Jet nozzle configuration method, device, electronic device and storage medium

Abstract

The present disclosure relates to a jet nozzle configuration method, device, electronic device, and storage medium. The method determines target jet core area information and an initial jet nozzle configuration, and determines target core area features that match the target jet core area information based on the initial jet nozzle configuration and a trained Gaussian process regression model. The structure of the jet nozzle is further configured based on the jet nozzle configuration corresponding to the target core area features. The embodiment of the present disclosure automatically determines the corresponding jet nozzle configuration based on the Gaussian process regression model and the target jet core area information, and configures the jet nozzle using the jet nozzle configuration as constraint information, thereby quickly and accurately obtaining a jet nozzle that can produce the required jet core area.

Description

Technical Field

[0001] The present disclosure relates to the field of fluid mechanics, and in particular to a jet nozzle configuration method, device, electronic device, and storage medium. Background Art

[0002] In jet testing, a jet nozzle is required to provide a Gaussian, uniform, and stable airflow environment with a suitable effective volume and spatial location at simulated flow rates. Conventional jet nozzles can have circular or rectangular outlets. However, to provide specific core flow fields for different test subjects, the jet nozzle often requires the design and optimization of specific constraints to achieve adaptive improvement of the core flow field. Summary of the Invention

[0003] In view of this, the present disclosure proposes a jet nozzle configuration method, device, electronic device and storage medium, aiming to improve the efficiency and accuracy of the process of configuring a jet nozzle structure that provides a specific core area.

[0004] According to a first aspect of the present disclosure, a method for configuring a jet nozzle is provided, the method comprising:

[0005] Determine the target jet core area information and the initial jet nozzle configuration;

[0006] Determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model;

[0007] The structure of the jet nozzle is configured according to the jet nozzle configuration corresponding to the target core area characteristics.

[0008] In one possible implementation, determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model includes:

[0009] Using the initial jet nozzle configuration as input to the Gaussian process regression model, determining corresponding core region features;

[0010] In response to the core region characteristics not matching the target jet core region information, updating the jet nozzle configuration and re-determining the corresponding core region characteristics;

[0011] In response to the core region feature matching the target jet core region information, the core region feature is determined to be a target core region feature.

[0012] In one possible implementation, the jet nozzle configuration includes a translation dimension and a side plate type.

[0013] In a possible implementation, updating the jet nozzle configuration and re-determining the corresponding core area characteristics includes:

[0014] The translation dimension of the top panel is adjusted within the translation dimension range of the top panel, and / or one of a plurality of preset side panel types is selected and updated as the side panel type in the jet nozzle configuration.

[0015] In one possible implementation, determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model includes:

[0016] According to a preset translation size range and a side plate type, the jet nozzle configuration is adjusted multiple times to obtain a jet nozzle configuration set;

[0017] Predicting core area characteristics of each jet nozzle configuration in the jet nozzle configuration set according to the Gaussian process regression model to obtain an initial population;

[0018] The population is evolved by using a genetic algorithm and the initial population to obtain core area features corresponding to the optimal solution as target core area features.

[0019] In a possible implementation, the core region characteristic includes at least one of pressure, temperature, Mach number, volume, and position.

[0020] In one possible implementation, the training process of the Gaussian process regression model includes:

[0021] Determine an observational dataset including multiple sample nozzle configurations and corresponding annotated core region features;

[0022] Determining corresponding predicted core area features through the sample nozzle configuration and a preset mean and covariance function;

[0023] determining a model difference based on the predicted core region characteristics and the annotated core region characteristics corresponding to each of the sample nozzle configurations;

[0024] In response to the difference not satisfying a preset convergence condition, adjusting hyperparameters of the mean and covariance function;

[0025] In response to the difference satisfying a preset convergence condition, a Gaussian process regression model is determined according to the mean and covariance function.

[0026] In one possible implementation, the jet end of the jet nozzle includes two side plates fixedly connected, and a top plate and a bottom plate slidably connected. The jet nozzle configuration includes a translational dimension for adjusting the sliding dimension of the top plate and the bottom plate connected to the jet nozzle, and a side plate type for limiting the shape of the two side plates.

[0027] According to a second aspect of the present disclosure, a jet nozzle configuration device is provided, the device comprising:

[0028] An information determination module is used to determine target jet core area information and initial jet nozzle configuration;

[0029] a feature determination module, configured to determine target core area features matching the target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model;

[0030] The structure configuration module is used to configure the structure of the jet nozzle according to the jet nozzle configuration corresponding to the target core area characteristics.

[0031] In a possible implementation, the feature determination module includes:

[0032] a feature acquisition submodule, configured to use the initial jet nozzle configuration as input to the Gaussian process regression model to determine corresponding core area features;

[0033] a feature updating submodule, configured to update the jet nozzle configuration and redetermine the corresponding core area feature in response to the core area feature not matching the target jet core area information;

[0034] The feature determination submodule is configured to determine that the core area feature is a target core area feature in response to a match between the core area feature and the target jet core area information.

[0035] In one possible implementation, the jet nozzle configuration includes a translation dimension and a side plate type.

[0036] In a possible implementation, the feature update submodule includes:

[0037] The structure adjustment unit is used to adjust the translation size of the top plate within the translation size range, and / or select one of a plurality of preset side plate types and update it as the side plate type in the jet nozzle configuration.

[0038] In a possible implementation, the feature determination module includes:

[0039] An information initialization submodule, configured to adjust the jet nozzle configuration multiple times according to a preset translation size range and side panel type to obtain a jet nozzle configuration set;

[0040] A population initialization submodule, configured to predict the core area characteristics of each jet nozzle configuration in the jet nozzle configuration set according to the Gaussian process regression model to obtain an initial population;

[0041] The population evolution submodule is used to perform population evolution using a genetic algorithm and the initial population, and obtain the core area characteristics corresponding to the optimal solution as the target core area characteristics.

[0042] In a possible implementation, the core region characteristic includes at least one of pressure, temperature, Mach number, volume, and position.

[0043] In one possible implementation, the training process of the Gaussian process regression model includes:

[0044] Determine an observational dataset including multiple sample nozzle configurations and corresponding annotated core region features;

[0045] Determining corresponding predicted core area features through the sample nozzle configuration and a preset mean and covariance function;

[0046] determining a model difference based on the predicted core region characteristics and the annotated core region characteristics corresponding to each of the sample nozzle configurations;

[0047] In response to the difference not satisfying a preset convergence condition, adjusting hyperparameters of the mean and covariance function;

[0048] In response to the difference satisfying a preset convergence condition, a Gaussian process regression model is determined according to the mean and covariance function.

[0049] In one possible implementation, the jet end of the jet nozzle includes two side plates fixedly connected, and a top plate and a bottom plate slidably connected. The jet nozzle configuration includes a translational dimension for adjusting the sliding dimension of the top plate and the bottom plate connected to the jet nozzle, and a side plate type for limiting the shape of the two side plates.

[0050] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0051] According to a fourth aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which computer program instructions are stored, wherein the computer program instructions implement the above method when executed by a processor.

[0052] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0053] In an embodiment of the present disclosure, the method determines target jet core area information and an initial jet nozzle configuration, and based on the initial jet nozzle configuration and a trained Gaussian process regression model, determines target core area features that match the target jet core area information. The jet nozzle structure is further configured based on the jet nozzle configuration corresponding to the target core area features. The present embodiment automatically determines the corresponding jet nozzle configuration based on the Gaussian process regression model and the target jet core area information, and uses the jet nozzle configuration as constraint information to configure the jet nozzle, quickly and accurately obtaining a jet nozzle that can produce the desired jet core area.

[0054] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0056] Figure 1 A flow chart showing a method for configuring a jet nozzle according to an embodiment of the present disclosure is shown;

[0057] Figure 2 A schematic diagram showing a jet nozzle according to an embodiment of the present disclosure;

[0058] Figure 3 A schematic diagram illustrating a training Gaussian process regression model according to an embodiment of the present disclosure is shown;

[0059] Figure 4 A schematic diagram illustrating a method for determining characteristics of a target core area according to an embodiment of the present disclosure is shown;

[0060] Figure 5 A schematic diagram showing a jet nozzle configuration device according to an embodiment of the present disclosure;

[0061] Figure 6 A schematic diagram illustrating an electronic device according to an embodiment of the present disclosure is shown;

[0062] Figure 7 A schematic diagram illustrating another electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0063] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0064] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0065] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0066] In one possible implementation, the jet nozzle configuration method of the embodiment of the present disclosure can be executed by an electronic device such as a processor, a terminal device, or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. A fixed or mobile terminal. The server can be a single server or a server cluster composed of multiple servers. The electronic device can implement the jet nozzle configuration method of the embodiment of the present disclosure by calling computer-readable instructions stored in a memory by a processor.

[0067] Figure 1 FIG. 1 is a flow chart showing a method for configuring a jet nozzle according to an embodiment of the present disclosure. Figure 1 As shown, the jet nozzle configuration process of the embodiment of the present disclosure may include the following steps S10-S30.

[0068] Step S10: Determine target jet core area information and initial jet nozzle configuration.

[0069] In one possible implementation, electronic equipment determines target jet core region information corresponding to a desired jet core region, as well as a default initial jet nozzle configuration for the jet nozzle. The target jet core region information may include specific core region characteristics, such as at least one of the following: pressure, temperature, Mach number, volume, and position of the core region of the airflow generated by the jet nozzle. Alternatively, the target jet core region information may include constraint information used to define specific core region characteristics, such as at least one of the following: a required pressure range, temperature range, Mach number range, volume range, and position range.

[0070] Optionally, the jet nozzle configuration is used to constrain the structural style of the jet nozzle. For example, when the jet end of the jet nozzle includes two fixedly connected side plates, and a top plate and a bottom plate that are slidably connected, the jet nozzle configuration is used to add constraints to the side plates, the top plate, and the bottom plate. That is, the jet nozzle configuration may include a translational dimension for adjusting the sliding dimension of the top plate and the bottom plate connected to the jet nozzle, and a side plate type for limiting the shape of the two side plates. Optionally, the fixed connection method between the side plate and the jet nozzle may include any connection method such as screw connection, snap connection, and gluing or welding. The sliding connection method between the top plate, the bottom plate, and the jet nozzle may include a sliding connection through a slide. The translational dimension of the top plate and the bottom plate in the initial jet nozzle configuration may be 0.

[0071] Figure 2 Schematic diagram of a jet nozzle according to an embodiment of the present disclosure is shown. Figure 2 As shown, the side panels of the jet nozzle are two plates of the same or different shapes, which can include regular shapes such as square, circular, and triangular, or irregular shapes such as arbitrary polygons. The top and bottom panels of the jet nozzle are slidably connected to the jet nozzle, and the characteristics of the core area of ​​the jet nozzle's airflow can be changed by sliding the bottom and / or top panels, or by changing the side panel styles.

[0072] Step S20: determining target core area features that match the target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model.

[0073] In one possible implementation, after determining the initial jet nozzle configuration and a trained Gaussian process regression model, the electronic device can determine target core region characteristics that match target jet core region information. The Gaussian process regression model can be determined based on a preset mean and covariance function, i.e., the Gaussian process regression model can be trained based on a preset observation dataset and a preset mean and covariance function.

[0074] Optionally, the process of training the Gaussian process regression model in the embodiment of the present disclosure can be implemented in an electronic device, or the Gaussian process regression model can be deployed in an electronic device after training in other devices. The training process can include determining an observation data set including multiple sample nozzle configurations and corresponding annotated core area features, determining the corresponding predicted core area features through the sample nozzle configurations and a preset mean and covariance function, and determining the model difference based on the predicted core area features and the annotated core area features corresponding to each sample nozzle configuration. In response to the difference not meeting the preset convergence condition, adjusting the hyperparameters of the mean and covariance function. In response to the difference meeting the preset convergence condition, determining the Gaussian process regression model based on the mean and covariance function.

[0075] Optionally, the observation dataset D used in the training process is D=(X,Y){(xi ,y i )|i=1,2,…,n} can be pre-set to various sample nozzle configurations x i The corresponding jet nozzle and the annotation core area feature y of the flow field corresponding to the jet nozzle are extracted i Obtained. Through the mean and covariance function, each sample nozzle configuration x in the observation data set i Predict the corresponding core area features, determine the model difference based on the predicted core area features and the annotated core area features corresponding to each sample nozzle configuration, and use the gradient optimization algorithm to solve the following formula to obtain the corresponding model hyperparameter θ as the hyperparameter of the mean and covariance function:

[0076]

[0077] After calculating the hyperparameters, we can get the specific expression of the covariance function and calculate the covariance matrix K(Z,X), K(X,X), K(Z,Z):

[0078]

[0079]

[0080]

[0081] Among them, Z is a prediction set that needs to include m core area features that need to be predicted. After obtaining the above covariance matrix, the mean vector function can be used to calculate the and covariance matrix function Determine the posterior probability distribution that the output of the prediction set obeys

[0082] Figure 3 FIG. 1 is a schematic diagram showing a training Gaussian process regression model according to an embodiment of the present disclosure. Figure 3 As shown, during the model training process, an observation dataset 30 can be first determined. Sample nozzle configurations 31 in the observation dataset 30 are processed using a mean and covariance function 33 to obtain corresponding predicted core area features 34. Then, a model difference 35 is determined based on the labeled core area features 32 corresponding to the sample nozzle configurations 31. A determination is made as to whether the model difference meets a convergence condition 36. If so, a corresponding Gaussian process regression model 37 is directly determined. If not, the hyperparameters of the mean and covariance function are adjusted.

[0083] In one possible implementation, the electronic device may determine, by any method, a target core region feature that matches the target jet core region information based on the initial jet nozzle configuration and a trained Gaussian process regression model. For example, the initial jet nozzle configuration may be used as input to the Gaussian process regression model to determine the corresponding core region feature. In response to a mismatch between the core region feature and the target jet core region information, the jet nozzle configuration is updated and the corresponding core region feature is re-determined. In response to a match between the core region feature and the target jet core region information, the core region feature is determined to be the target core region feature. The core region feature may include at least one of pressure, temperature, Mach number, volume, and position. If the jet nozzle configuration includes a translation dimension and a side panel type, the process of the electronic device updating the jet nozzle configuration and re-determining the corresponding core region feature may include adjusting the translation dimension within the translation dimension range of the top panel and / or selecting one of multiple preset side panel types and updating the selected side panel type as the side panel type in the jet nozzle configuration.

[0084] Figure 4 FIG. 1 is a schematic diagram showing a method for determining target core area characteristics according to an embodiment of the present disclosure. Figure 4 As shown, the electronic device can determine a core area feature 42 corresponding to the current jet nozzle configuration 40 based on the current jet nozzle configuration 40 and the Gaussian process regression model 41. The electronic device can further compare the current core area feature 42 with target jet core area information 43 to determine whether they match 44. If so, the current core area feature 42 is determined to be the target core area feature 45. If not, the current jet nozzle configuration 40 is updated.

[0085] Optionally, the electronic device may predetermine rules for determining whether the core region feature 42 matches the target jet core region information 43. For example, if the target jet core region information 43 is constraint information for limiting specific core region features, that is, it may include at least one of the required pressure range, temperature range, Mach number range, volume range, and position range of the core region feature, the electronic device may determine that the core region feature 42 matches the target jet core region information 43 when each feature in the core region feature 42 meets the constraint of the target jet core region information 43. If the target jet core region information 43 is a specific feature, that is, it may include at least one of the required pressure, temperature, Mach number, volume, and position of the core region of the airflow generated by the jet nozzle, the electronic device may determine that the core region feature 42 matches the target jet core region information 43 when the difference between each feature in the target jet core region information 43 and the corresponding feature in the core region feature 42 is less than a preset threshold.

[0086] In one possible implementation, the electronic device may also determine the target core area characteristics through other means. For example, based on a preset translational size range and side panel type, the jet nozzle configuration is adjusted multiple times to obtain a jet nozzle configuration set. The core area characteristics of each jet nozzle configuration in the jet nozzle configuration set are then predicted using a Gaussian process regression model to obtain an initial population. Population evolution is then performed using a genetic algorithm and the initial population to obtain the core area characteristics corresponding to the optimal solution, which serve as the target core area characteristics. Optionally, the translational size range can represent the maximum size range within which the top and / or bottom panels can move relative to the jet nozzle, and the side panel type can include multiple interchangeable side panel types.

[0087] The genetic algorithm may be the NSGA-II algorithm, and the specific mathematical description of the algorithm is as follows:

[0088] max[f1(x),f2(x),…,f m (x)]

[0089]

[0090] Among them, f i (x) is the objective function to be optimized, x is the variable to be optimized, lb and ub are the lower and upper bound constraints of the variable x, Aeq*x=beq is the linear equality constraint of the variable x, and A*x≤b is the linear inequality constraint of the variable x.

[0091] Step S30: configuring the structure of the jet nozzle according to the jet nozzle configuration corresponding to the target core area characteristics.

[0092] In one possible implementation, after determining the target core area characteristics, the electronic device may further obtain the jet nozzle configuration that determines the target core area characteristics as the corresponding target jet nozzle configuration. The target jet nozzle configuration is used as the structural constraint of the jet nozzle to adjust the jet nozzle, for example, by replacing the side panels of the jet nozzle with side panels of the type specified in the jet nozzle configuration, and sliding the top and / or bottom panels of the jet nozzle according to the translation dimensions specified in the jet nozzle configuration.

[0093] Optionally, the process of changing the side panel type according to the jet nozzle configuration can be performed before applying the jet nozzle, and the process of sliding the top panel and / or bottom panel according to the jet nozzle configuration can be performed before or during the application of the jet nozzle.

[0094] Based on the above technical features, the disclosed embodiment can automatically determine the corresponding jet nozzle configuration based on the Gaussian process regression model and the target jet core area information, and use the jet nozzle configuration as constraint information to configure the jet nozzle, quickly and accurately obtaining a jet nozzle that can produce the required jet core area. At the same time, it can also use a genetic algorithm to screen target core area features that match the target jet core area information from a variety of core area features. This is applicable to both continuous and discrete variables, improving the generalization capability of the disclosed embodiment. Without the need for gradient information, a global parallel search can be performed within the solution space, making it easy to integrate with other optimization methods and less likely to fall into local optimality, thereby improving the accuracy of the final determination of the jet nozzle configuration.

[0095] Figure 5 Schematic diagram of a jet nozzle configuration device according to an embodiment of the present disclosure is shown. Figure 5 As shown, the jet nozzle configuration device of the embodiment of the present disclosure may include:

[0096] An information determination module 50 is used to determine target jet core area information and initial jet nozzle configuration;

[0097] A feature determination module 51 is configured to determine target core area features that match the target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model;

[0098] The structure configuration module 52 is used to configure the structure of the jet nozzle according to the jet nozzle configuration corresponding to the target core area characteristics.

[0099] In a possible implementation, the feature determination module 51 includes:

[0100] a feature acquisition submodule, configured to use the initial jet nozzle configuration as input to the Gaussian process regression model to determine corresponding core area features;

[0101] a feature updating submodule, configured to update the jet nozzle configuration and redetermine the corresponding core area feature in response to the core area feature not matching the target jet core area information;

[0102] The feature determination submodule is configured to determine that the core area feature is a target core area feature in response to a match between the core area feature and the target jet core area information.

[0103] In one possible implementation, the jet nozzle configuration includes a translation dimension and a side plate type.

[0104] In a possible implementation, the feature update submodule includes:

[0105] The structure adjustment unit is used to adjust the translation size of the top plate within the translation size range, and / or select one of a plurality of preset side plate types and update it as the side plate type in the jet nozzle configuration.

[0106] In a possible implementation, the feature determination module 51 includes:

[0107] An information initialization submodule, configured to adjust the jet nozzle configuration multiple times according to a preset translation size range and side panel type to obtain a jet nozzle configuration set;

[0108] A population initialization submodule, configured to predict the core area characteristics of each jet nozzle configuration in the jet nozzle configuration set according to the Gaussian process regression model to obtain an initial population;

[0109] The population evolution submodule is used to perform population evolution using a genetic algorithm and the initial population, and obtain the core area characteristics corresponding to the optimal solution as the target core area characteristics.

[0110] In a possible implementation, the core region characteristic includes at least one of pressure, temperature, Mach number, volume, and position.

[0111] In one possible implementation, the training process of the Gaussian process regression model includes:

[0112] Determine an observational dataset including multiple sample nozzle configurations and corresponding annotated core region features;

[0113] Determining corresponding predicted core area features through the sample nozzle configuration and a preset mean and covariance function;

[0114] determining a model difference based on the predicted core region characteristics and the annotated core region characteristics corresponding to each of the sample nozzle configurations;

[0115] In response to the difference not satisfying a preset convergence condition, adjusting hyperparameters of the mean and covariance function;

[0116] In response to the difference satisfying a preset convergence condition, a Gaussian process regression model is determined according to the mean and covariance function.

[0117] In one possible implementation, the jet end of the jet nozzle includes two side plates fixedly connected, and a top plate and a bottom plate slidably connected. The jet nozzle configuration includes a translational dimension for adjusting the sliding dimension of the top plate and the bottom plate connected to the jet nozzle, and a side plate type for limiting the shape of the two side plates.

[0118] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0119] The present disclosure also provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0120] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0121] An embodiment of the present disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0122] Figure 6 A schematic diagram of an electronic device 800 according to an embodiment of the present disclosure is shown. For example, the electronic device 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0123] Reference Figure 6 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0124] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0125] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0126] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0127] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0128] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0129] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0130] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect changes in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and temperature changes of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0131] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0132] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0133] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions. The computer program instructions can be executed by the processor 820 of the electronic device 800 to perform the above method.

[0134] Figure 7 FIG2 is a schematic diagram showing another electronic device 1900 according to an embodiment of the present disclosure. For example, the electronic device 1900 can be provided as a server or a terminal device. Figure 7The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0135] The electronic device 1900 may further include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output (I / O) interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0136] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0137] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0138] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0139] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0140] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0141] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0142] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0143] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0144] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0145] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for configuring a jet nozzle, characterized in that: The method comprises: Determine the target jet core area information and the initial jet nozzle configuration; Determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model; configuring the structure of the jet nozzle according to the jet nozzle configuration corresponding to the target core area characteristics; Determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model includes: According to a preset translation size range and a side plate type, the jet nozzle configuration is adjusted multiple times to obtain a jet nozzle configuration set; Predicting core area characteristics of each jet nozzle configuration in the jet nozzle configuration set according to the Gaussian process regression model to obtain an initial population; Performing population evolution using a genetic algorithm and the initial population to obtain core area features corresponding to the optimal solution as target core area features; The core region characteristic includes at least one of pressure, temperature, Mach number, volume and position; The training process of the Gaussian process regression model includes: Determine an observational dataset including multiple sample nozzle configurations and corresponding annotated core region features; Determining corresponding predicted core area features through the sample nozzle configuration and a preset mean and covariance function; determining a model difference based on the predicted core region characteristics and the annotated core region characteristics corresponding to each of the sample nozzle configurations; In response to the difference not satisfying a preset convergence condition, adjusting hyperparameters of the mean and covariance function; In response to the difference satisfying a preset convergence condition, a Gaussian process regression model is determined according to the mean and covariance function.

2. The method according to claim 1, characterized in that Determining target core area features that match target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model includes: Using the initial jet nozzle configuration as input to the Gaussian process regression model, determining corresponding core region features; In response to the core region characteristics not matching the target jet core region information, updating the jet nozzle configuration and re-determining the corresponding core region characteristics; In response to the core region feature matching the target jet core region information, the core region feature is determined to be a target core region feature.

3. The method according to claim 2, characterized in that The jet nozzle configuration includes translational dimensions and side plate type.

4. The method according to claim 3, characterized in that The updating of the jet nozzle configuration and re-determining the corresponding core area characteristics includes: The translation dimension of the top panel is adjusted within the translation dimension range of the top panel, and / or one of a plurality of preset side panel types is selected and updated as the side panel type in the jet nozzle configuration.

5. The method according to any one of claims 1 to 4, characterized in that The jet end of the jet nozzle includes two side plates fixedly connected, and a top plate and a bottom plate slidably connected. The jet nozzle configuration includes a translational dimension for adjusting the sliding dimension of the top plate and the bottom plate connected to the jet nozzle, and a side plate type for limiting the shape of the two side plates.

6. A jet nozzle configuration device, characterized in that: The device comprises: An information determination module is used to determine target jet core area information and initial jet nozzle configuration; a feature determination module, configured to determine target core area features matching the target jet core area information based on the initial jet nozzle configuration and the trained Gaussian process regression model; a structure configuration module, configured to configure the structure of the jet nozzle according to the jet nozzle configuration corresponding to the target core area characteristics; The feature determination module includes: An information initialization submodule, configured to adjust the jet nozzle configuration multiple times according to a preset translation size range and side panel type to obtain a jet nozzle configuration set; A population initialization submodule, configured to predict the core area characteristics of each jet nozzle configuration in the jet nozzle configuration set according to the Gaussian process regression model to obtain an initial population; A population evolution submodule is used to perform population evolution using a genetic algorithm and the initial population, and obtain core area features corresponding to the optimal solution as target core area features; The core region characteristic includes at least one of pressure, temperature, Mach number, volume and position; The training process of the Gaussian process regression model includes: Determine an observational dataset including multiple sample nozzle configurations and corresponding annotated core region features; Determining corresponding predicted core area features through the sample nozzle configuration and a preset mean and covariance function; determining a model difference based on the predicted core region characteristics and the annotated core region characteristics corresponding to each of the sample nozzle configurations; In response to the difference not satisfying a preset convergence condition, adjusting hyperparameters of the mean and covariance function; In response to the difference satisfying a preset convergence condition, a Gaussian process regression model is determined according to the mean and covariance function.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to implement the method according to any one of claims 1 to 5 when executing the instructions stored in the memory.

8. A non-volatile computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 5 is implemented.

Images