that is, space-time coordination. When designing the extreme offset test of a single environmental factor, the present invention takes into account this significant space-time coordination characteristic. Specifically, when offsetting the environmental factor level in a certain altitude interval, the environmental factor level values in the remaining altitude intervals are set to be based on the output value of the environmental prediction model f i of

[0022] a) Extreme value statistical calculation of historical environmental data at different altitudes - seasons

[0023] First, divide the altitude range of the aircraft mission process into intervals, and further statistically calculate the extreme values of environmental factors in all altitude intervals in the historical environmental data for the four seasons of spring, summer, autumn, and winter, including the maximum value and the minimum value where i represents the environmental factor category and s represents the altitude interval range. and are not the absolute values of the environmental factor levels, but percentages with respect to the predicted values (obtained through the environmental prediction model f i ).

[0024] b) Setting the number of groups for the step test of a single environmental factor

[0025] According to the sensitivity rankings of "probability-based" (mainly rainfall) and "non-probability-based" (temperature, humidity, wind speed) environmental factors during the aircraft mission process, the level values within the boundaries of this environmental factor are divided into G i,s groups. Here, sensitivity represents the influencing ability of each environmental factor on the aircraft performance indicators, which is described by the degree of change in the aircraft performance indicators when each environmental factor changes proportionally. The greater the change in the performance indicators, the more sensitive the corresponding environmental factor (the sensitivity offset test in "Step 1" supports the acquisition of this ranking). The sensitivity ranking is the order of sensitivity of all environmental factors (mainly including temperature, humidity, wind speed, and rainfall). According to the ranking from high to low sensitivity, the number of groups G i,s for the level values of the four environmental factors are set to 6, 5, 4, and 3 respectively. Where i represents the environmental factor category and s represents the altitude interval range. In addition, the environmental factor with higher sensitivity is set with more groups.

[0026] c) Determination of the most probable level of environmental data in different altitude intervals

[0027] Analyze and calculate the maximum probability levels of "probabilistic" (mainly rainfall) and "non-probabilistic" (temperature, humidity, wind speed) environmental factors in different season-altitude intervals In the present invention, the maximum probability level value of each type of environmental factor is approximately set as the predicted level of the environmental factor at the real-time mission point of the aircraft (this predicted value is obtained through the environmental prediction model f i obtained), that is where i represents the category of environmental factors and s represents the altitude interval range It is not the absolute value of the environmental factor level, but the percentage of the predicted value of the environmental factor at the real-time mission point of the aircraft in the coastal area (obtained through the environmental prediction model f i obtained)

[0028] d) Expand the limit deviation boundary based on the maximum probability level

[0029] Integrate the extreme levels of various environmental factors in the historical data Maximum probability level Set the proportionality coefficient δ 1 , and expand the limit deviation boundary of a single environmental factor from the historical boundary to the limit boundary, that is, the upper bound and the lower bound

[0030]

[0031] e) Determine the limit deviation boundary and step size of a single environmental factor

[0032] Combined with the grouping number setting in b), divide the expanded limit deviation boundary of a single environmental factor into G i,s groups, and obtain the corresponding deviation step length le i,s :

[0033]

[0034] In the formula, le i,s is the step length of each environmental factor during the limit deviation step test, i is the category of environmental factors, s is the altitude interval, is the floor symbol. In addition, considering the spatio-temporal coordination characteristics of environmental factors during the aircraft mission. When conducting the limit deviation test of a single environmental factor, the other environmental factors and the horizontal values of other altitude intervals of the same environmental factor remain at the reference value (that is, the output value obtained through the environmental prediction model f i obtained)

[0035] Step 3: Design the limit deviation step test for complex environments considering combined dimensionality reduction; specifically, it includes the following steps

[0036] The extreme pull - deviation step - by - step test in a complex environment aims to determine the respective pull - deviation boundaries / step lengths when multiple environmental factors are coupled during the mission process of a cross - domain aircraft. When there are many categories of environmental factors, it is easy to have a situation of too high combined dimensions. The present invention takes the "probabilistic" environmental factors as the breakthrough point, focuses on the possible co - occurrence situations in the "probabilistic" environmental factors, and discretizes their level values. For the "non - probabilistic" environmental factors, the pull - deviation boundaries / step lengths obtained in "Step 2" are adopted.

[0037] a) Determination of the pull - deviation boundary and step length when the "probabilistic" environmental factor does not occur

[0038] When the "probabilistic" environmental factors, rainfall, snowfall, hail, and sand dust, do not occur, only the coupling effect between the "non - probabilistic" environmental factors (temperature, humidity, and wind speed) needs to be considered. At this time, the extreme lower / upper pull - deviation boundaries of temperature, humidity, and wind speed are respectively: and The extreme pull - deviation step lengths of temperature, humidity, and wind speed are respectively le T,s , le H,s and le W,s ; The number of pull - deviation groups G i,s of temperature, humidity, and wind speed are set to 6, 5, and 4 in the order of decreasing sensitivity. These indexes have been constructed through e) in "Step 2".

[0039] b) Determination of the pull - deviation boundary and step length when the "probabilistic" environmental factor occurs

[0040] When the "probabilistic" environmental factors, such as rainfall, snowfall, hail, and sand dust, occur (in actual research, rainfall is mainly considered for the "probabilistic" environmental factors, and the rest can be ignored as appropriate), it is necessary to consider the coupling effect between it and the "non - probabilistic" environmental factors (temperature, humidity, and wind speed) at the same time. Among them, the extreme pull - deviation boundaries and step lengths of the "non - probabilistic" environmental factors have been described in a) of "Step 3"; in the "probabilistic" environmental factors, only rainfall - hail and rainfall - snowfall may occur simultaneously, that is, only the extreme pull - deviation boundaries of rainfall and snowfall and as well as the step lengths le R,s and le S,s ; The number of pull - deviation groups G i,s of rainfall and snowfall are both set to 3. Therefore, the possible combination analysis of the "probabilistic" environmental factors reduces the dimension of the complex environmental factor combination and improves the test efficiency.

[0041] The advantages and beneficial effects of the present invention are as follows:

[0042] ①In view of the short-time, wide-domain, and high-altitude mission characteristics of cross-domain aircraft, the present invention conducts sensitivity analysis of environmental factors during the mission process and designs a digital test plan for extreme deviation of single / complex environmental factors, which can significantly improve the efficiency and refinement level of complex environmental adaptability analysis;

[0043] ②The present invention constructs a digital test plan for extreme deviation of a single environmental factor through an extreme boundary expansion method based on historical statistical boundaries, maximum probability levels, and sensitivity levels, which can significantly improve the test efficiency;

[0044] ③The present invention classifies environmental factors into "probabilistic" and "non-probabilistic", and realizes the "dimension reduction" of the combination of complex environmental factors with the "probabilistic" environmental factors as the breakthrough point, providing ideas for solving complex high-dimensional environmental adaptability tests and analyses under spatio-temporal coordination;

[0045] ④The method of the present invention is scientific, has good processability, and has broad application and promotion value. Description of the Drawings

[0046] Figure 1 Schematic diagram of the method flow of the present invention.

[0047] Figure 2 Flow chart of the digital test for the adaptability of a single environmental factor in the present invention.

[0048] Figure 3 Flow chart of the digital test for the adaptability of complex environmental factors in the present invention. Detailed Embodiment

[0049] To make the above objects and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0050] The present invention provides a digital test design method for complex environmental adaptability under spatio-temporal coordination of a cross-domain aircraft. Taking a certain type of long-range missile as a case object, the implementation flow chart is as Figure 1 shown, and the specific implementation method is as follows;

[0051] Step 1: Design of a sensitivity deviation step test for environmental factors considering season-height differences; specifically including the following steps:

[0052] This implementation case considers the initial conditions of missile launch in coastal areas, and mainly studies 4 types of natural environmental factors and their impacts during the missile mission process, namely temperature, humidity, wind speed, and rainfall.

[0053] a) Classification of "probabilistic" and "non-probabilistic" of environmental factors

[0054] For the natural environment categories considered in the missile mission process of this implementation case, temperature, humidity, and wind speed are classified as "non-probabilistic" environmental factors, and rainfall is classified as a "probabilistic" environmental factor. Based on this, a sensitivity biasing step-by-step test design for environmental factors is carried out.

[0055] b) Determination of the biasing upper and lower bounds of "non-probabilistic" environmental factors

[0056] Considering the variation characteristics of "non-probabilistic" environmental factors at different altitudes and seasons, using the historical environmental factor data of this coastal area from 2018 to 2021, the maximum value of the environmental factor at h i ×t j is statistically analyzed and the minimum value At the same time, the environmental factor value V i ×t j ×(l 1 , l 2 ) is screened. Among them, h i,j , i = 1, 2, 3 represent the sample heights of the troposphere, stratosphere, and mesosphere, which are 8 km, 20 km, and 40 km respectively; t i , j = 1, 2, 3, 4 represent the seasonal central time points of spring, summer, autumn, and winter; (l j , l 1 , l 2 ) represents the coordinates of the central point of the mission area's longitude and latitude in this case. At different altitudes and seasons, the biasing upper bound and the biasing lower bound of each type of environmental factor are calculated respectively. In the formula, represents the biasing upper bound at h i ×t j ; represents the biasing lower bound at h i ×t j . Further calculate the maximum value D in all U and the minimum value D in all L , which are used as the biasing upper and lower bounds of this type of environmental factor at t j respectively. The calculation results of the biasing upper and lower bounds of temperature, humidity, and wind speed can be obtained, as shown in Tables 1 - 3. In the tables, h i ×t j is abbreviated as h i t j .

[0057] Table 1 Biasing upper and lower bounds of temperature at different altitudes - seasons in the coastal area (unit: percentage)

[0058]

[0059] Table 2 Pull - deviation upper and lower bounds of humidity at different heights - seasons in coastal areas (unit: percentage)

[0060]

[0061] Table 3 Pull - deviation upper and lower bounds of wind speed at different heights - seasons in coastal areas (unit: percentage)

[0062]

[0063] Based on the pull - deviation upper and lower bounds of temperature, humidity, and wind speed, the unified pull - deviation upper and lower bounds of "non - probabilistic" environmental factors are determined. After calculation, the unified pull - deviation upper and lower bounds are 36% and 34% respectively.

[0064] c) Determination of pull - deviation upper and lower bounds of "probabilistic" environmental factors

[0065] In this case, the pull - deviation upper / lower bounds of the only "probabilistic" environmental factor, that is, rainfall, are set as the unified pull - deviation upper bound of 36% and lower bound of 34% of "non - probabilistic" environmental factors.

[0066] d) Determination of unified pull - deviation upper and lower bounds and step sizes of various environmental factors

[0067] Based on the above steps, the unified sensitivity pull - deviation upper bound of 36% and lower bound of 34% of 4 types of environmental factors are determined. Considering the purpose of sensitivity pull - deviation, that is, analyzing the sensitivity of each factor when the environmental factor levels change proportionally, and combining with the digital test efficiency, the pull - deviation step size is set to 2%. Accordingly, the boundaries and step sizes of the digital pull - deviation test of environmental factor sensitivity are obtained. The pull - deviation upper bound of 36% and lower bound of 34% in this case are not the absolute level values of environmental factors, but percentages with respect to the predicted values (obtained through the environmental prediction model f i ), which effectively solves the analysis problem caused by the inconsistent dimensions of different environmental factors.

[0068] Step 2: Design of the limit pull - deviation step - by - step test for a single environmental factor based on historical boundaries and maximum probability levels under spatio - temporal coordination; specifically, it includes the following steps:

[0069] a) Statistical calculation of extreme values of historical environmental data at different heights - seasons

[0070] In this case, different height intervals are set with a step of 1 km. The maximum values and minimum values of different environmental factors i in coastal areas from 2018 to 2021 at different height intervals s and different seasons are statistically calculated. As an example, this step shows the temperature extreme values in the 0 - 3 km height interval, as shown in Table 4. The extreme values of environmental factors in the remaining height intervals are obtained by the exact same method.

[0071] Example of extreme value statistics of temperature at different heights - seasons (unit: percentage)

[0072]

[0073] b) Setting the number of groups for step tests of a single environmental factor

[0074] Based on the analysis results of the sensitivity biasing test, the sensitivity ranking is obtained as: temperature, wind speed, humidity, rainfall. Set the number of level groups G of the 4 environmental factors i,s , which are 6, 5, 4, and 3 respectively.

[0075] c) Determining the maximum probability level of historical environmental data at different heights - seasons

[0076] Taking temperature as an example, in the same season and for the same height range, approximate the maximum probability level of temperature in the coastal area as the temperature prediction level of the real - time task point, that is, the maximum probability level of temperature is 100%. The maximum probability levels of the remaining environmental factors are all approximately set to 100%.

[0077] d) Expanding the limit biasing boundary based on the maximum probability level

[0078] Set the proportionality coefficient δ 1 = 0.15, and calculate the expanded limit boundary of temperature based on the historical boundary according to the formula This step shows the temperature limit boundary values in the height range of 0 - 3 km as an example, and the results are shown in Table 5. The limit boundaries of environmental factors in other height ranges are obtained by the exact same method. In the formula, and represent the expanded upper limit and lower limit of the limit respectively; and represent the historical upper limit and historical lower limit of the environmental factor level respectively; represents the maximum probability level, which is 100%; i and s represent the environmental factor category and different height ranges respectively.

[0079] Table 5 Limit boundaries of temperature at different heights - seasons (unit: percentage)

[0080]

[0081] e) Determining the limit biasing boundary and step size of a single environmental factor

[0082] Based on the limit boundaries of temperature at different heights - seasons in Table 5, combined with the number of level groups of temperature in step b) being 6, and using the formula The corresponding offset step length can be determined. As an example, this step shows the temperature limit edge offset step length in the altitude range of 0 - 3 km, and the results are shown in Table 6. The environmental factor limit offset step lengths for the remaining altitude ranges are obtained according to exactly the same method.

[0083] Table 6 Limit offset step lengths of temperature at different altitudes - seasons (unit: percentage)

[0084]

[0085] Combining Table 5 and Table 6, the limit offset step length and boundary of temperature can be obtained. According to exactly the same method, the limit offset boundaries and step lengths of the other three types of environmental factors during the missile mission can be determined.

[0086] Step 3: Design of the limit offset step - by - step test for complex environments considering combined dimensionality reduction; specifically, it includes the following steps:

[0087] a) Determination of the offset boundary and step length when the "probabilistic" environmental factor does not occur

[0088] In this case, the "probabilistic" environmental factor is only rainfall. When rainfall does not occur, the offset boundary and step length of temperature are the boundary and step length obtained in "Step 2", and the methods for solving the step lengths and boundaries of humidity and wind speed are exactly the same as those of temperature.

[0089] b) Determination of the offset boundary and step length when the "probabilistic" environmental factor occurs

[0090] In this implementation case, there is only the "probabilistic" environmental factor of rainfall. Set its number of horizontal groups to 3, namely "rainfall does not occur", "light rain", and "heavy rain". The number of horizontal groups of temperature, humidity, and wind speed are set to 6, 5, and 4 respectively. Using the limit offset step lengths and boundaries of the "non - probabilistic" environmental factors obtained in "Step 3" a), in the discrete cases of "rainfall does not occur", "light rain", and "heavy rain", combined with the limit offset boundaries and step lengths of temperature, humidity, and wind speed, the final combined offset test method can be obtained.

[0091] In summary, the present invention relates to a digital test design method for complex environment adaptability under the spatio-temporal coordination of cross-domain aircraft, mainly including environmental factor sensitivity offset test, single environmental factor extreme offset test, and complex environmental factor extreme offset test. It involves a digital step test design method for complex environment adaptability under spatio-temporal coordination based on the boundaries of "probabilistic" and "non-probabilistic" historical environmental factors, the maximum probability level, the expansion of extreme offset boundaries, and the "dimensionality reduction" of complex environmental factor combinations. The specific steps of this method are as follows: First, the design of environmental factor sensitivity offset step test considering season-height differences; Second, the design of single environmental factor extreme offset step test based on historical boundaries and maximum probability level under spatio-temporal coordination; Third, the design of complex environmental extreme offset step test considering combined dimensionality reduction. The present invention is applicable to multi-type environmental adaptability tests and analyses of aircraft with cross-domain, short-time, and high-dimensional spatio-temporal mission characteristics, and significantly improves the efficiency of environmental adaptability tests through digital means and the combined dimensionality reduction of environmental factors, providing effective information input for its mission decision-making.

Claims

1. A digital test design method for complex environment adaptability under cross-domain aircraft space-time collaboration, characterized in that: The following steps are involved: Step 1: Design a seasonal-altitude difference in environmental factor sensitivity step-by-step experiment; The sensitivity levels of different environmental factors in different seasons and altitude ranges during the mission of the aircraft require the prediction values ​​of environmental factors in four-dimensional space-time coordinates to be dynamically and real-time pulled during the mission. The prediction values ​​are obtained through the environmental prediction model; the environmental prediction model refers to the mapping relationship between the environmental factor level value and time, altitude, longitude and latitude, which is represented by Le i =f i (t,h,l o ,l a ), where t, h, l o ,l a Respectively represent time, altitude, longitude and latitude values; i represents the i-th type of environmental factor; f i Le represents the prediction model of the i-th environmental factor; i It represents the predicted level value obtained based on the prediction model of the i-th environmental factor; the predicted value of the environmental factor under the current space-time coordinates is used as the benchmark value, and the upper and lower bounds and the step length of the deviation about the benchmark value are set, both in percentage; Step 2: Design a single environmental factor extreme deviation step test based on historical boundaries and maximum probability levels under spatiotemporal coordination; The single environmental factor extreme deflection test aims to obtain the corresponding data set of environmental factor level-aircraft performance index by setting the upper and lower limits / step lengths of the single extreme deflection for each environmental factor and inputting them into a predetermined aircraft digital simulation platform, thus providing a data basis for subsequent analysis and evaluation. Step 3: Design a complex environment extreme pull-off step test with combined dimensionality reduction; The complex environment extreme deflection step test is designed to determine the deflection boundaries / step sizes of each type of environmental factors when coupled during a cross-domain aircraft mission.

2. According to claim 1, a digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination is characterized by: In step 1, categorize environmental factors into probabilistic and non-probabilistic types: Among the natural environmental factors experienced by the aircraft during the mission, temperature, humidity, and wind speed factors are environmental loads that will definitely be borne, while rain, snowfall, and hail factors do not necessarily appear during the aircraft's mission; therefore, the environmental factors in the mission process are divided into probabilistic and non-probabilistic types based on whether they will definitely occur; during the mission, the former indicates that the environmental factor may not necessarily occur, while the latter indicates that the environmental factor will definitely occur.

3. A digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination according to claim 1 or 2, characterized in that: In step 1, determine the upper and lower bounds of the non-probabilistic environmental factors: Non-probabilistic environmental factors characterize the environmental loads that the aircraft will definitely experience during the mission, including temperature, humidity and wind speed. Based on the changing characteristics of each type of specific environmental factor with altitude and time, combined with the characteristics of the mission process of the cross-domain aircraft, the upper and lower bounds of the non-probabilistic environmental factors are determined respectively. Determine the unified deviation boundary of non-probabilistic environmental factors: take the minimum value of the deviation upper and lower bounds of all specific non-probabilistic environmental factors as the unified deviation boundary of non-probabilistic environmental factors; that is, the deviation lower bound of non-probabilistic environmental factors satisfies Pull the upper bound to meet In the formula and They represent the lower limits of temperature, humidity and wind speed respectively. and They represent the upper limits of temperature, humidity and wind speed respectively.

4. According to claim 3, a digital test design method for complex environment adaptability under cross-domain aircraft spatiotemporal coordination is characterized by: In step 1, determine the upper and lower bounds of the probabilistic environmental factors: Set the upper and lower bounds of the probability-based environmental factors to the same upper and lower bounds of the non-probability-based environmental factors; that is, In the formula and They respectively represent the lower and upper limits of the deviation of probabilistic environmental factors.

5. According to claim 4, a digital test design method for complex environment adaptability under cross-domain aircraft spatiotemporal coordination is characterized by: In step 1, determine the uniform upper and lower bounds and step sizes for various environmental factors: Get the uniform lower bound of all environmental factors and pull the upper bound In the setting of the pulling step length, the determination of the pulling step length Le needs to ensure that the number of sensitivity pulling tests (D U +D L ) / Le is no more than 20 times; all deviation boundaries / step sizes are not absolute level values ​​of environmental factors, but percentages of predicted values.

6. According to claim 1, a digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination is characterized by: In step 2, the extreme values ​​of historical environmental data at different altitudes and seasons are calculated: The altitude range of the aircraft mission process is divided into intervals, and the extreme values ​​of environmental factors in all altitude intervals in the historical environmental data are further calculated for the four seasons of spring, summer, autumn and winter, including the maximum value. and minimum Among them, i represents the environmental factor category, and s represents the height range; and It is not the absolute value of the level of the environmental factor, but the percentage with respect to the predicted value.

7. A digital test design method for complex environment adaptability under cross-domain aircraft spatiotemporal coordination according to claim 1 or 6, characterized in that: In step 2, set the number of step test groups for a single environmental factor: According to the sensitivity ranking of probabilistic and non-probabilistic environmental factors in the aircraft mission process, the level value within the boundary of the environmental factor is divided into G i,s group; the sensitivity here refers to the ability of each type of environmental factor to affect the performance index of the aircraft, which is described by the degree of change of the aircraft performance index when each type of environmental factor changes in equal proportion. The greater the change of the performance index, the more sensitive the corresponding environmental factor; the sensitivity ranking is the order of sensitivity of all environmental factors; according to the ranking of sensitivity from high to low, the level values ​​of the four types of environmental factors are grouped into groups G i,s They are set to 6, 5, 4, and 3 respectively; i represents the category of environmental factors, and s represents the height range; in addition, more groups are set for environmental factors with higher sensitivity.

8. The method for designing digital experiments on adaptability to complex environments under cross-domain aircraft spatiotemporal coordination according to claim 7 is characterized by: In step 2, the maximum probability level of environmental data in different altitude intervals is determined: Analyze and calculate the maximum probability levels of probabilistic and non-probabilistic environmental factors in different seasons and altitude ranges The maximum probability level value of each type of environmental factor is set as the environmental factor prediction level of the aircraft real-time mission point, that is, Among them, i represents the environmental factor category, and s represents the height range; It is not the absolute value of the environmental factor level, but the percentage of the predicted value of the environmental factor at the real-time mission point of the aircraft in the coastal area.

9. The digital test design method for complex environment adaptability under cross-domain aircraft spatiotemporal coordination according to claim 1 is characterized by: In step 2, the limit is extended based on the maximum probability level: Comprehensive historical data on the extreme levels of various environmental factors Maximum probability level Set the proportional coefficient δ1 to expand the limit deviation boundary of a single environmental factor from the historical boundary to the limit boundary, that is, the upper boundary and the lower bound 10. The digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination according to claim 1 is characterized by: In step 2, determine the limit deviation boundary and step size of a single environmental factor: Pull the expanded single environmental factor limit to the boundary Divided into G i,s group, and get the corresponding deviation step length le i,s : In the formula, le i,s is the step length of each environmental factor in the extreme pull step test, i is the environmental factor category, s is the height range, The floor symbol.

11. The digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination according to claim 1 is characterized by: In step 3, the deviation boundary and step length are determined when the probabilistic environmental factors do not occur: When the probabilistic environmental factors do not occur, only the coupling between the non-probabilistic environmental factors needs to be considered; at this time, the lower / upper limits of temperature, humidity and wind speed are: and The limit deviation step lengths of temperature, humidity and wind speed are le T,s ,le H,s and W,s ; Number of pull groups of temperature, humidity and wind speed G i,s The settings are 6, 5, and 4 in descending order of sensitivity.

12. A digital test design method for complex environment adaptability under cross-domain aircraft space-time coordination according to claim 1 or 11, characterized in that: In step 3, the deviation boundary and step length when probabilistic environmental factors occur are determined: When probabilistic environmental factors occur, the coupling effect between them and non-probabilistic environmental factors needs to be considered at the same time; among the probabilistic environmental factors, only rain-hail and rain-snowfall may occur at the same time, that is, only the extreme deviation boundary of rain and snowfall is considered. and and step length le R,s and S,s ; Number of groups of rain and snowfall G i,s Both are set to 3.

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