Information recommendation method and device, equipment, storage medium and program product

Abstract

The invention relates to an information recommendation method and device, equipment, a storage medium and a program product. According to one embodiment of the invention, the method comprises the following steps: in response to an information recommendation condition meeting a user at present, obtaining a target copywriting template from a pre-constructed recommended copywriting database, the target copywriting template being presented in a natural language form; pre-determined target recommendation content is filled in the target copywriting template to obtain target recommendation information, the target recommendation content is determined based on equipment operation habits of the user in a historical time period, and the target recommendation content comprises at least one of equipment identification, a room where equipment is located and equipment operation; and pushing the target recommendation information to the user. According to the information recommendation method and device, the user can quickly and clearly understand the equipment control content recommended based on the previous operation habits of the user, so that whether recommendation is accepted or not is better determined based on own requirements, the intelligence and individuation level of information recommendation can be improved, and the user experience is improved.

Description

Technical Field

[0001] This disclosure relates to the field of smart home control technology, and in particular to an information recommendation method, apparatus, device, storage medium, and program product. Background Technology

[0002] With the continuous development and popularization of smart home control technology, more and more families are adopting smart home devices to improve their quality of life. In this context, smart home control systems have emerged. Among related technologies, the system can comprehensively utilize four sensing capabilities—environment, vision, hearing, and behavior—to learn the user's device operating habits and preferences, thereby controlling the working mode of each device and / or the collaborative mode between multiple devices, ultimately providing users with a more intelligent and personalized smart ecosystem experience. For example, when a user plays music at home, speakers can be automatically connected; when a user answers a phone call at home, the TV volume can be automatically adjusted, and the TV volume can be restored after the user hangs up the phone.

[0003] To meet users' actual needs, after learning users' device operating habits and preferences, the system can make recommendations to users based on the learned information (such as operable operations in specific scenarios), and then implement control of the relevant devices after user confirmation. However, the methods of information recommendation in related technologies are mostly rigid and inflexible, making it difficult for users to quickly and clearly understand the recommended information, which in turn affects the user experience. Summary of the Invention

[0004] To overcome the problems existing in the related technologies, the present disclosure provides an information recommendation method, apparatus, device, storage medium, and program product to solve the defects in the related technologies.

[0005] According to a first aspect of the present disclosure, an information recommendation method is provided, the method comprising:

[0006] In response to the current information recommendation conditions that meet the user's requirements, a target copy template is obtained from a pre-built recommendation copy database, and the target copy template is presented in natural language form;

[0007] The target recommendation content is filled into the target copy template to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits in a historical time period. The target recommendation content includes at least one of the following: device identification, room where the device is located, and device operation.

[0008] The target recommendation information is pushed to the user.

[0009] In some embodiments, the method further includes:

[0010] In response to receiving a request from the user to view recommended information, determine that the current information recommendation conditions are met.

[0011] In some embodiments, obtaining the target copy template from a pre-built recommended copy database includes:

[0012] Determine the current recommended scenario for the user;

[0013] Search the recommendation copy database for a recommendation copy template corresponding to the current recommendation scenario;

[0014] In response to finding the recommended copywriting template, it is used as the target copywriting template.

[0015] In some embodiments, determining the user's current recommended scenario includes:

[0016] Detect the current display interface of the user's terminal device;

[0017] The recommended scene corresponding to the current display interface is determined based on a preset correspondence, and is used as the current recommended scene.

[0018] In some embodiments, searching the recommendation copy template corresponding to the current recommendation scenario in the recommendation copy database includes:

[0019] Query the language type that the user has pre-set for displaying the target recommendation information;

[0020] Search the recommended copywriting database for a copywriting template of the language type that corresponds to the current recommended scenario.

[0021] In some embodiments, the method further includes pre-determining the target recommended content based on the following:

[0022] Obtain the user's device operation habits during a preset time period prior to the current moment;

[0023] At least one piece of equipment operation information is determined based on the aforementioned equipment operation habits;

[0024] The target recommended content is extracted from the at least one piece of device operation information.

[0025] In some embodiments, the method further includes pre-constructing the recommended copy database based on the following:

[0026] Based on the at least one piece of device operation information and the preset artificial intelligence big data model, determine recommendation copy templates for multiple preset recommendation scenarios;

[0027] The recommended copywriting templates are entered into the first database to obtain the recommended copywriting database.

[0028] In some embodiments, the step of inputting the recommended copywriting template into the first database includes:

[0029] The recommended copywriting template is converted into multiple preset language types;

[0030] The converted recommendation copy template is entered into the first database.

[0031] In some embodiments, determining recommendation text templates for multiple preset recommendation scenarios based on the at least one piece of device operation information and a preset artificial intelligence big data model includes:

[0032] Generate the target basic text based on the at least one piece of device operation information;

[0033] The target basic text and the set guiding words are input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

[0034] In some embodiments, before inputting the target basic text and the set guiding words into the preset artificial intelligence big data model, the method further includes:

[0035] Replace the variable information in the target basic text with preset text symbols. The variable information includes at least one of the following: device identifier, room where the device is located, and device operation.

[0036] In some embodiments, generating the target basic text based on the at least one piece of device operation information includes:

[0037] In the pre-built basic text database, search for the basic text corresponding to each piece of information in the at least one piece of device operation information;

[0038] Based on each of the multiple preset dimensions, the basic text corresponding to each of the at least one piece of device operation information is aggregated to obtain the aggregated text under the same preset dimension. The preset dimension includes at least one of the device identification dimension, device category dimension, and room dimension where the device is located.

[0039] By splicing together aggregated texts under different preset dimensions, the target basic text is obtained.

[0040] In some embodiments, the method further includes pre-constructing the basic copywriting database based on the following:

[0041] Determine the textual components for each type of equipment, including equipment category, equipment identification, room where the equipment is located, and at least two of the equipment operation parameters;

[0042] Generate basic copy for each device based on the aforementioned copywriting elements;

[0043] The basic text is entered into the second database to obtain the basic text database.

[0044] According to a second aspect of the present disclosure, an information recommendation apparatus is provided, the apparatus comprising:

[0045] The template acquisition module is used to retrieve a target copy template from a pre-built recommendation copy database in response to the current information recommendation conditions that meet the user's requirements. The target copy template is presented in natural language form.

[0046] The information acquisition module is used to fill the target text template with pre-determined target recommendation content to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits in a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation.

[0047] The information push module is used to push the target recommendation information to the user.

[0048] In some embodiments, the apparatus further includes:

[0049] The condition determination module is used to determine, in response to receiving a request from the user to view recommended information, that the current information recommendation conditions are met.

[0050] In some embodiments, the template acquisition module includes:

[0051] A scenario determination unit is used to determine the current recommended scenario for the user;

[0052] The template search unit is used to search the recommended copywriting database for a recommended copywriting template that corresponds to the current recommended scenario.

[0053] The template acquisition unit is used to retrieve the recommended copywriting template as the target copywriting template in response to finding it.

[0054] In some embodiments, the scene determination unit is further configured to:

[0055] Detect the current display interface of the user's terminal device;

[0056] The recommended scene corresponding to the current display interface is determined based on a preset correspondence, and is used as the current recommended scene.

[0057] In some embodiments, the template lookup unit is further configured to:

[0058] Query the language type that the user has pre-set for displaying the target recommendation information;

[0059] Search the recommended copywriting database for a copywriting template of the language type that corresponds to the current recommended scenario.

[0060] In some embodiments, the apparatus further includes a content determination module;

[0061] The content determination module includes:

[0062] The habit acquisition unit is used to acquire the user's device operation habits during a preset time period prior to the current moment;

[0063] An operation determination unit is used to determine at least one piece of equipment operation information based on the equipment operation habits.

[0064] The content determination unit is used to extract the target recommended content from the at least one piece of device operation information.

[0065] In some embodiments, the apparatus further includes a recommended library construction module;

[0066] The recommended document library construction module includes:

[0067] The recommendation template determination unit is used to determine recommendation copy templates for multiple preset recommendation scenarios based on the at least one piece of device operation information and a preset artificial intelligence big data model.

[0068] The recommended document acquisition unit is used to input the recommended document templates into the first database to obtain the recommended document database.

[0069] In some embodiments, the recommended document library acquisition unit is further configured to:

[0070] The recommended copywriting template is converted into multiple preset language types;

[0071] The converted recommendation copy template is entered into the first database.

[0072] In some embodiments, the recommendation template determining unit is further configured to:

[0073] Generate the target basic text based on the at least one piece of device operation information;

[0074] The target basic text and the set guiding words are input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

[0075] In some embodiments, the recommendation template determination unit is further configured to replace variable information in the target basic text with preset text symbols, wherein the variable information includes at least one of device identifier, room where the device is located, and device operation.

[0076] In some embodiments, the recommendation template determining unit is further configured to:

[0077] In the pre-built basic text database, search for the basic text corresponding to each piece of information in the at least one piece of device operation information;

[0078] Based on each of the multiple preset dimensions, the basic text corresponding to each of the at least one piece of device operation information is aggregated to obtain the aggregated text under the same preset dimension. The preset dimension includes at least one of the device identification dimension, device category dimension, and room dimension where the device is located.

[0079] By splicing together aggregated texts under different preset dimensions, the target basic text is obtained.

[0080] In some embodiments, the apparatus further includes a basic library construction module;

[0081] The basic library construction module includes:

[0082] An element determination unit is used to determine the textual components of each type of equipment. The textual components include equipment category, equipment identification, room where the equipment is located, and at least two of the equipment operation elements.

[0083] The copywriting generation unit is used to generate basic copywriting for each device based on the copywriting components;

[0084] The copywriting input unit is used to input the basic copywriting into the second database to obtain the basic copywriting database.

[0085] According to a third aspect of the present disclosure, an electronic device is provided, the device comprising:

[0086] Processor and memory used to store computer programs;

[0087] The processor is configured to implement any of the above-described information recommendation methods when executing the computer program.

[0088] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the information recommendation method described in any of the preceding claims.

[0089] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the information recommendation method described in any of the preceding claims.

[0090] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0091] This disclosure, in response to information recommendation conditions that currently meet the user's needs, retrieves a target text template from a pre-built recommendation text database. The target text template is presented in natural language, and pre-determined target recommendation content is filled into the template to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits over a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation. This information is then pushed to the user. This allows for the recommendation of device operation information in a user-friendly natural language format after learning device operation habits, enabling users to quickly and clearly understand the recommended information and better determine whether to accept the recommendation based on their own needs. This improves the intelligence and personalization of information recommendation and enhances the user experience.

[0092] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0093] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0094] Figure 1 This is a flowchart illustrating an information recommendation method according to an exemplary embodiment of the present disclosure;

[0095] Figure 2A This is a flowchart illustrating how to determine the current recommendation scenario for the user according to an exemplary embodiment of this disclosure;

[0096] Figure 2B This is a schematic diagram illustrating a details viewing interface according to an exemplary embodiment of the present disclosure;

[0097] Figure 2C This is a schematic diagram illustrating a recommendation details interface according to an exemplary embodiment of the present disclosure;

[0098] Figure 2D This is a schematic diagram illustrating a user interface of my device according to an exemplary embodiment of this disclosure;

[0099] Figure 3This is a flowchart illustrating how to determine the target recommended content according to an exemplary embodiment of this disclosure;

[0100] Figure 4 This is a flowchart illustrating how to construct the recommended copywriting database according to an exemplary embodiment of this disclosure;

[0101] Figure 5 This is a flowchart illustrating how to determine recommendation copy templates for various preset recommendation scenarios according to an exemplary embodiment of this disclosure;

[0102] Figure 6 This is a flowchart illustrating how to construct the basic text database according to an exemplary embodiment of this disclosure;

[0103] Figure 7 This is a flowchart illustrating an information recommendation method according to an exemplary embodiment of the present disclosure;

[0104] Figure 8A This is a block diagram illustrating an information recommendation device according to an exemplary embodiment of the present disclosure;

[0105] Figure 8B This is a block diagram illustrating yet another information recommendation device according to an exemplary embodiment of the present disclosure;

[0106] Figure 9 This is a block diagram illustrating an electronic device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0107] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0108] Figure 1 This is a flowchart illustrating an information recommendation method according to an exemplary embodiment. The method of this embodiment can be executed by an information recommendation device, which can be configured in an electronic device serving as a smart home control system (hereinafter referred to as the "system"), such as a server, workstation, mobile terminal (e.g., mobile phone, tablet computer), wearable device (e.g., glasses, watch), etc. Figure 1 As shown, the method includes the following steps S101-S103:

[0109] In step S101, in response to the current information recommendation conditions that meet the user's requirements, the target copy template is obtained from the pre-built recommendation copy database.

[0110] In this embodiment, the system can retrieve a target text template from a pre-built recommendation text database in response to information recommendation conditions that currently meet the user's requirements. This target text template is presented in natural language (i.e., language forms that humans spontaneously develop for communication in daily life, such as colloquial communication).

[0111] In some embodiments, the system can determine that the information recommendation conditions are currently met upon receiving a request to view recommendation information from the user. For example, when a user triggers the function to view recommendation information on a client app on a terminal device (such as a smartphone), the client app can send the aforementioned request to the system. The system can then determine that the user's information recommendation conditions are currently met upon receiving the request, and subsequently retrieve the target text template from a pre-built recommendation text database. It is understood that the above method of condition judgment is only for illustrative purposes and can be adjusted based on scenario requirements in actual applications. For example, the system can also determine that the information recommendation conditions are currently met upon reaching a target time period. For instance, if the system detects that a user turns on the living room light when returning home from get off work each day, it can determine that the user's information recommendation conditions are currently met when the system detects the user returning home from get off work again.

[0112] In some embodiments, when a target copy template is obtained from a pre-built recommendation copy database, the user's current recommendation scenario can be determined, and a recommendation copy template corresponding to the current recommendation scenario can be searched in the recommendation copy database. In response to finding the recommendation copy template, it can be used as the target copy template.

[0113] It is worth noting that the types of recommendation scenarios mentioned above can be set based on actual application needs, such as "scenarios where recommendations are pushed to users", "scenarios where users view recommendation details", and "scenarios where users view recommendations after they have saved them as a habit". This embodiment does not limit this.

[0114] The method for determining the user's current recommendation scenario described above can be set based on actual business needs, and this embodiment does not limit it in this regard. For example, the method for determining the user's current recommendation scenario can also be found in the following... Figure 2A The embodiments shown will not be described in detail here.

[0115] For example, corresponding copywriting templates can be pre-matched for different recommendation scenarios and entered into the recommendation copywriting database. Then, once the current recommendation scenario for the user is determined, the corresponding copywriting template can be found in the recommendation copywriting database.

[0116] In some embodiments, the specific device information in the above-mentioned copy template can also be represented by variable symbols. That is to say, the variable content in the copy template is in a default state, and can be filled in later according to the situation to be recommended to the user (e.g., the user's actual device control content). For example, a copy template in this embodiment can be: "When you come home, do you need me to turn on the light in ${room name}?". Here, "${}" is the variable symbol, and "room name" is the variable content. It is understood that the "room name (i.e., the room where the light is located)" here is only used for illustrative purposes. In actual applications, it can be adjusted according to the needs of the scenario, such as adjusting it to device identification and / or device operation, etc. This embodiment does not limit this.

[0117] Compared to the rigid and inflexible "Should I turn on the light in room ${name}?", the above copy, presented in natural language, "When you come home, do you need me to turn on the light in room ${name}?" conveys the recommended content concisely and clearly, achieving a more humanized and intelligent information delivery, thereby enhancing the user's reading experience.

[0118] In other embodiments, when searching for a recommendation template corresponding to the current recommendation scenario in the recommendation copy database, the system can first query the language type preset by the user for displaying the target recommendation information, and then search the recommendation copy database for a copy template corresponding to that language type. For example, if the user's preset language type is "Chinese," alternative copy templates corresponding to the current recommendation scenario can be searched in the recommendation copy database, such as alternative copy template 1 for Chinese language: "When you get home, do you need me to turn on the lights in ${room name}?" and alternative copy template 2 for English language: "Can I turn on the ${ROOM} lights when you get home?". Based on this, alternative copy template 1 can be selected from alternative copy template 1 and alternative copy template 2, based on the user's preset language type being "Chinese." This ensures that the target recommendation information generated based on this template conforms to the user's language usage habits.

[0119] In step S102, the predetermined target recommendation content is filled into the target copy template to obtain target recommendation information.

[0120] In this embodiment, after the system obtains the target copy template from the pre-built recommendation copy database, it can fill the target copy template with predetermined target recommendation content to obtain target recommendation information.

[0121] The target recommended content can be determined based on the user's device operation habits over a historical period. The target recommended content includes at least one of the following: device identifier, room where the device is located, and device operation.

[0122] Using the above copywriting template, "When you come home, do you need me to turn on the light in ${room name}?" as an example, assuming the target recommended content determined by the user's device operating system is "living room", then this content can be filled into the above copywriting template to obtain the target recommended information, namely, "When you come home, do you need me to turn on the light in ${living room}?"

[0123] It is worth noting that the method for determining the above-mentioned target recommendation content can refer to solutions in related technologies, and this embodiment does not limit it. In other embodiments, the method for determining the above-mentioned target recommendation content can also refer to the following... Figure 3 The embodiments shown will not be described in detail here.

[0124] In step S104, the target recommendation information is pushed to the user.

[0125] In this embodiment, after the target recommendation content is filled into the copy template and the target recommendation information is obtained, the system can push the target recommendation information to the user.

[0126] For example, the system can push the target recommendation content to a client app on the user's terminal device via the network, so that the target recommendation information is displayed on the client app's user interface. Thus, the user can quickly and clearly understand the currently recommended device control content based on the target recommendation content, and then decide whether to accept the recommendation based on their own needs (e.g., whether to save the current recommendation as a habit for the system to automatically control the relevant device to perform operations later).

[0127] As described above, the method of this embodiment, in response to information recommendation conditions that currently meet the user's needs, retrieves a target text template from a pre-built recommendation text database. The target text template is presented in natural language, and pre-determined target recommendation content is filled into the target text template to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits over a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation. The target recommendation information is then pushed to the user. This method can recommend device operation information to the user in a user-friendly natural language format after learning device operation habits, enabling the user to quickly and clearly understand the content of the recommended information and better determine whether to accept the recommendation based on their own needs. This can improve the intelligence and personalization of information recommendation and enhance the user experience.

[0128] Figure 2A This is a flowchart illustrating how to determine the current recommended scenario for a user according to an exemplary embodiment of the present disclosure; this embodiment is based on the above embodiment and takes how to determine the current recommended scenario for a user as an example for illustrative explanation.

[0129] like Figure 2A As shown, determining the user's current recommendation scenario in step S101 above may include the following steps S201-S202:

[0130] In step S201, the current display interface of the user's terminal device is detected.

[0131] In this embodiment, when the user's current recommended scenario is determined, the current display interface of the user's terminal device can be detected.

[0132] For example, when a user's terminal device sends a request to the system to view recommended information, it can carry the interface identifier of the terminal device's current display interface, so that the system can determine the current display interface of the terminal device based on the interface identifier.

[0133] It is worth noting that the method described above for determining the current display interface of the terminal device based on the interface identifier carried in the recommendation information viewing request is only for illustrative purposes. In practical applications, the system may also use other methods from related technologies to detect the current display interface of the user's terminal device, and this embodiment does not limit this.

[0134] In step S202, the recommended scene corresponding to the current display interface is determined based on a preset correspondence and is used as the current recommended scene.

[0135] In this embodiment, after detecting the current display interface of the user's terminal device, a recommended scene corresponding to the current display interface can be determined based on a preset correspondence and used as the current recommended scene.

[0136] In some embodiments, the recommended scene corresponding to the current display interface can be determined based on a pre-built correspondence, i.e., the current recommended scene. Here, the pre-built correspondence refers to the correspondence between different display interfaces of the terminal device and their corresponding recommended scenes.

[0137] It is worth noting that the above correspondence can be constructed based on actual scenario requirements, and this embodiment does not limit it.

[0138] For example, the above correspondence can be seen in Table 1 below:

[0139] Table 1

[0140]

[0141] For example, Figure 2B This is a schematic diagram illustrating a details viewing interface according to an exemplary embodiment of the present disclosure; Figure 2C This is a schematic diagram illustrating a recommendation details interface according to an exemplary embodiment of the present disclosure; Figure 2D This is a schematic diagram illustrating a user interface of my device according to an exemplary embodiment of this disclosure.

[0142] For example, when the current display interface of the terminal device is Figure 2B The "View Details" interface shown above, when queried in Table 1 above, indicates that the current recommended scenario is "the scenario of pushing recommendations to users".

[0143] As described above, this embodiment detects the current display interface of the user's terminal device and determines the recommended scenario corresponding to the current display interface based on a preset correspondence. This determines the current recommended scenario for the user, enabling accurate identification of the current recommended scenario. Subsequently, it allows for searching a pre-built recommendation template in a recommendation template database that corresponds to the recommended scenario. Upon finding the template, the system fills it with target recommended content, obtains target recommended information, and pushes the target recommended information to the user. This improves the intelligence and personalization of information recommendation, thereby enhancing the user experience.

[0144] Figure 3 This is a flowchart illustrating how to determine the target recommended content according to an exemplary embodiment of the present disclosure; this embodiment is based on the above embodiment and takes how to determine the target recommended content as an example for illustrative explanation.

[0145] like Figure 3 As shown, the information recommendation method in this embodiment may further include determining the target recommendation content based on the following steps S301-S303:

[0146] In step S301, the user's device operation habits during a preset time period prior to the current moment are obtained.

[0147] In this embodiment, the system can obtain the user's device operation habits during a preset time period prior to the current moment.

[0148] For example, the system can detect the operating status of various smart devices in a user's home, and thus obtain the user's device operation habits during a preset time period before the current moment.

[0149] The length of the aforementioned preset time period can be set based on actual application needs, such as 3 days, one week, or one month. This embodiment does not limit this.

[0150] In step S302, at least one piece of equipment operation information is determined based on the equipment operation habits.

[0151] In this embodiment, after obtaining the user's device operation habits within a preset time period before the current moment, the system can determine at least one piece of device operation information based on the device operation habits.

[0152] For example, if the system obtains the device operation habits including "turning on the living room light, the bedroom air filter, and the living room TV when returning home at night", then information can be extracted based on these operation habits to obtain the following three device operation information: (1) turn on the living room light; (2) close the bedroom curtains; (3) turn on the robot vacuum cleaner.

[0153] In step S303, the target recommended content is extracted from the at least one piece of device operation information.

[0154] In this embodiment, after determining at least one piece of device operation information based on the device operation habits, the target recommended content can be extracted from the at least one piece of device operation information.

[0155] Taking the three device operation information entries mentioned above as an example, after obtaining these three entries, each entry can be split according to its part of speech to obtain the splitting results. Then, combined with preset variable types, the target recommended content can be extracted from the splitting results, as shown in Table 2 below:

[0156] Table 2

[0157] Equipment operation information Target Recommended Content Turn on the living room lights living room Close the bedroom curtains Bedroom + Curtains Turn on the robot vacuum sweeper

[0158] It is worth noting that the types of target recommended content in Table 2 above are for illustrative purposes only. In actual applications, they can be adjusted based on scenario requirements, that is, adjusted to at least one of device identification, device operation, and the room where the device is located. This embodiment does not limit this.

[0159] As described above, this embodiment obtains the user's device operation habits within a preset time period prior to the current moment, determines at least one piece of device operation information based on the device operation habits, and then extracts the target recommended content from the at least one piece of device operation information. This allows for accurate determination of the target recommended content based on the user's past device operation habits. Consequently, in response to finding a text template, the target recommended content is filled into the text template to obtain the target recommended information and push it to the user. This improves the intelligence and personalization of information recommendation, thereby enhancing the user experience.

[0160] Figure 4 This is a flowchart illustrating how to construct the recommended copywriting database according to an exemplary embodiment of the present disclosure; this embodiment is based on the above embodiment and takes how to construct the recommended copywriting database as an example for illustrative explanation.

[0161] like Figure 4 As shown, the information recommendation method in this embodiment may further include constructing the recommendation text database based on the following steps S401-S402:

[0162] In step S401, recommendation templates for various preset recommendation scenarios are determined based on the at least one piece of device operation information and a preset artificial intelligence big data model.

[0163] In this embodiment, after obtaining at least one piece of device operation information in step S302, recommendation copy templates for various preset recommendation scenarios can be determined based on the at least one piece of device operation information and the preset artificial intelligence big model.

[0164] The aforementioned preset artificial intelligence model can be selected from relevant technologies based on actual application needs, such as Kimi or GPT-4, and this embodiment does not limit it in this regard.

[0165] In some embodiments, the method for determining recommendation copy templates under multiple preset recommendation scenarios can be found in the following. Figure 5 The embodiments shown will not be described in detail here.

[0166] In step S402, the recommended copywriting template is entered into the first database to obtain the recommended copywriting database.

[0167] In this embodiment, after determining the recommendation copy templates for multiple preset recommendation scenarios, the recommendation copy templates can be entered into the first database to obtain the recommendation copy database.

[0168] In some embodiments, when the recommended copy template is entered into the first database, the recommended copy template can first be converted into multiple preset language types, such as Chinese, English, Japanese, and Korean, and then the converted recommended copy templates in multiple language types can be entered into the first database.

[0169] In some embodiments, the recommended copywriting database can essentially be a recommended copywriting storage table. For example, this recommended copywriting storage table is shown in Table 3 below:

[0170] Table 3

[0171]

[0172] As described above, this embodiment determines recommendation templates for various preset recommendation scenarios based on at least one piece of device operation information and a preset artificial intelligence model, and inputs the recommendation templates into a first database to obtain the recommendation database. This enables the construction of a recommendation database suitable for users, and allows for the subsequent search for templates corresponding to the recommended scenarios within the database. In response to finding a template, the system fills the template with target recommendation content to obtain target recommendation information, which is then pushed to the user. This improves the intelligence and personalization of information recommendations, thereby enhancing the user experience.

[0173] Figure 5 This is a flowchart illustrating how to determine recommendation copy templates for multiple preset recommendation scenarios according to an exemplary embodiment of the present disclosure; this embodiment is based on the above embodiment and takes how to determine recommendation copy templates for multiple preset recommendation scenarios as an example for illustrative explanation.

[0174] like Figure 5 As shown, the information recommendation method in this embodiment may further include determining recommendation text templates for various preset recommendation scenarios based on the following steps S501-S502:

[0175] In step S501, a target basic text is generated based on the at least one piece of device operation information.

[0176] In some embodiments, when generating the target basic text based on the at least one piece of device operation information, the following steps S5011-S5013 may be performed:

[0177] S5011. In the pre-built basic text database, find the basic text corresponding to each piece of information in the at least one piece of device operation information.

[0178] For example, suppose we receive the following three device operation messages: (1) Turn on the living room light; (2) Close the bedroom curtains; (3) Turn on the robot vacuum cleaner. Then, based on the device category and executable action of each device operation message, we can search for the corresponding basic text in the basic text database.

[0179] In some embodiments, the basic copywriting database can essentially be a basic copywriting storage table. For example, this basic copywriting storage table is shown in Table 4 below:

[0180] Table 4

[0181]

[0182] For example, for the first device operation information above, "Turn on the living room light," the corresponding basic text "Turn on ${room name} light" can be found based on the category "light" and the action "turn on." Similarly, the corresponding basic texts "Close ${room name} curtains" and "Turn on the robot vacuum cleaner" can be found for the second and third device operation information above, respectively.

[0183] The construction method of the aforementioned basic copywriting database can be found in the following... Figure 6 The embodiments shown will not be described in detail here.

[0184] S5012. Based on each of the multiple preset dimensions, aggregate the basic text corresponding to each of the at least one piece of device operation information to obtain aggregated text under the same preset dimension.

[0185] The preset dimensions include at least one of the following: device identification dimension, device category dimension, and room dimension where the device is located.

[0186] In some embodiments, when aggregating multiple basic texts, different operations with the same device ID can be aggregated into one by using the device identifier as the dimension; then, the same operations in the same room and the same category can be aggregated into one by using the category and room as the dimensions.

[0187] For example, if the two basic copy messages are "Turn on the lights of ${room name 1}" and "Turn on the lights of ${room name 1}", then after aggregation, the aggregated copy message can be "Turn on the two lights of ${room name 1}".

[0188] For example, if the three basic texts are "Turn on the lights in room 1", "Turn on the lights in room 1", and "Turn off the TV in room 2", then after aggregation, the aggregated texts can be "Turn on the two lights in room 1" and "Turn off the TV in room 2".

[0189] S5013. Combine the aggregated texts under different preset dimensions to obtain the target basic text.

[0190] In this embodiment, when the basic text corresponding to each of the at least one piece of device operation information is aggregated based on each of the multiple preset dimensions to obtain the aggregated text under the same preset dimension, the aggregated text under different preset dimensions can be spliced ​​together to obtain the target basic text.

[0191] For example, if you get the aggregated copy as "Turn on the two lights in room 1" and "Turn off the TV in room 2", you can combine these two aggregated copy to get the target base copy "Turn on the two lights in room 1, turn off the TV in room 2".

[0192] In step S502, the target basic text and the set guiding words are input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

[0193] In this embodiment, after generating the target basic text based on the at least one piece of device operation information, the target basic text and the set guiding words can be input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

[0194] The aforementioned prompt word (or "prompt") can be used to guide the preset artificial intelligence model to generate recommendation templates for the various preset recommendation scenarios based on the target basic text.

[0195] It is worth noting that the aforementioned guiding words can be multiple sentences, and the specific content can be set based on the actual needs of the scenario. This embodiment does not limit this. For example, the aforementioned guiding words can be as follows:

[0196] "You are a copywriting optimization master, please help me complete the copywriting optimization task. Requirements:"

[0197] (1) Use colloquial expressions, but don't be too verbose; just use normal language.

[0198] (2) Some room names may be omitted, but at least one room name must be retained;

[0199] (3) The newly generated values ​​after optimization need to be marked with "「」;

[0200] (4) Appropriately add words like "will", "let", "control", and "of" to make the expression more natural;

[0201] (5) Please study the following example carefully…

[0202] As can be seen from the above process, the solution in this embodiment adopts an asynchronous strategy, that is, the template is stored first after it is generated, and then queried when it is used later. This avoids the problem of long template generation time due to the slow generation speed and unstable output of large models, and can improve the efficiency and stability of template generation.

[0203] In other embodiments, to reduce the number of times the aforementioned large model is invoked and to allow more recommended content to reuse the same copy, the variable information in the target basic copy can be replaced with preset text symbols before inputting the target basic copy and the set guiding words into the preset artificial intelligence large model (i.e., the input and output copy variables are not filled with specific content). For example, the copy variables may include at least one of the following: device identifier, room where the device is located, and executable operations.

[0204] As described above, this embodiment generates target basic text based on at least one piece of device operation information, and inputs the target basic text and set guiding words into the preset artificial intelligence big model to obtain recommendation text templates for multiple preset recommendation scenarios. This can accurately determine recommendation text templates for multiple preset recommendation scenarios, thereby improving the quality of the subsequent construction of the recommendation text database and making the text of the subsequently generated target recommendation information more concise and more in line with users' reading habits.

[0205] Figure 6 This is a flowchart illustrating how to construct the basic copywriting database according to an exemplary embodiment of the present disclosure; this embodiment is based on the above embodiment and takes how to construct the basic copywriting database as an example for illustrative explanation.

[0206] like Figure 6 As shown, the information recommendation method in this embodiment may further include constructing the basic text database based on the following steps S601-S603:

[0207] In step S601, the textual components of each device are determined.

[0208] In this embodiment, when constructing the basic copywriting database, the copywriting components for each type of device can be determined.

[0209] For example, the aforementioned copywriting elements may include at least two of the following: the type of equipment, its label, the room where the equipment is located, and how the equipment is operated. For instance, the type of equipment may refer to a category of the equipment as a product, such as lights, cameras, air conditioners, air purifiers, and door locks.

[0210] Device operation can include turning it on or off.

[0211] In step S602, basic copywriting for each device is generated based on the copywriting elements.

[0212] In this embodiment, once the text elements for each device are determined, basic text for each device can be generated based on the text elements.

[0213] For example, in the category of "lights," the basic text for turning on the light could be defined as "Turn on ${room name}${device name}"; the basic text for adjusting the brightness could be defined as "${room name}${device name} brightness adjusted to ${value}%", etc. Here, "${room name}", "${device name}", and "${value}" are the variables in the text.

[0214] In step S603, the basic text is entered into the second database to obtain the basic text database.

[0215] In this embodiment, after generating basic text for each device based on the text composition elements, the basic text can be entered into the second database to obtain the basic text database.

[0216] As described above, this embodiment determines the textual components of each device and generates basic text corresponding to each device based on the textual components. Then, the basic text is entered into the second database to obtain the basic text database. This can accurately construct the basic text database, and subsequently, based on the basic text database, accurately find each piece of information in the at least one piece of device operation information.

[0217] Figure 7 This is a flowchart illustrating an information recommendation method according to an exemplary embodiment of the present disclosure; such as Figure 7 As shown, the method in this embodiment includes the following steps S701-S705.

[0218] During the preparation phase, it is possible to build Figure 7 The "Basic Copy Database" shown in the upper right corner allows you to define basic copy for each product category and operation, and store it in the database. For example, for the "lamp" category, the "open" operation could have the basic copy "Open ${room name}${device name}", and the "adjust brightness" operation could have the basic copy "${room name}${device name} brightness adjusted to ${value}%", etc. Here, "${room name}", "${device name}", and "${value}" are the variables in the copy.

[0219] In step S701, the system can generate at least one piece of device operation information for the user based on the user's past device operation habits;

[0220] In step S702, for each piece of equipment operation information mentioned above, the system can search for the corresponding basic text from the basic text database according to the equipment category and operation.

[0221] In step S703, the basic texts of the same dimension are first aggregated, then the aggregated texts of different dimensions are spliced ​​together, and the large model is called to generate recommendation texts for different recommendation scenarios, and the texts are converted into multiple languages ​​to obtain multiple recommendation texts, which are then stored in the recommendation text database.

[0222] In this embodiment, different prompts can be used to generate recommendation copy templates for different scenarios and languages. Furthermore, in order to save the number of times the large model is called and to allow more recommendation information to reuse the same recommendation copy template, the text variables of the input and output of the large model in this embodiment can be left blank, that is, they can be replaced by the variable symbol "${}".

[0223] In step S704, the system can query the target copywriting template from the recommended copywriting database based on the language set by the user and the current display interface of the user's terminal device;

[0224] In step S705, the system can fill in the target recommendation content (such as device name, room, etc.) in the target text template, obtain the target recommendation information, and return it to the user, thereby realizing information recommendation for the user.

[0225] As described above, this embodiment can generate easily understandable, natural language-based target recommendation information for users. It generates basic text for different devices and operations through a basic text database, and optimizes and translates this basic text using a large model. This results in user-friendly, natural language-based recommendation templates, improving the intelligence and personalization of subsequent information recommendations. Considering the slow speed and unstable output of the large model in generating templates, this embodiment adopts an asynchronous strategy: after generating the recommendation template, it is first stored in the recommendation text database and then queried when needed. Furthermore, by representing text variables with preset variable symbols during the text generation process, and then replacing them with the user's specific information after the target text template is retrieved, the universality of the recommendation template can be greatly improved, reducing the number of times the large model is called, thereby improving the efficiency of information recommendation.

[0226] Figure 8A This is a block diagram illustrating an information recommendation device according to an exemplary embodiment of the present disclosure; the device of this embodiment can be configured in an electronic device that serves as a smart home control system, such as a server, workstation, mobile terminal (e.g., mobile phone, tablet computer, etc.), wearable device (e.g., glasses, watch, etc.). Figure 8A As shown, the device may include: a template acquisition module 110, an information acquisition module 120, and an information push module 130, wherein:

[0227] The template acquisition module 110 is used to retrieve a target copy template from a pre-built recommendation copy database in response to the current information recommendation conditions that meet the user's requirements. The target copy template is presented in natural language form.

[0228] The information acquisition module 120 is used to fill the target text template with predetermined target recommendation content to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits in a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation.

[0229] The information push module 130 is used to push the target recommendation information to the user.

[0230] As described above, the device in this embodiment, in response to information recommendation conditions that currently meet the user's needs, retrieves a target text template from a pre-built recommendation text database. The target text template is presented in natural language, and pre-determined target recommendation content is filled into the target text template to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits over a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation. The device then pushes the target recommendation information to the user. This allows the device to recommend device operation information to the user in a user-friendly natural language format after learning device operation habits. This enables the user to quickly and clearly understand the content of the recommended information, thereby better determining whether to accept the recommendation based on their own needs. This improves the intelligence and personalization of information recommendation and enhances the user experience.

[0231] Figure 8B This is a block diagram illustrating another information recommendation device according to an exemplary embodiment of the present disclosure. The device of this embodiment can be configured in an electronic device serving as a smart home control system, such as a server, workstation, mobile terminal (e.g., mobile phone, tablet computer), wearable device (e.g., glasses, watch), etc. The template acquisition module 210, information acquisition module 220, and information push module 230 are as described above. Figure 8A The template acquisition module 110, information acquisition module 120 and information push module 130 in the illustrated embodiment have the same functions, which will not be described in detail here.

[0232] like Figure 8B As shown, the device may further include:

[0233] The condition determination module 240 is used to determine, in response to receiving a request from the user to view recommended information, that the current information recommendation conditions are met.

[0234] In some embodiments, the template acquisition module 210 described above may include:

[0235] Scene determination unit 211 is used to determine the current recommended scene of the user;

[0236] Template search unit 212 is used to search the recommended copywriting database for a recommended copywriting template corresponding to the current recommended scenario;

[0237] The template acquisition unit 213 is used to use the recommended copywriting template as the target copywriting template in response to finding the recommended copywriting template.

[0238] In some embodiments, the scenario determination unit 211 described above can also be used for:

[0239] Detect the current display interface of the user's terminal device;

[0240] The recommended scene corresponding to the current display interface is determined based on a preset correspondence, and is used as the current recommended scene.

[0241] In some embodiments, the template lookup unit 212 described above can also be used for:

[0242] Query the language type that the user has pre-set for displaying the target recommendation information;

[0243] Search the recommended copywriting database for a copywriting template of the language type that corresponds to the current recommended scenario.

[0244] In some embodiments, the above-described apparatus may further include a content determination module 250;

[0245] The content determination module 250 may include:

[0246] The habit acquisition unit 251 is used to acquire the user's device operation habits during a preset time period before the current moment;

[0247] The operation determination unit 252 is used to determine at least one piece of device operation information based on the device operation habits.

[0248] The content determination unit 253 is used to extract the target recommended content from the at least one piece of device operation information.

[0249] In some embodiments, the above apparatus may further include a recommended library construction module 260;

[0250] The recommended document library construction module 260 may include:

[0251] The recommendation template determination unit 261 is used to determine recommendation copy templates for multiple preset recommendation scenarios based on the at least one piece of device operation information and a preset artificial intelligence big model.

[0252] The recommended document acquisition unit 262 is used to input the recommended document template into the first database to obtain the recommended document database.

[0253] In some embodiments, the above-described recommended document library acquisition unit 262 can also be used for:

[0254] The recommended copywriting template is converted into multiple preset language types;

[0255] The converted recommendation copy template is entered into the first database.

[0256] In some embodiments, the above-described recommendation template determining unit 261 is further configured to:

[0257] Generate the target basic text based on the at least one piece of device operation information;

[0258] The target basic text and the set guiding words are input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

[0259] In some embodiments, the recommendation template determination unit 261 is further configured to replace variable information in the target basic text with preset text symbols, wherein the variable information includes at least one of device identifier, room where the device is located, and device operation.

[0260] In some embodiments, the recommendation template determining unit 261 is further configured to:

[0261] In the pre-built basic text database, search for the basic text corresponding to each piece of information in the at least one piece of device operation information;

[0262] Based on each of the multiple preset dimensions, the basic text corresponding to each of the at least one piece of device operation information is aggregated to obtain the aggregated text under the same preset dimension. The preset dimension includes at least one of the device identification dimension, device category dimension, and room dimension where the device is located.

[0263] By splicing together aggregated texts under different preset dimensions, the target basic text is obtained.

[0264] In some embodiments, the above-described apparatus may further include a basic library construction module 270;

[0265] The basic document library construction module 270 may include:

[0266] The element determination unit 271 is used to determine the text composition elements of each type of equipment. The text composition elements include equipment category, equipment identification, room where the equipment is located, and at least two of the equipment operation elements.

[0267] The copywriting generation unit 272 is used to generate basic copywriting for each device based on the copywriting components.

[0268] The text input unit 273 is used to input the basic text into the second database to obtain the basic text database.

[0269] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0270] Figure 9 This is a block diagram illustrating an electronic device according to an exemplary embodiment. For example, device 900 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0271] Reference Figure 9 The device 900 may include one or more of the following components: a processing component 902, a memory 904, a power supply component 906, a multimedia component 908, an audio component 910, an input / output (I / O) interface 912, a sensor component 914, and a communication component 916.

[0272] Processing component 902 typically controls the overall operation of device 900, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the information recommendation method described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include a multimedia module to facilitate interaction between multimedia component 908 and processing component 902.

[0273] Memory 904 is configured to store various types of data to support the operation of device 900. Examples of this data include instructions for any application or method operating on device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0274] Power supply component 906 provides power to various components of device 900. Power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 900.

[0275] Multimedia component 908 includes a screen that provides an output interface between the device 900 and the user. In some embodiments, the screen may include a liquid crystal display panel and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 908 includes a front-facing camera and / or a rear-facing camera. When the device 900 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0276] Audio component 910 is configured to output and / or input audio signals. For example, audio component 910 includes a microphone (MIC) configured to receive external audio signals when device 900 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 904 or transmitted via communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.

[0277] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0278] Sensor assembly 914 includes one or more sensors for providing status assessments of various aspects of device 900. For example, sensor assembly 914 can detect the on / off state of device 900, the relative positioning of components such as the display panel and keypad of device 900, changes in the position of device 900 or a component of device 900, the presence or absence of user contact with device 900, the orientation or acceleration / deceleration of device 900, and temperature changes of device 900. Sensor assembly 914 may also include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 914 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0279] Communication component 916 is configured to facilitate wired or wireless communication between device 900 and other devices. Device 900 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G or 5G, or combinations thereof. In one exemplary embodiment, communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 916 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0280] In an exemplary embodiment, device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the information recommendation method described above.

[0281] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of device 900 to complete the aforementioned information recommendation method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0282] In an exemplary embodiment, a computer program product including instructions is also provided, which can be executed by the processor 920 of the device 900 to perform the information recommendation method described above.

[0283] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the foregoing claims.

[0284] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An information recommendation method, characterized in that, The method includes: In response to the current information recommendation conditions that meet the user's requirements, a target copy template is obtained from a pre-built recommendation copy database, and the target copy template is presented in natural language form; The target recommendation content is filled into the target copy template to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits in a historical time period. The target recommendation content includes at least one of the following: device identifier, room where the device is located, and device operation. The target recommendation information is pushed to the user.

2. The method according to claim 1, characterized in that, The method further includes: In response to receiving a request from the user to view recommended information, determine that the current information recommendation conditions are met.

3. The method according to claim 1, characterized in that, The step of obtaining the target copywriting template from a pre-built recommended copywriting database includes: Determine the current recommended scenario for the user; Search the recommendation copy database for a recommendation copy template corresponding to the current recommendation scenario; In response to finding the recommended copywriting template, it is used as the target copywriting template.

4. The method according to claim 3, characterized in that, Determining the user's current recommendation scenario includes: Detect the current display interface of the user's terminal device; The recommended scene corresponding to the current display interface is determined based on a preset correspondence, and is used as the current recommended scene.

5. The method according to claim 3, characterized in that, The step of searching the recommendation copy template corresponding to the current recommendation scenario in the recommendation copy database includes: Query the language type that the user has pre-set for displaying the target recommendation information; Search the recommended copywriting database for a copywriting template of the language type that corresponds to the current recommended scenario.

6. The method according to claim 1, characterized in that, The method also includes pre-determining the target recommended content based on the following: Obtain the user's device operation habits during a preset time period prior to the current moment; At least one piece of equipment operation information is determined based on the aforementioned equipment operation habits; The target recommended content is extracted from the at least one piece of device operation information.

7. The method according to claim 6, characterized in that, The method also includes pre-constructing the recommended copy database based on the following: Based on the at least one piece of device operation information and the preset artificial intelligence big data model, determine recommendation copy templates for multiple preset recommendation scenarios; The recommended copywriting templates are entered into the first database to obtain the recommended copywriting database.

8. The method according to claim 7, characterized in that, The step of inputting the recommended copywriting template into the first database includes: The recommended copywriting template is converted into multiple preset language types; The converted recommendation copy template is entered into the first database.

9. The method according to claim 7, characterized in that, The step of determining recommendation templates for multiple preset recommendation scenarios based on at least one piece of device operation information and a preset artificial intelligence big data model includes: Generate the target basic text based on the at least one piece of device operation information; The target basic text and the set guiding words are input into the preset artificial intelligence big model to obtain recommendation text templates for various preset recommendation scenarios.

10. The method according to claim 9, characterized in that, Before inputting the target basic text and the set guiding words into the preset artificial intelligence large model, the method further includes: Replace the variable information in the target basic text with preset text symbols. The variable information includes at least one of the following: device identifier, room where the device is located, and device operation.

11. The method according to claim 9, characterized in that, The generation of the target basic text based on the at least one piece of device operation information includes: In the pre-built basic text database, search for the basic text corresponding to each piece of information in the at least one piece of device operation information; Based on each of the multiple preset dimensions, the basic text corresponding to each of the at least one piece of device operation information is aggregated to obtain the aggregated text under the same preset dimension. The preset dimension includes at least one of the device identification dimension, device category dimension, and room dimension where the device is located. By splicing together aggregated texts under different preset dimensions, the target basic text is obtained.

12. The method according to claim 11, characterized in that, The method also includes pre-constructing the basic copywriting database based on the following: Determine the textual components for each type of equipment, including equipment category, equipment identification, room where the equipment is located, and at least two of the equipment operation parameters; Generate basic copy for each device based on the aforementioned copywriting elements; The basic text is entered into the second database to obtain the basic text database.

13. An information recommendation device, characterized in that, The device includes: The template acquisition module is used to retrieve a target copy template from a pre-built recommendation copy database in response to the current information recommendation conditions that meet the user's requirements. The target copy template is presented in natural language form. The information acquisition module is used to fill the target text template with pre-determined target recommendation content to obtain target recommendation information. The target recommendation content is determined based on the user's device operation habits in a historical time period. The target recommendation content includes at least one of device identification, the room where the device is located, and device operation. The information push module is used to push the target recommendation information to the user.

14. An electronic device, characterized in that, The device includes: Processor and memory used to store computer programs; The processor is configured to implement the information recommendation method according to any one of claims 1 to 12 when executing the computer program.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the information recommendation method according to any one of claims 1 to 12.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the information recommendation method according to any one of claims 1 to 12.

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