Window heating optimization method and device and electronic equipment

Abstract

The invention provides a window heating optimization method and apparatus, and an electronic device. The method comprises the steps of obtaining a first heating parameter for controlling a heating device to work; obtaining droplet mass transfer parameters when droplets on the wall surface of the window are evaporated and water vapor is condensed in the structure of the window after the window is heated by the heating device according to the first heating parameter; simulating the liquid drops on the window to obtain liquid drop simulation parameters; inputting the parameters of the window, the droplet mass transfer parameters and the droplet simulation parameters into a droplet evaporation and condensation model to obtain an output droplet heating simulation result; inputting the droplet heating simulation result into a window heating simulation model to obtain an output window heating simulation result; and optimizing the structure and / or parameters of the heating device and / or the first heating parameter according to the window heating simulation result. The simulation degree of the mist liquid drops can be improved, and then the structure and / or parameters of the heating device and / or the first heating parameters are optimized.

Description

Technical Field

[0001] This application relates to the field of thermal fluid simulation technology, and in particular to a window heating optimization method, apparatus and electronic device. Background Technology

[0002] With the development of artificial intelligence technology, intelligent driving technology has emerged. Intelligent driving technology is a technology that assists or replaces humans in driving, and it mainly relies on three parts: environmental perception, decision-making, and control. Among them, environmental perception is the foundation of intelligent driving's decision-making and control. Environmental perception mainly depends on the vehicle's onboard cameras. Fogging of the camera window will prevent the camera from clearly capturing lane lines or other targets such as vehicles in front, thus affecting intelligent driving functions, and in severe cases, even causing some intelligent driving functions to malfunction.

[0003] Currently, to solve the above problems, the main approach is to achieve defogging by heating the imaging window with a resistance wire, and to provide parameter guidance through simulation models of fog droplet evaporation and condensation, such as commercial computational fluid dynamics software Fluent.

[0004] However, simulation methods using commercial computational fluid dynamics software such as Fluent assume that the mist is uniformly distributed as a thin film on the solid surface when evaporation and condensation are simulated. But mist actually exists as droplets distributed on the solid surface, leading to significant errors with existing simulation methods. Summary of the Invention

[0005] This invention provides a window heating optimization method, apparatus, and electronic device to solve or alleviate technical problems in the prior art.

[0006] The technical solution adopted in this invention is as follows:

[0007] In a first aspect, embodiments of this application provide a window heating optimization method, comprising: acquiring a first heating parameter for controlling the operation of a heating device; acquiring droplet mass transfer parameters when droplets evaporate and water vapor condenses in the structure of the window after the window is heated by the heating device with the first heating parameter; simulating droplets on the window to obtain droplet simulation parameters; inputting the window parameters, the droplet mass transfer parameters, and the droplet simulation parameters into a droplet evaporation and condensation model to obtain droplet heating simulation results output by the droplet evaporation and condensation model; inputting the droplet heating simulation results into a window heating simulation model to obtain window heating simulation results output by the window heating simulation model, wherein the window heating simulation model is constructed based on the structure and parameters of the window and the structure and parameters of the heating device; and optimizing the structure and / or parameters of the heating device and / or the first heating parameter based on the window heating simulation results.

[0008] Secondly, embodiments of this application provide a window heating optimization device, comprising: an acquisition module, configured to acquire first heating parameters for controlling the operation of the heating device, and acquire droplet mass transfer parameters when droplets evaporate and water vapor condenses in the structure of the window after the window is heated by the heating device; a first simulation module, configured to simulate droplets on the window to obtain droplet simulation parameters; a second simulation module, configured to input the parameters of the window and the droplet simulation parameters into a droplet evaporation and condensation model to obtain droplet heating simulation results output by the droplet evaporation and condensation model; a window heating simulation module, configured to input the droplet heating simulation results into a window heating simulation model to obtain window heating simulation results output by the window heating simulation model, wherein the window heating simulation model is constructed based on the structure and parameters of the window and the structure and parameters of the heating device; and an optimization module, configured to optimize the structure and / or parameters and / or the first heating parameters of the heating device based on the window heating simulation results.

[0009] Thirdly, embodiments of this application provide an electronic device having a computer program stored thereon, which, when executed by a processor, implements the window heating optimization method as described in any one of the first aspects of the embodiments.

[0010] The processor, communication interface, memory, and communication bus are provided. The processor, memory, and communication interface communicate with each other through the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the window heating optimization method as described in any of the first aspects of the embodiments.

[0011] As can be seen from the above scheme, by simulating the droplets on the window, the accuracy of the droplet heating simulation results output by the droplet evaporation and condensation model can be improved. At the same time, by outputting the window heating simulation results through the window heating simulation model, it is convenient to observe the defogging situation of the window after being heated under the first heating parameter, and thus the structure and / or parameters and / or the first heating parameter of the heating device can be optimized. Attached Figure Description

[0012] Figure 1 This is a flowchart of a window heating optimization method according to an embodiment of this application;

[0013] Figure 2 This is a schematic diagram of the simulation results of window heating according to one embodiment of this application;

[0014] Figure 3 This is a schematic diagram of the window heating simulation results according to another embodiment of this application;

[0015] Figure 4This is a flowchart of a droplet evaporation and condensation model training method according to an embodiment of this application;

[0016] Figure 5 This is a schematic diagram of a window heating optimization device according to an embodiment of this application;

[0017] Figure 6 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Figure 1 This is a flowchart of a window heating optimization method according to an embodiment of this application, as follows: Figure 1 As shown, the window heating optimization method includes the following steps:

[0020] Step 101: Obtain the first heating parameters used to control the operation of the heating device.

[0021] Step 102: Obtain the droplet mass transfer parameters when the droplets on the window wall evaporate and water vapor condenses in the structure of the window after the window is heated by the heated device with the first heating parameters.

[0022] Step 103: Simulate the droplets on the viewport to obtain the droplet simulation parameters.

[0023] Step 104: Input the window parameters, droplet mass transfer parameters, and droplet simulation parameters into the droplet evaporation and condensation model to obtain the droplet heating simulation results output by the droplet evaporation and condensation model.

[0024] Step 105: Input the droplet heating simulation results into the window heating simulation model to obtain the window heating simulation results output by the window heating simulation model.

[0025] Step 106: Based on the window heating simulation results, optimize the structure and / or parameters and / or the first heating parameter of the heating device.

[0026] To optimize the arrangement and specific parameters of the heating components used for defogging the camera window, such as the thickness of the heating wire and the heating power, it is first necessary to obtain the initial heating parameters currently used to control the operation of the heating devices. This includes obtaining the droplet mass transfer parameters when droplets evaporate and water vapor condenses within the window structure after the window is heated by the heating devices using these initial heating parameters. At this point, the structure and parameters of the current heating devices can be evaluated. Specifically, droplets on the window can be simulated to obtain droplet simulation parameters. These droplets are then used as the defogging target. The window parameters, droplet mass transfer parameters, and droplet simulation parameters are input into the droplet evaporation and condensation model. The droplet heating simulation results output from the window heating simulation model are also input into the window heating simulation model. The construction of the window heating simulation model can be based on the currently configured structure and parameters of the heating components, as well as the structure and parameters of the window, to perform simulations and obtain the window heating simulation model. Then, the completion status of the defogging target can be determined by observation, thereby understanding the direction for optimizing the structure and / or parameters and / or the first heating parameter of the heating device. For example, if the simulation results of window heating show that fog is not completely removed in some areas of the camera window, the structure of the heating wire needs to be changed to improve the defogging effect in those areas. Although the heating parameters of the heating component can be modified, such as increasing the heating power of the heating component to improve the defogging effect in some areas, this will also increase the temperature of the camera window, affecting the display effect. Therefore, it is still necessary to modify the structure and / or parameters of the heating wire in the heating component, such as the number of turns and the thickness of the heating wire. The simulation results of window heating can be as follows: Figure 2 As shown, the state of the droplets in the window can be displayed using the droplet diameter after heating and cooling for a certain period of time, avoiding the formation of droplets. It can be seen that using... Figure 2 The heating device in the simulation was defogging. After 270 seconds of heating, liquid remained on the viewing window. Heating continued for 360 seconds, at which point only a small number of droplets remained on the viewing window. Defogging was completed at 450 seconds. Therefore, this can be used as a basis to guide the optimization of the structure and parameters of the heating device. Completing defogging in 360 seconds can shorten the defogging time. Alternatively, the simulation results of window heating can be as follows: Figure 3 As shown, the droplet diameter is used to display the state of the window after heating and cooling for a certain period of time to avoid droplets. It can be seen that when the heating power is low, due to external convection heat dissipation, it cannot effectively remove fog (a large number of fog droplets still exist after heating for 20 minutes). Moreover, the defogging time and heating power are not linearly related, but rather exhibit a power-law relationship. Therefore, under the premise of meeting the defogging time requirements, an appropriate heating power can be selected. After optimization, simulation can be performed again to observe and evaluate the optimized defogging effect.

[0027] In this embodiment of the application, by simulating the droplets on the window, the accuracy of the droplet heating simulation results output by the droplet evaporation and condensation model can be improved. At the same time, by outputting the window heating simulation results through the window heating simulation model, it is convenient to observe the defogging situation of the window after being heated under the first heating parameter, thereby enabling the optimization of the structure and parameters of the heating device.

[0028] In one possible implementation, the process of simulating a droplet on a viewport and obtaining droplet simulation parameters may further include: determining a second relationship between the droplet cap area and the droplet wetting radius and droplet contact angle based on a first relationship between droplet height and droplet wetting radius and droplet contact angle; determining a third relationship between droplet volume and droplet wetting radius and droplet contact angle based on the first relationship; determining a fourth relationship between droplet wetting radius and droplet contact angle and droplet volume based on the third relationship; and determining droplet simulation parameters based on the droplet wetting radius, droplet volume, second relationship, and fourth relationship of the droplet on the viewport.

[0029] To simulate a droplet on a viewport, we first determine the primary relationship between the droplet height, the droplet wetting radius, and the droplet contact angle. This primary relationship can be expressed as:

[0030]

[0031] Where h represents the droplet height, r represents the droplet wetting radius, and θ represents the droplet contact angle.

[0032] Then, based on the first relationship, the second relationship between the droplet cap area and the droplet wetting radius and droplet contact angle is determined, as well as the third relationship between the droplet volume and the droplet wetting radius and droplet contact angle. The second relationship can be expressed as:

[0033]

[0034] Among them, A dome Used to characterize the area of ​​a droplet cap.

[0035] The third relation can be represented as:

[0036]

[0037] Then, the derivation is performed:

[0038]

[0039] V is used to characterize the droplet volume.

[0040] Based on the third relationship, a fourth relationship can be determined between the droplet wetting radius, the droplet contact angle, and the droplet cap area. This fourth relationship can be derived using Cardano's formula.

[0041]

[0042] The third relation can also be derived from equation (3) as follows:

[0043]

[0044] In this embodiment of the application, by simulating the droplet on the window, the fourth relationship between the droplet wetting radius, the droplet contact angle and the droplet volume can be determined. Therefore, the droplet can be simulated according to the parameters of the actual droplet on the window, and the actual contact angle of the droplet can be simulated, thereby making the droplet simulation results more accurate.

[0045] Figure 4 This is a flowchart illustrating a droplet evaporation and condensation model training method according to an embodiment of this application. Figure 4 As shown, the droplet evaporation and condensation model is obtained through the following steps:

[0046] Step 301: Calculate the mass flow rate of the sample window structure wall and determine the state of the sample window structure wall as either droplet condensation or droplet evaporation based on the mass flow rate.

[0047] Step 302: When the state of the sample window structure wall is droplet condensation, calculate the water vapor mass source term based on the contact angle of the droplets on the sample window structure wall.

[0048] Step 303: When the state of the sample window structure wall is droplet evaporation, calculate the water vapor mass source term based on the volume of the droplets on the sample window structure wall.

[0049] Step 304: Update the droplet parameters according to the obtained water vapor mass source term and the preset iteration time, and generate the droplet evaporation and condensation model based on the droplet parameters obtained in each iteration.

[0050] To train the droplet evaporation and condensation model, the mass flow rate of the sample window structure wall needs to be calculated first. The mass flow rate can be calculated using the following formula:

[0051]

[0052] in, H is used to characterize the mass flow rate of the sample window structure wall, and c is used to characterize the convective heat transfer coefficient. p P is used to characterize the specific heat capacity of air. r Prandtl number, S, is used to characterize air. c The Schmidt number, M, is used to characterize air. wM is used to characterize the molar mass of water. air P is used to characterize the molar mass of air. sf P is used to characterize the saturated vapor pressure of the sample window structure wall at a unit temperature. sb Used to characterize the saturated vapor pressure of the sample window structure wall at air temperature. Used to characterize the relative humidity of the sample window structure wall surface. P0 is used to characterize the partial pressure of water vapor, while P0 is used to characterize atmospheric pressure.

[0053] The mass flow rate can be used to determine whether the state of the sample window structure wall is droplet condensation or droplet evaporation. For example, when the mass flow rate is greater than 0, it means the droplet is evaporating; when the mass flow rate is less than 0, it means the droplet is condensing; and when the mass flow rate is equal to 0, the droplet state does not change.

[0054] If the mass flow rate indicates that the state of the sample window structure wall is droplet condensation, the water vapor mass source term can be calculated based on the contact angle of the droplets on the sample window structure wall. If the mass flow rate indicates that the state of the sample window structure wall is droplet evaporation, the water vapor mass source term can be calculated based on the volume of the droplets on the sample window structure wall. The water vapor mass source term is used to characterize the evaporation or condensation process of water vapor. After obtaining the water vapor mass source term, the droplet parameters are updated according to the preset iteration time, and a droplet evaporation and condensation model is generated based on the droplet parameters obtained in each iteration. For example, assuming a single droplet at iteration time t... iter If the volume change is ΔV, then:

[0055]

[0056] It can be derived that the volume V of a single droplet after (n+1) iterations is... n+1 =V n +ΔV n+1 (9), where n is a positive integer.

[0057] In addition, it is necessary to calculate the wetting ratio of each unit grid in the wall of the sample window structure:

[0058] R Awet =Nπr 2 / A cell (10)

[0059] Among them, R Awet The wettability of a unit grid is used to characterize the proportion of droplets, N is used to characterize the number of droplets on the sample window structure wall, and A is used to characterize the number of droplets on the sample window structure wall. cell Used to characterize the area of ​​a unit grid.

[0060] The calculation of the water vapor mass source term can then be expressed as the following formula:

[0061]

[0062] Among them, S w V is used to characterize the water vapor mass source term. cell Used to characterize the near-wall unit grid volume, which is the partial volume of a droplet within the area of ​​each unit grid.

[0063] When mist droplets cover most of the solid surface, adjacent droplets merge. After merging, the droplet volume remains unchanged, but the contact area with the solid surface decreases, thus releasing new dry surface area and generating new droplets. Therefore, the maximum wetting ratio of mist droplets on the solid surface is close to a constant value. Let the maximum wetting ratio be R. Mwet After finely dividing the unit grid of the sample window structure wall, since the unit grid surface is small, it can be assumed that the droplets are uniformly distributed on the unit grid surface. Therefore, when deriving the self-similar condensation of droplets, the calculation method for the number of droplets on the unit grid of the sample window structure wall can be:

[0064]

[0065] at the same time:

[0066]

[0067] The relationship between the droplet radius r and the number N of droplets per unit grid on the sample window structure wall can be derived:

[0068]

[0069] Specifically, when the sample window structure wall is in the state of droplet condensation, the method for calculating the water vapor mass source term based on the contact angle of the droplets on the sample window structure wall can be as follows: Obtain the contact angle of the droplets on the sample window structure wall. When the contact angle is equal to 0, update the contact angle to the wetting angle and the wetting ratio to the maximum wetting ratio. Here, the wetting angle is the angle between the tangent of the droplet and the sample window structure wall, and the maximum wetting ratio is the ratio of the area occupied by the droplet in contact with and attached to the sample window structure wall to the unit grid area of ​​the sample window structure wall. Calculate the droplet volume per unit grid area based on the mass flow rate, the maximum wetting ratio, and the wetting angle, and calculate the number of droplets per unit grid area based on the droplet volume per unit grid area and the maximum wetting ratio. Calculate the water vapor mass source term based on the droplet volume per unit grid area, the number of droplets per unit grid area, the number of iterations, and the iteration time.

[0070] For example, assuming the droplet radius r, droplet volume V, contact angle θ, number of droplets N on the cell grid of the sample window structure wall, and wetting ratio R... Awet Water vapor mass source item Sw All are 0.

[0071] When the contact angle θ = 0, it represents initial condensation. Since the unit mesh of the sample window structure wall is small, and to ensure camera imaging quality, the inner surface of the sample window structure wall is a clean surface. The model assumes that initial condensation is due to self-similarity in droplet distribution. At this time, the droplet height h is 0. Then, the current iteration t is calculated respectively. iter The subsequent droplet parameters,

[0072] Update the contact angle θ to θ w Wetting ratio R Awet Updated to R Mwet .

[0073] Calculate the total volume V of a unit grid droplet using the following formula. all :

[0074]

[0075] Update the unit grid droplet radius r according to the following formula:

[0076]

[0077] The number N of droplets on the cell grid of the sample window structure wall is then updated according to the following formula:

[0078] N = R Mwet *A cell / (πr 2 (18)

[0079] The droplet volume V is then updated according to the following formula:

[0080] V = V all / N (19)

[0081] After updating the above parameters, the water vapor mass source term can be updated according to equation (11).

[0082] Specifically, when the contact angle equals the wetting angle and the wetting ratio equals the maximum wetting ratio, the droplet height, cap area, and volume change at each iteration on the sample window structure wall are calculated based on the mass flow rate, maximum wetting ratio, and wetting angle. The number of droplets per unit grid area is then calculated based on the volume change at each iteration and the maximum wetting ratio. Finally, the water vapor mass source term is calculated based on the droplet height, cap area, volume change at each iteration, and number of droplets per unit grid area on the sample window structure wall.

[0083] When the contact angle θ = θ w And R Awet =R MwetWhen the droplet condensation mode changes to self-similar growth, the droplet height h can be calculated using equation (1), and the droplet cap area A can be calculated using equation (2). dome The droplet volume change ΔV is calculated using equation (8), the droplet volume V is updated using equation (9), the droplet radius r is updated using equation (14), the number of droplets N on the cell grid of the sample window structure wall is updated using equation (15), and the water vapor mass source term S is updated using equation (11). w .

[0084] Specifically, when the contact angle equals the wetting angle and the wetting ratio is less than the maximum wetting ratio, the droplet volume per unit grid area is calculated based on the mass flow rate, the maximum wetting ratio, and the wetting angle, and the water vapor mass source term is calculated based on the droplet volume per unit grid area, the number of iterations, and the iteration time.

[0085] When the contact angle θ = θ w And R Awet <R Mwet Since the growth resistance is small when the existing droplet is the nucleation point, the model assumes that the droplet condenses and grows with the existing droplet as the nucleation point. The condensation and growth mode is that the wetting radius r increases while the contact angle θ remains unchanged. Then, the droplet height h can be calculated by equation (1), and the droplet cap area A can be calculated by equation (2). dome The droplet volume change ΔV is calculated using equation (8), the droplet volume V is updated using equation (9), the droplet radius r is updated using equation (6), and the wetting ratio R is updated using equation (10). Awet .

[0086] If R Awet ≤R Mwet If the number N of droplets on the cell grid of the sample window structure wall remains unchanged.

[0087] If R Awet >R Mwet Then it transforms into droplet distribution self-similar growth, at which point R needs to be updated. Awet =R Mwet The droplet radius r is updated by equation (14), the number N of droplets on the cell grid of the sample window structure wall is updated by equation (15), and the water vapor mass source term S is updated by equation (11). w .

[0088] Specifically, when the contact angle is less than the wetting angle but greater than the critical angle, the droplet volume per unit grid area is calculated based on the mass flow rate, maximum wetting ratio, and contact angle, where the critical angle is the minimum angle during droplet evaporation. The contact angle is iterated based on the droplet volume per unit grid area, and the water vapor mass source term is calculated based on the iterated contact angle, the droplet volume per unit grid area, the number of iterations, and the iteration time.

[0089] When the contact angle θc <θ<θ w Since the growth resistance is small when the existing droplet cap interface is used as the nucleation point, the model assumes that the droplet condenses and grows at the existing droplet cap interface, and the condensation growth mode is assumed to be an increase in the contact angle θ. The droplet height h can then be calculated using equation (1). The droplet cap area A can be calculated using equation (2). dome The droplet volume change ΔV is calculated using equation (8). The droplet volume V is updated using equation (9). The droplet contact angle θ is updated using equation (5).

[0090] If θ≤θ w Then, the number N, droplet radius r, and wetting ratio R of droplets on the cell grid of the sample window structure wall are... Awet It remains unchanged.

[0091] If θ>θ w Then update θ = θ w The droplet radius r is updated using equation (6). The wetting ratio R is updated using equation (10). Awet If R Awet <R Mwet If the number N of droplets on the cell grid of the sample window structure wall remains unchanged.

[0092] If R Awet >R Mwet Then it transforms into droplet distribution self-similar growth, at which point R is updated. Awet =R Mwet The droplet radius r is updated using equation (14). The number N of droplets on the cell grid of the sample window structure wall is updated using equation (15). The water vapor mass source term S is updated using equation (11). w .

[0093] If the mass flow rate indicates that the state of the sample window structure wall is droplet evaporation, then the water vapor mass source term needs to be calculated based on the volume of the droplets on the sample window structure wall. Specifically, it is first necessary to determine whether the droplet volume on the sample window structure wall is greater than 0.

[0094] If the droplet volume on the sample window structure wall is equal to 0, then there are no droplets. The droplet wetting radius r, droplet volume V, contact angle θ, number of droplets N on the unit grid of the sample window structure wall, and wetting ratio R are also considered. Awet Keep it at 0, water vapor mass source term S w Updated to 0.

[0095] If the volume of the droplet on the wall surface of the sample window structure is greater than 0, then the height, spherical cap area, and volume change at each iteration of the droplet on the wall surface of the sample window structure are calculated based on the mass flow rate, maximum wetting ratio, and contact angle. Here, the maximum wetting ratio is the ratio of the area occupied by the droplet contacting and adhering to the wall surface of the sample window structure to the unit grid area of the wall surface of the sample window structure.

[0096] If the droplet volume V > 0 and the droplet starts to evaporate at this time, the droplet height h can be calculated by Equation (1). The spherical cap area A of the droplet is calculated by Equation (2) dome . The droplet volume change ΔV is calculated by Equation (8).

[0097] When the volume change at iteration is less than or equal to the droplet volume, and the contact angle is less than the wetting angle and greater than the critical angle, the water vapor mass source term is calculated based on the height, spherical cap area, volume change at each iteration of the droplet on the wall surface of the sample window structure, and the number of droplets in the unit grid area. Here, the critical angle is the minimum angle during the droplet evaporation process, and the wetting angle is the angle between the tangent of the droplet and the wall surface of the sample window structure.

[0098] If ΔV ≤ V, and θ c < θ ≤ θ w During the droplet evaporation process, the contact angle θ decreases. At this time, the droplet volume V can be updated by Equation (9). The water vapor mass source term S is updated by Equation (11) w . At the same time, since the number N of droplets on the unit grid of the wall surface of the sample window structure remains unchanged at this time, the contact angle θ can be calculated by Equation (5).

[0099] If θ c ≤ θ ≤ θ w , then the droplet radius r and the wetting ratio R Awet remain unchanged.

[0100] If θ < θ c , then θ = θ c , and at this time, the wetting radius r can be updated by Equation (6). The wetting ratio R is updated by Equation (10) Awet .

[0101] When the volume change at iteration is less than the droplet volume and the contact angle is equal to the critical angle, the wetting ratio is iterated based on the height, spherical cap area, and volume change at each iteration of the droplet on the wall surface of the sample window structure, and the water vapor mass source term is calculated based on the iterated wetting ratio and the volume change at each iteration.

[0102] If ΔV < V, and θ = θ c , then during the droplet evaporation process, the wetting radius r decreases. At this time, the wetting radius r can be updated by Equation (6). The wetting ratio R is updated by Equation (10) AwetThe droplet volume V is updated using equation (9). The water vapor mass source term S is updated using equation (11). w At this time, the number N of droplets on the cell grid of the sample window structure remains unchanged.

[0103] If ΔV > V, the droplet evaporates and disappears. In this case, the water vapor mass source term S is updated by the following formula. w :

[0104] S w =N·V / V cell / t iter (20)

[0105] It also updates the wetting radius r to 0, the droplet volume V to 0, the contact angle θ to 0, the number of droplets N on the cell grid of the sample window structure wall to 0, and the wetting ratio R. Awet It is 0.

[0106] After obtaining the above water vapor mass source items S w And obtain the water vapor mass source term S w During the process, the basic model is trained based on the parameters updated at each iteration to obtain the droplet evaporation and condensation model.

[0107] In this embodiment of the application, by training the model with various parameters of droplets on the wall of the simulated sample window structure under different conditions, the obtained droplet evaporation and condensation model can be made more accurate.

[0108] Figure 5 This is a schematic diagram of a window heating optimization device according to an embodiment of this application, as shown. Figure 5 As shown, the window heating optimization device 400 includes: an acquisition module 401, a first simulation module 402, a second simulation module 403, a window heating simulation module 404, and an optimization module 405.

[0109] The acquisition module 401 is used to acquire the first heating parameters for controlling the operation of the heating device, and to acquire the droplet mass transfer parameters when the droplets on the wall of the window evaporate and water vapor condenses in the structure of the window after the window is heated by the heating device.

[0110] The first simulation module 402 is used to simulate the droplets on the view window and obtain the droplet simulation parameters.

[0111] The second simulation module 403 is used to input the parameters of the window and the droplet simulation parameters into the droplet evaporation and condensation model to obtain the droplet heating simulation results output by the droplet evaporation and condensation model.

[0112] The window heating simulation module 404 is used to input the droplet heating simulation results into the window heating simulation model and obtain the window heating simulation results output by the window heating simulation model. The window heating simulation model is constructed based on the structure and parameters of the window and the structure and parameters of the heating device.

[0113] The optimization module 405 is used to optimize the structure and / or parameters and / or the first heating parameter of the heating device based on the heating simulation results of the window.

[0114] To optimize the arrangement and specific parameters of the heating components used for defogging the camera window, such as the thickness of the heating wire and the heating power, it is first necessary to obtain the initial heating parameters currently used to control the operation of the heating devices. This includes obtaining the droplet mass transfer parameters when droplets evaporate and water vapor condenses within the window structure after the window is heated by the heating devices using these initial heating parameters. At this point, the structure and parameters of the current heating devices can be evaluated. Specifically, droplets on the window can be simulated to obtain droplet simulation parameters. These droplets are then used as the defogging target. The window parameters, droplet mass transfer parameters, and droplet simulation parameters are input into the droplet evaporation and condensation model. The droplet heating simulation results output from the window heating simulation model are also input into the window heating simulation model. The construction of the window heating simulation model can be based on the currently configured structure and parameters of the heating components, as well as the structure and parameters of the window, to perform simulations and obtain the window heating simulation model. Then, the completion status of the defogging target can be determined by observation, thereby understanding the direction for optimizing the structure and / or parameters and / or the first heating parameter of the heating device. For example, if the simulation results of window heating show that the fog is not completely removed in some areas of the camera window, it is necessary to change the structure of the heating wire to improve the defogging effect in those areas. Although the heating parameters of the heating component can be modified, such as increasing the heating power of the heating component to improve the defogging effect in some areas, this will also increase the temperature of the camera window, which will affect the display effect. Therefore, it is still necessary to modify the structure and / or parameters of the heating wire in the heating component, such as the number of turns and the thickness.

[0115] In this embodiment of the application, by simulating the droplets on the window, the accuracy of the droplet heating simulation results output by the droplet evaporation and condensation model can be improved. At the same time, by outputting the window heating simulation results through the window heating simulation model, it is convenient to observe the defogging situation of the window after being heated under the first heating parameter, thereby enabling the optimization of the structure and / or parameters and / or the first heating parameter of the heating device.

[0116] Figure 6 This is a schematic diagram of an electronic device according to one embodiment of this application. The specific embodiments of this application do not limit the specific implementation of the electronic device. Figure 6As shown, the electronic device 500 may include: a processor 501, a communications interface 502, a memory 503, and a communications bus 504. Wherein:

[0117] The processor 501, communication interface 502, and memory 503 communicate with each other through the communication bus 504.

[0118] Communication interface 502 is used for communication with other electronic devices or servers.

[0119] The processor 501 is used to execute program 505, which can specifically execute the relevant steps in any of the aforementioned method embodiments.

[0120] Specifically, program 505 may include program code that includes computer operation instructions.

[0121] The processor 501 may be a CPU, an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The smart device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0122] Memory 503 is used to store program 505. Memory 503 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0123] Specifically, program 505 can be used to cause processor 501 to execute any of the methods in the foregoing embodiments.

[0124] The specific implementation of each step in program 505 can be found in the corresponding steps and units described in the aforementioned window heating optimization method embodiments, and will not be repeated here. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the devices and modules described above can be referred to the corresponding process descriptions in the aforementioned method embodiments, and will not be repeated here.

[0125] The electronic device described in this application processes images of documents to be detected using a pre-trained model and at least one target model. This replaces manual selection of abnormal documents, and the model-based detection method improves the accuracy of document anomaly detection. Furthermore, by setting up a pre-trained model and at least one target model, the efficiency of anomaly detection can be improved.

[0126] This application also provides a computer-readable storage medium storing instructions for causing a machine to perform any of the methods described in the various method embodiments herein. Specifically, a system or apparatus equipped with a storage medium storing software program code that implements the functions of any of the embodiments described above, and enabling the computer (or CPU or MPU) of the system or apparatus to read and execute the program code stored in the storage medium.

[0127] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of this application.

[0128] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0129] This application also provides a computer program product, including computer instructions that instruct a computing device to perform any corresponding operation in the above-described plurality of method embodiments.

[0130] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of this application can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of this application.

[0131] The methods described in the embodiments of this application can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code downloaded over a network that is originally stored in a remote recording medium or a non-transitory machine-readable medium and will be stored in a local recording medium. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0132] It should be noted that not all steps and modules in the above processes and system structure diagrams are necessary; some steps or modules can be omitted as needed. The execution order of each step is not fixed and can be adjusted as required. The system structure described in the above embodiments can be a physical structure or a logical structure. That is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or they may be jointly implemented by certain components in multiple independent devices.

[0133] In the above embodiments, the hardware modules can be implemented mechanically or electrically. For example, a hardware module may include permanent dedicated circuitry or logic (such as a dedicated processor, FPGA, or ASIC) to perform the corresponding operation. The hardware module may also include programmable logic or circuitry (such as a general-purpose processor or other programmable processor), which can be temporarily configured by software to perform the corresponding operation. The specific implementation method (mechanical, dedicated permanent circuitry, or temporarily configured circuitry) can be determined based on cost and time considerations.

[0134] The present application has been shown and described in detail above with reference to the accompanying drawings and preferred embodiments. However, the present application is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art will know that more embodiments of the present application can be obtained by combining the code review methods in the different embodiments above. These embodiments are also within the protection scope of the present application.

Claims

1. A method of window heating optimization, characterized by, The method comprises the following steps: obtaining a first heating parameter for controlling the operation of a heating device; obtaining a droplet mass transfer parameter of a droplet on a wall surface of a window after the window is heated by the heating device at the first heating parameter, and the evaporation of the droplet and the condensation of water vapor in the structure of the window; simulating the droplet on the window to obtain a droplet simulation parameter; inputting the parameters of the window, the droplet mass transfer parameter and the droplet simulation parameter into a droplet evaporation and condensation model to obtain a droplet heating simulation result output by the droplet evaporation and condensation model; inputting the droplet heating simulation result into a window heating simulation model to obtain a window heating simulation result output by the window heating simulation model, wherein the window heating simulation model is constructed according to the structure and parameters of the window and the structure and parameters of the heating device; optimizing the structure and / or parameters of the heating device and / or the first heating parameter according to the window heating simulation result.

2. The method of claim 1, wherein, The method of simulating the droplet on the window to obtain the droplet simulation parameter comprises the following steps: determining a second relationship between a droplet spherical cap area and a droplet wetting radius and a droplet contact angle according to a first relationship between the droplet height and the droplet wetting radius and the droplet contact angle; determining a third relationship between a droplet volume and the droplet wetting radius and the droplet contact angle according to the first relationship; determining a fourth relationship between the droplet wetting radius and the droplet contact angle and the droplet volume according to the third relationship; determining the droplet simulation parameter according to the droplet wetting radius, the droplet volume, the second relationship and the fourth relationship of the droplet on the window.

3. The method of claim 1, wherein, The droplet evaporation and condensation model is obtained by the following method: calculating a mass flow rate of a sample window structure wall surface, and determining a state of the sample window structure wall surface as droplet condensation or droplet evaporation according to the mass flow rate; when the state of the sample window structure wall surface is droplet condensation, calculating a water vapor mass source term according to a contact angle of a droplet on the sample window structure wall surface, wherein the water vapor mass source term is used to represent the evaporation or condensation process of water vapor; when the state of the sample window structure wall surface is droplet evaporation, calculating the water vapor mass source term according to a volume of the droplet on the sample window structure wall surface; updating the droplet parameters according to the obtained water vapor mass source term at a preset iteration time, and generating the droplet evaporation and condensation model by the droplet parameters obtained at each iteration.

4. The method of claim 3, wherein, The method of calculating the water vapor mass source term according to the contact angle of the droplet on the sample window structure wall surface comprises the following steps: obtaining the contact angle of the droplet on the sample window structure wall surface; when the contact angle is equal to 0, updating the contact angle to a wetting angle and updating a wetting proportion to a maximum wetting proportion, wherein the wetting angle is an included angle between a tangent line of the droplet and the sample window structure wall surface, and the maximum wetting proportion is a ratio of an area occupied by the droplet when the droplet contacts and adheres to the sample window structure wall surface to a unit grid area of the sample window structure wall surface. calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time.

5. The method of claim 4, wherein, The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time.

6. The method of claim 4, wherein, The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; 7. The method of claim 4, wherein, calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time. The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; 8. The method of claim 3, wherein, The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time. The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time. The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time. The method further comprises: calculating a droplet volume in a unit grid area according to the mass flow rate, the maximum wetting ratio and the wetting angle, and calculating a droplet number in the unit grid area according to the droplet volume in the unit grid area and the maximum wetting ratio; calculating the water vapor mass source term according to the droplet volume in the unit grid area, the droplet number in the unit grid area, the iteration number and the iteration time. The method further comprises: if the droplet volume on the sample window structure wall surface is greater than 0, calculating a droplet height on the sample window structure wall surface, a spherical cap area and a volume change at each iteration according to the mass flow rate, the maximum wetting ratio and the contact angle, wherein the maximum wetting ratio is a ratio of an area occupied by the droplet contacting and adhering to the sample window structure wall surface to a unit grid area of the sample window structure wall surface; calculating the water vapor mass source term according to the droplet height on the sample window structure wall surface, the spherical cap area, the volume change at each iteration and the droplet number in the unit grid area when the volume change at each iteration is less than or equal to the droplet volume and the contact angle is less than the wetting angle and greater than a critical angle, wherein the critical angle is a minimum angle in a droplet evaporation process, and the wetting angle is an angle between a tangent of the droplet and the sample window structure wall surface; When the volume change at each iteration is less than the droplet volume and the contact angle is equal to the critical angle, the wetting ratio is iterated according to the height of the droplet on the wall surface of the sample window structure, the spherical cap area and the volume change at each iteration, and the water vapor mass source term is calculated according to the iterated wetting ratio and the volume change at each iteration.

9. A window heating optimization device, characterized by Comprise: An acquisition module, configured to acquire a first heating parameter for controlling operation of the heating device, and acquire a droplet mass transfer parameter when a droplet on a wall surface of the window evaporates and water vapor condenses in a structure of the window after the window is heated by the heating device; A first simulation module, configured to simulate the droplet on the window to obtain a droplet simulation parameter; A second simulation module, configured to input the parameter of the window and the droplet simulation parameter into a droplet evaporation and condensation model to obtain a droplet heating simulation result output by the droplet evaporation and condensation model; A window heating simulation module, configured to input the droplet heating simulation result into a window heating simulation model to obtain a window heating simulation result output by the window heating simulation model, wherein the window heating simulation model is constructed according to the structure and parameter of the window and the structure and parameter of the heating device; An optimization module, configured to optimize the structure and / or parameter of the heating device and / or the first heating parameter according to the window heating simulation result.

10. An electronic device comprising: A processor, a communication interface, a memory and a communication bus, the processor, the memory and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the corresponding operation of the window heating optimization method in any one of claims 1-8.

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