Drawing method based on AI model, computer equipment and medium

Abstract

The invention provides a drawing method based on an AI model, computer equipment and a medium. The drawing method based on the AI model comprises the steps that indoor design initial data to be drawn are obtained; acquiring historical learning information, historical examination information and historical work browsing information of the current user; according to the historical learning information, the historical examination information and the historical work browsing information, prompting words needed by the current user are predicted, and a plurality of to-be-selected prompting words are obtained; outputting a plurality of to-be-selected cue words, so that the current user selects a plurality of target cue words from the plurality of to-be-selected cue words; and inputting the plurality of target prompt words and the interior design initial data into a preset AI drawing model to receive an interior design effect picture output by the AI drawing model. The drawing efficiency of the AI model can be improved.

Description

Technical Field

[0001] The present application relates to the field of AI model drawing technology, and specifically to a drawing method, computer equipment and medium based on an AI model. Background Art

[0002] In industries such as architectural design, AI (Artificial Intelligence) models can be provided for drawing interior design-related content. For example, AI models can be used to render the decoration effects of rough drafts, or to render interior design line drafts to obtain interior design renderings.

[0003] When using AI models to draw interior design-related content, users usually need to manually enter prompt words to make the AI ​​model draw the desired effect. For example, a user can enter the prompt word "cream style" to make the AI ​​model draw an interior design effect with a cream style.

[0004] However, this method of manually inputting prompt words is very inconvenient, and the prompt words require users to think repeatedly, summarize and refine them, which greatly reduces the drawing efficiency of the AI ​​model. Summary of the Invention

[0005] The embodiments of the present application provide a drawing method, computer device and medium based on an AI model, aiming to improve the drawing efficiency of the AI ​​model.

[0006] In a first aspect, an embodiment of the present application provides a drawing method based on an AI model, the drawing method based on the AI ​​model comprising: Obtain the initial data of the interior design to be drawn; Obtain the current user's historical learning information, historical examination information, and historical work browsing information; predicting prompt words required by the current user based on the historical learning information, the historical examination information, and the historical work browsing information to obtain a plurality of prompt words to be selected; Outputting a plurality of the candidate prompt words, so that the current user can select a plurality of target prompt words from the plurality of the candidate prompt words; Input the plurality of target prompt words and the initial interior design data into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0007] In some embodiments, the outputting of the plurality of prompt words to be selected includes: Obtaining the user demand degree of each of the prompt words to be selected; According to the user demand, the plurality of prompt words to be selected are sorted from largest to smallest to obtain a first order; According to the first order, a plurality of prompt words to be selected are output.

[0008] In some embodiments, the historical learning information includes the first knowledge point learned by the current user in the historical period; and / or The historical examination information includes a second knowledge point of the current user in the examination during the historical period, and a third knowledge point of the second knowledge point to which the current user answered incorrectly; and / or The historical work browsing information includes a plurality of preset prompt words associated with preset interior design works browsed by the current user in a historical period; The user demand degree of the candidate prompt word can be determined by the following steps: Determining the number of occurrences of the candidate prompt word in each of the plurality of first knowledge points, the plurality of second knowledge points, the plurality of second knowledge points, and the plurality of preset prompt words; The weighted sum of the occurrence times is performed to obtain the user demand degree of the prompt word to be selected.

[0009] In some embodiments, the weight when performing the weighted summation of the number of occurrences is determined by: Determining a first historical moment of a first knowledge point most recently learned in the historical learning information; Determine a second historical moment of a second knowledge point of a most recent exam in the historical exam information; Determining a third historical moment of a third knowledge point in the historical test information where a third knowledge point was most recently answered incorrectly; Determining a fourth historical moment of the most recently browsed preset interior design work in the historical work browsing information; Based on the time sequence between the first historical moment, the second historical moment, the third historical moment and the fourth historical moment, different weights are set for the number of times the selected prompt word appears in multiple first knowledge points, multiple second knowledge points, multiple second knowledge points and multiple preset prompt words.

[0010] In some embodiments, the prompt word required by the current user is predicted based on the historical learning information, the historical examination information, and the historical work browsing information to obtain multiple prompt words to be selected, including: Determining a first keyword in a plurality of the first knowledge points, a second keyword in a plurality of the second knowledge points, and a third keyword in a plurality of the third knowledge points; taking the first keyword, the second keyword, the third keyword, and a set of multiple preset prompt words as a prompt word set; Based on the user demand degree of each prompt word in the prompt word set, a plurality of prompt words to be selected are determined in the prompt word set.

[0011] In some embodiments, determining a plurality of prompt words to be selected from the prompt word set based on the user demand for each prompt word in the prompt word set includes: sorting all prompt words in the prompt word set from largest to smallest according to the user demand degree of the prompt words in the prompt word set to obtain a second order; In the second sequence, a preset number of prompt words ranked at the top are determined and used as the plurality of prompt words to be selected.

[0012] In some embodiments, the plurality of prompt words to be selected also include a plurality of fourth prompt words, and after obtaining the initial data of the interior design to be drawn, the method further includes: determining a plurality of interior design elements in the initial interior design data; Determining a target interior design work having at least one interior design element in the initial interior design data from among a plurality of preset interior design works; A plurality of preset prompt words associated with the target interior design work are used as the plurality of fourth prompt words.

[0013] In some embodiments, determining a plurality of interior design elements in the initial interior design data includes: Obtaining a target neural network model for recognizing interior design elements; The initial interior design data is input into the target neural network model to receive multiple interior design elements in the initial interior design data output by the target neural network model.

[0014] In a second aspect, an embodiment of the present application provides a drawing device based on an AI model, wherein the drawing device based on the AI ​​model includes: The first acquisition module is used to acquire the initial data of the interior design to be drawn; The second acquisition module is used to obtain the current user's historical learning information, historical examination information and historical work browsing information; a prediction module, configured to predict the prompt words required by the current user based on the historical learning information, the historical examination information, and the historical work browsing information, and obtain a plurality of prompt words to be selected; A selection module is configured to output a plurality of prompt words to be selected, so that the current user can select a plurality of target prompt words from the plurality of prompt words to be selected; The drawing module is used to input the plurality of target prompt words and the initial interior design data into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0015] In a third aspect, an embodiment of the present application provides a computer device comprising a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement the AI ​​model-based drawing method as described in any one of the above items.

[0016] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program is configured to be executed by a processor to implement the AI ​​model-based drawing method as described in any one of the above items.

[0017] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program or instructions, which are executed by a processor to implement the AI ​​model-based drawing method as described in any of the above items.

[0018] Beneficial effects of the embodiments of the present application: In an embodiment of the present application, multiple target prompt words are predicted through historical learning information, historical examination information and historical work browsing information, and then input into a preset AI drawing model based on the initial data of the interior design to be drawn and the multiple target prompt words to obtain the corresponding interior design renderings. There is no need for the user to manually input the prompt words. The user can complete the drawing of the AI ​​model only by selecting the prompt words, thereby improving the drawing efficiency of the AI ​​model. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flowchart of an embodiment of the AI ​​model-based drawing method provided in an embodiment of the present application; Figure 2 This is a flow chart of another embodiment of the AI ​​model-based drawing method provided in an embodiment of the present application; Figure 3 This is a flow chart of another embodiment of the AI ​​model-based drawing method provided in the embodiments of the present application; Figure 4This is a flow chart of another embodiment of the AI ​​model-based drawing method provided in the embodiments of the present application; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0022] In the description of this application, "plurality" means two or more, unless otherwise specifically defined. Furthermore, in the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features being described. Thus, features defined as "first" or "second" may explicitly or implicitly include one or more of the described features.

[0023] In the first aspect, the embodiment of the present application provides a drawing method based on an AI model. Specifically, referring to Figure 1 , Figure 1 The figure is a flow chart of an embodiment of a drawing method based on an AI model. Figure 1 In the embodiment of the present invention, the drawing method based on the AI ​​model may include: 101. Obtain the initial data of the interior design to be drawn.

[0024] In the embodiment of the present application, the initial interior design data to be drawn refers to the original interior design data that needs to be drawn to obtain interior design renderings, such as rough decoration design drawings, interior design line drafts, etc.

[0025] 102. Obtain the current user's historical learning information, historical examination information, and historical work browsing information.

[0026] In the embodiments of the present application, the current user refers to the user currently logged into a platform that provides an AI model drawing function. This platform is connected to a platform for learning, practicing, and assessing content such as interior design, and a platform that provides a browsing function for interior design works, so as to obtain the current user's historical learning information and historical examination information from the platform for learning, practicing, and assessing content such as interior design. The current user's historical work browsing information can be obtained from the platform that provides the browsing function for interior design works. The platform that provides the browsing function for interior design works can be provided with multiple preset interior design works for users to learn and appreciate.

[0027] In the embodiments of the present application, the current user's historical learning information refers to the current user's historical learning records for content such as interior design. The current user's historical examination information refers to the current user's historical examination records for content such as interior design. The current user's historical work browsing information refers to the current user's browsing records for preset interior design works.

[0028] 103. Predict the prompt words needed by the current user based on the historical learning information, historical examination information, and historical work browsing information, and obtain multiple prompt words to be selected.

[0029] In an embodiment of the present application, historical learning information, historical examination information and historical work browsing information represent the current user's historical needs for content such as interior design. Therefore, based on the historical learning information, historical examination information and historical work browsing information, the prompt words required by the current user can be predicted, making the prediction of the prompt words required by the current user more accurate.

[0030] It is understandable that users may have doubts or misunderstandings during the process of learning history, taking history exams, and browsing historical works, and therefore there will be a need to further understand and consolidate relevant content. Therefore, based on historical learning information, historical exam information, and historical work browsing information, the prompt words required by the current user are predicted, so that the predicted multiple prompt words to be selected can match the user's needs.

[0031] 104. Output multiple prompt words to be selected, so that the current user can select multiple target prompt words from the multiple prompt words to be selected.

[0032] In an embodiment of the present application, multiple prompt words to be selected can be output to the terminal where the current user is located, so that the current user can select multiple target prompt words from the multiple prompt words to be selected. In this way, the current user does not need to manually input the prompt words, nor does he need to repeatedly think, summarize and refine the prompt words. By selecting from the multiple prompt words to be selected, multiple target prompt words can be determined.

[0033] 105. Input multiple target prompt words and initial interior design data into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0034] In the embodiments of the present application, a preset AI (Artificial Intelligence) drawing model provides an AI drawing function that can be used to draw according to multiple target prompt words and initial interior design data input to obtain corresponding interior design renderings. The AI ​​drawing function of the AI ​​drawing model can be obtained through prior model training and is not limited here.

[0035] It can be seen that in the above-mentioned embodiment of the present application, multiple target prompt words are predicted through historical learning information, historical examination information and historical work browsing information, and then input into the preset AI drawing model based on the initial data of the interior design to be drawn and the multiple target prompt words to obtain the corresponding interior design renderings. There is no need for the user to manually input the prompt words. The user can complete the drawing of the AI ​​model only by selecting the prompt words, thereby improving the drawing efficiency of the AI ​​model.

[0036] In some embodiments of the present application, Figure 2 As shown, in Figure 1 Based on the embodiment shown, multiple prompt words to be selected are output, which may include: 201. Obtain the user demand degree of each prompt word to be selected.

[0037] In the embodiment of the present application, each prompt word to be selected has a corresponding user demand degree, which represents the degree of matching between the prompt word to be selected and the user demand.

[0038] In some embodiments of the present application, a method for calculating the user demand degree is described. Specifically, the historical learning information may include the first knowledge point that the current user learned in the historical period; and / or, the historical examination information may include the second knowledge point of the current user in the examination in the historical period, and the third knowledge point that the current user answered incorrectly in the second knowledge point; and / or the historical work browsing information may include multiple preset prompt words associated with the preset interior design works browsed by the current user in the historical period, wherein the multiple preset prompt words associated with the preset interior design works can be pre-marked based on actual conditions. It can be understood that the first knowledge point, the second knowledge point, and the third knowledge point can all be knowledge points in the interior design content, for example, and may include wall stone dry hanging construction technology, wall wood veneer construction technology, door and window decoration engineering construction, etc.

[0039] Accordingly, the user demand degree of the prompt word to be selected can be determined by the following steps: determining the number of times the prompt word to be selected appears in multiple first knowledge points, multiple second knowledge points, multiple second knowledge points and multiple preset prompt words respectively; performing weighted summation on the number of appearances to obtain the user demand degree of the prompt word to be selected, so that the determined user demand degree is more accurate.

[0040] In some embodiments of the present application, the weight when performing weighted summation of the number of occurrences can be determined in the following manner: determining the first historical moment of the first knowledge point most recently learned in the historical learning information; determining the second historical moment of the second knowledge point most recently tested in the historical examination information; determining the third historical moment of the third knowledge point most recently answered incorrectly in the historical examination information; determining the fourth historical moment of the preset interior design work most recently browsed in the historical work browsing information; based on the time sequence between the first historical moment, the second historical moment, the third historical moment and the fourth historical moment, setting different weights for the number of occurrences of the selected prompt word in multiple first knowledge points, multiple second knowledge points, multiple second knowledge points and multiple preset prompt words, respectively.

[0041] Among them, since each first knowledge point in the historical learning information corresponds to a corresponding user learning moment, the user learning moment closest to the current time point can be determined among multiple user learning moments, and used as the first historical moment of the first knowledge point most recently learned in the historical learning information.

[0042] Since each second knowledge point in the historical examination information corresponds to a corresponding user examination time, the user examination time closest to the current time point can be determined among the user examination times of multiple second knowledge points, and used as the second historical moment of the second knowledge point of the most recent examination in the historical examination information.

[0043] Since each third knowledge point in the historical examination information corresponds to a corresponding user examination time, the user examination time closest to the current time point can be determined among the user examination times of multiple third knowledge points, and used as the third historical moment of the third knowledge point that was most recently answered incorrectly in the historical examination information.

[0044] Since each preset interior design work in the historical work browsing information can correspond to a corresponding user browsing time, the user browsing time closest to the current time point can be determined from multiple user browsing times and used as the fourth historical moment of the most recently browsed preset interior design work in the historical work browsing information.

[0045] In the chronological order of the first, second, third, and fourth historical moments, the closer the candidate prompt word is to the current time point, the greater the weight of its appearance in the corresponding keywords. Taking the chronological order of the first, second, third, and fourth historical moments as an example, the weight of the number of appearances of the candidate prompt word in the first keywords, the weight of the number of appearances of the candidate prompt word in the second keywords, the weight of the number of appearances of the candidate prompt word in the third keywords, and the weight of the number of appearances of the candidate prompt word in the preset keywords decrease in that order. In this way, through weighted summation, a more accurate user demand can be obtained.

[0046] 202. Sort the plurality of prompt words to be selected from largest to smallest according to the degree of user demand to obtain a first order.

[0047] 203. Output a plurality of prompt words to be selected according to the first order.

[0048] In an embodiment of the present application, multiple prompt words to be selected are output in a first order. For example, a prompt word sequence formed by multiple prompt words to be selected can be displayed, and the multiple prompt words to be selected in the prompt word sequence are sorted in sequence according to the first order. In this way, the user can first see the prompt words to be selected that are more in demand by the user, and thus select the desired target prompt word more quickly.

[0049] In some embodiments of the present application, Figure 3 As shown, in Figures 1 to 2 Based on any of the embodiments shown, the prompt words required by the current user are predicted based on historical learning information, historical examination information, and historical work browsing information, and multiple prompt words to be selected are obtained, which may include: 301. Determine a first keyword in a plurality of first knowledge points, a second keyword in a plurality of second knowledge points, and a third keyword in a plurality of third knowledge points.

[0050] In the embodiments of the present application, the first, second, and third knowledge points all include corresponding keywords. For example, the keywords may include at least one of interior design styles and interior design elements. Interior design styles may include, for example, modern, cream, Nordic, and log styles. Interior design elements may include, for example, ceilings, doors and windows, tables and chairs, wall decorations, floor decorations, and the interior design area.

[0051] The interior design area range may be a preset area range of the indoor floor area. Of course, the interior design area range may also be a preset area range of the indoor area to be designed (including at least one of the indoor floor area, wall area, and ceiling area).

[0052] 302. Use the first keyword, the second keyword, the third keyword, and a set of multiple preset prompt words as a prompt word set.

[0053] In an embodiment of the present application, since the first keyword, the second keyword, the third keyword and multiple preset prompt words may all be prompt words required by the user, the set of the first keyword, the second keyword, the third keyword and multiple preset prompt words can be used as a prompt word set.

[0054] 303. Based on the user demand degree of each prompt word in the prompt word set, determine a plurality of prompt words to be selected in the prompt word set.

[0055] In an embodiment of the present application, a corresponding user demand level can be determined for each prompt word in the prompt word set, and then, based on the user demand level, multiple candidate prompt words can be determined from the prompt word set. For example, all prompt words in the prompt word set whose user demand level is greater than a preset demand level threshold can be selected as candidate prompt words, thereby ensuring that the candidate prompt words better meet user needs.

[0056] In some embodiments of the present application, based on the user demand for each prompt word in the prompt word set, determining multiple prompt words to be selected in the prompt word set can include: sorting all prompt words in the prompt word set from largest to smallest according to the user demand for the prompt words in the prompt word set to obtain a second order; in the second order, determining a preset number of prompt words that are ranked high and use them as the multiple prompt words to be selected, thereby avoiding the user having to spend too much time searching for the required prompt words due to an excessive number of prompt words to be selected, and improving the overall accuracy of the recommended prompt words to be selected.

[0057] In some embodiments of the present application, the user demand for each prompt word in the prompt word set can be determined by: for each prompt word in the prompt word set, determining the number of times the prompt word appears in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset prompt words; and determining the user demand for the prompt word in the prompt word set based on the number of appearances. It can be seen that based on the number of appearances, the user demand for the prompt word that better matches the user's needs can be determined.

[0058] In some embodiments of the present application, determining the user demand for a prompt word in a prompt word set based on the number of occurrences may include: performing a weighted summation of the number of occurrences of the prompt word in multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset prompt words in the prompt word set to obtain a comprehensive number of occurrences; and determining the user demand for the prompt word in the prompt word set based on the comprehensive number of occurrences, for example, directly using the comprehensive number of occurrences as the user demand for the prompt word. It can be seen that the weighted summation can provide a more accurate user demand, thereby ensuring that the selected prompt word better meets user needs.

[0059] The weight of the number of times the prompt word appears in the multiple first keywords, multiple second keywords, multiple third keywords, and multiple preset prompt words can be set based on the chronological order of the first historical moment, the second historical moment, the third historical moment, and the fourth historical moment. Taking the chronological order of the first historical moment, the second historical moment, the third historical moment, and the fourth historical moment as an example, the weight of the number of times the prompt word appears in the multiple first keywords, the weight of the number of times the prompt word appears in the multiple second keywords, the weight of the number of times the prompt word appears in the multiple third keywords, and the weight of the number of times the prompt word appears in the multiple preset prompt words will decrease in that order.

[0060] In some embodiments of the present application, Figure 4 As shown, in Figures 1 to 3 Based on any of the embodiments shown, the plurality of prompt words to be selected further include a plurality of fourth prompt words. After obtaining the initial data of the interior design to be drawn, the following may also be included: 401. Determine multiple interior design elements in the initial interior design data.

[0061] In an embodiment of the present application, the multiple interior design elements in the initial interior design data may include, for example, suspended ceilings, doors and windows, tables and chairs, wall decorations, floor decorations, interior design area range, etc.

[0062] The indoor architectural design area range may be a preset area range within which the indoor floor area falls. Alternatively, the architectural design area may be a preset area range within which the indoor area to be designed (including at least one of the indoor floor area, wall area, and ceiling area) falls, thereby ensuring that the determined target interior design work is more closely aligned with the initial interior design data.

[0063] In some embodiments of the present application, interior design elements in the initial interior design data can be identified using a neural network model. Specifically, determining multiple interior design elements in the initial interior design data can include: obtaining a target neural network model for identifying interior design elements; inputting the initial interior design data into the target neural network model to receive the multiple interior design elements in the initial interior design data output by the target neural network model. The target neural network model for identifying interior design elements can be obtained through a corresponding model training data set and model training, which is not limited here.

[0064] 402. Determine a target interior design work having at least one interior design element in the initial interior design data from among multiple preset interior design works.

[0065] In an embodiment of the present application, multiple preset interior design works can be obtained from a platform that provides an interior design work browsing function. Each preset interior design work is pre-labeled with its interior design elements. Therefore, by comparing the interior design elements, at least one target interior design work that has at least one interior design element in the initial interior design data can be identified from the multiple preset interior design works.

[0066] 403. Use multiple preset prompt words associated with the target interior design work as multiple fourth prompt words.

[0067] In the embodiments of the present application, since each preset interior design work is pre-labeled with its corresponding preset prompt words, once the target interior design work is determined, the preset prompt words associated with the target interior design work can be directly used as multiple fourth prompt words and as at least a portion of the multiple candidate prompt words. This provides users with a richer selection of prompt words that better meet their needs, eliminating the need for users to manually enter prompt words and thus improving the AI ​​model's drawing efficiency.

[0068] In a second aspect, based on the AI ​​model-based drawing method of the above embodiment, embodiments of the present application provide an AI model-based drawing device, which is configured to perform the steps of any of the above AI model-based drawing methods. Specifically, the AI ​​model-based drawing device may include: The first acquisition module is used to acquire the initial data of the interior design to be drawn; The second acquisition module is used to obtain the current user's historical learning information, historical examination information and historical work browsing information; A prediction module is used to predict the prompt words required by the current user based on historical learning information, historical examination information, and historical work browsing information, and obtain multiple prompt words to be selected; A selection module is used to output multiple candidate prompt words so that the current user can select multiple target prompt words from the multiple candidate prompt words; The drawing module is used to input multiple target prompt words and initial interior design data into a preset AI drawing model to receive the interior design renderings output by the AI ​​drawing model.

[0069] In a third aspect, embodiments of the present application provide a computer device that integrates any of the AI ​​model-based drawing devices provided in the embodiments of the present application. The computer device includes a processor and a memory, wherein the memory stores a computer program configured to be executed by the processor to implement the AI ​​model-based drawing method described in any of the above embodiments, for example: Obtaining initial data of the interior design to be drawn; obtaining the current user's historical learning information, historical examination information, and historical work browsing information; predicting the prompt words required by the current user based on the historical learning information, historical examination information, and historical work browsing information to obtain multiple prompt words to be selected; outputting multiple prompt words to be selected so that the current user can select multiple target prompt words from the multiple prompt words to be selected; inputting the multiple target prompt words and the initial data of the interior design into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0070] In a fourth aspect, an embodiment of the present application provides a computer device that integrates any one of the AI ​​model-based drawing devices provided in the embodiment of the present application. Figure 5 , which shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically: The computer device may include one or more processing core processors 501, one or more computer readable storage medium storage units 502, a power supply 503 and an input unit 504. Those skilled in the art will understand that Figure 5 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently. Processor 501 is the control center of the computer device. It connects the various components of the computer device using various interfaces and circuits. By running or executing software programs and / or modules stored in storage unit 502 and accessing data stored in storage unit 502, it performs various functions of the computer device and processes data, thereby providing overall monitoring of the computer device. Optionally, processor 501 may include one or more processing cores. Preferably, processor 501 may integrate an application processor and a modem processor, with the application processor primarily processing the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 501.

[0071] The storage unit 502 can be used to store software programs and modules. The processor 501 executes various functional applications and data processing by running the software programs and modules stored in the storage unit 502. The storage unit 502 may primarily include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as sound playback or image playback); the data storage area may store data generated based on the use of the computer device. Furthermore, the storage unit 502 may include high-speed random access memory and non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state memory device. Accordingly, the storage unit 502 may also include a memory controller to provide the processor 501 with access to the storage unit 502.

[0072] The computer device also includes a power supply 503 for supplying power to various components. Preferably, the power supply 503 can be logically connected to the processor 501 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 503 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0073] The computer device may further include an input unit 504 , which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0074] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail here. Specifically, in the embodiment of the present application, the processor 501 in the computer device will load the executable files corresponding to the processes of one or more application programs into the storage unit 502 according to the following instructions, and the processor 501 will run the application programs stored in the storage unit 502 to implement various functions, such as: Obtaining initial data of the interior design to be drawn; obtaining the current user's historical learning information, historical examination information, and historical work browsing information; predicting the prompt words required by the current user based on the historical learning information, historical examination information, and historical work browsing information to obtain multiple prompt words to be selected; outputting multiple prompt words to be selected so that the current user can select multiple target prompt words from the multiple prompt words to be selected; inputting the multiple target prompt words and the initial data of the interior design into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0075] In a fifth aspect, embodiments of the present application provide a computer-readable storage medium, which may include: a read-only memory (ROM), a random access memory (RAM), a disk, or an optical disk. The computer-readable storage medium stores a computer program, which is configured to be executed by a processor to implement the AI ​​model-based drawing method described in any of the above items, for example: Obtaining initial data of the interior design to be drawn; obtaining the current user's historical learning information, historical examination information, and historical work browsing information; predicting the prompt words required by the current user based on the historical learning information, historical examination information, and historical work browsing information to obtain multiple prompt words to be selected; outputting multiple prompt words to be selected so that the current user can select multiple target prompt words from the multiple prompt words to be selected; inputting the multiple target prompt words and the initial data of the interior design into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0076] In a sixth aspect, embodiments of the present application provide a computer program product or computer program, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to execute to implement any of the above AI model-based drawing methods, for example: Obtaining initial data of the interior design to be drawn; obtaining the current user's historical learning information, historical examination information, and historical work browsing information; predicting the prompt words required by the current user based on the historical learning information, historical examination information, and historical work browsing information to obtain multiple prompt words to be selected; outputting multiple prompt words to be selected so that the current user can select multiple target prompt words from the multiple prompt words to be selected; inputting the multiple target prompt words and the initial data of the interior design into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

[0077] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, based on the ideas of the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A drawing method based on an AI model, characterized in that: The AI ​​model-based drawing method includes: Obtain the initial data of the interior design to be drawn; Obtain the current user's historical learning information, historical examination information, and historical work browsing information; predicting prompt words required by the current user based on the historical learning information, the historical examination information, and the historical work browsing information to obtain a plurality of prompt words to be selected; Outputting a plurality of the candidate prompt words, so that the current user can select a plurality of target prompt words from the plurality of the candidate prompt words; Input the plurality of target prompt words and the initial interior design data into a preset AI drawing model to receive the interior design rendering output by the AI ​​drawing model.

2. The drawing method based on the AI ​​model according to claim 1, characterized in that: The outputting of the plurality of prompt words to be selected includes: Obtaining the user demand degree of each of the prompt words to be selected; According to the user demand, the plurality of prompt words to be selected are sorted from largest to smallest to obtain a first order; According to the first order, a plurality of prompt words to be selected are output.

3. The drawing method based on the AI ​​model according to claim 2, characterized in that: The historical learning information includes the first knowledge point learned by the current user in the historical period; and / or The historical examination information includes a second knowledge point in the examination of the current user during the historical period, and a third knowledge point in the second knowledge point to which the current user answered incorrectly; and / or The historical work browsing information includes a plurality of preset prompt words associated with preset interior design works browsed by the current user in a historical period; The user demand degree of the candidate prompt word can be determined by the following steps: Determining the number of occurrences of the candidate prompt word in each of the plurality of first knowledge points, the plurality of second knowledge points, the plurality of second knowledge points, and the plurality of preset prompt words; The weighted sum of the occurrence times is performed to obtain the user demand degree of the prompt word to be selected.

4. The drawing method based on the AI ​​model according to claim 3, characterized in that: The weight when performing weighted summation on the number of occurrences is determined by: Determining a first historical moment of a first knowledge point most recently learned in the historical learning information; Determine a second historical moment of a second knowledge point of a most recent exam in the historical exam information; Determining a third historical moment of a third knowledge point in the historical test information where a third knowledge point was most recently answered incorrectly; Determining a fourth historical moment of the most recently browsed preset interior design work in the historical work browsing information; Based on the time sequence between the first historical moment, the second historical moment, the third historical moment and the fourth historical moment, different weights are set for the number of times the selected prompt word appears in multiple first knowledge points, multiple second knowledge points, multiple second knowledge points and multiple preset prompt words.

5. The drawing method based on the AI ​​model according to claim 3, characterized in that: The method predicts the prompt words required by the current user based on the historical learning information, the historical examination information, and the historical work browsing information to obtain multiple prompt words to be selected, including: Determining a first keyword in a plurality of the first knowledge points, a second keyword in a plurality of the second knowledge points, and a third keyword in a plurality of the third knowledge points; taking the first keyword, the second keyword, the third keyword, and a set of multiple preset prompt words as a prompt word set; Based on the user demand degree of each prompt word in the prompt word set, a plurality of prompt words to be selected are determined in the prompt word set.

6. The drawing method based on the AI ​​model according to claim 5, characterized in that: The step of determining a plurality of prompt words to be selected from the prompt word set based on the user demand degree of each prompt word in the prompt word set includes: sorting all prompt words in the prompt word set from largest to smallest according to the user demand degree of the prompt words in the prompt word set to obtain a second order; In the second sequence, a preset number of prompt words ranked at the top are determined and used as the plurality of prompt words to be selected.

7. The drawing method based on the AI ​​model according to claim 1, wherein: The plurality of prompt words to be selected also include a plurality of fourth prompt words. After obtaining the initial data of the interior design to be drawn, the method further includes: determining a plurality of interior design elements in the initial interior design data; Determining a target interior design work having at least one interior design element in the initial interior design data from among a plurality of preset interior design works; A plurality of preset prompt words associated with the target interior design work are used as the plurality of fourth prompt words.

8. The drawing method based on the AI ​​model according to claim 7, characterized in that: Determine multiple interior design elements in the initial interior design data, including: Obtaining a target neural network model for recognizing interior design elements; The initial interior design data is input into the target neural network model to receive multiple interior design elements in the initial interior design data output by the target neural network model.

9. A computer device, characterized in that: The computer device includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor to implement the AI ​​model-based drawing method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is configured to be executed by a processor to implement the AI ​​model-based drawing method according to any one of claims 1 to 8.

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