Brand content strategy reasoning methods, devices, equipment, and media

Abstract

This disclosure provides a brand content strategy reasoning method, apparatus, device, and medium, comprising: creating a brand identification rule set by structurally analyzing relevant information of a specific brand, the brand identification rule set including: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule; semantically mapping the task objective using the first identification rule to generate core theme data strongly associated with the specific brand; constructing a stylized narrative model corresponding to the specific brand based on tone information in the second identification rule, the stylized narrative model being used to reason about narrative structure and emotional tone parameters adapted to the specific brand; constraining the core theme data using the third and fourth identification rules to generate corresponding target expression data; and generating a target content strategy corresponding to the specific brand based on the target expression data and the stylized narrative model. This effectively improves the efficiency of brand content creation.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of artificial intelligence technology, and more specifically, to a method, apparatus, device, and medium suitable for brand content strategy reasoning. Background Technology

[0002] In brand communication and content marketing, brands typically possess complete brand identity elements such as visual guidelines, language guidelines, and value propositions. However, these guidelines usually exist in natural language or mixed text and graphics formats (such as PDF brand manuals), making them difficult to utilize in a structured manner by computational systems.

[0003] In related technologies, when creating content, AI (Artificial Intelligence) or human creators struggle to retrieve and understand complex brand documents in real time, making it difficult for the content to consistently reflect the brand's tone. Summary of the Invention

[0004] The embodiments described herein provide a brand content strategy reasoning method, apparatus, device, and medium that overcome the aforementioned problems.

[0005] Firstly, based on the content of this disclosure, a brand content strategy reasoning method is provided, including: By performing structured analysis of relevant information about a specific brand, a brand identification rule set is created. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of the specific brand, the second identification rule is used to identify the language information corresponding to the specific brand, the third identification rule is used to identify the visual information corresponding to the specific brand, and the fourth identification rule is used to impose expression constraints on the specific brand. The first identification rule is used to perform semantic mapping on the task target to generate core concept data that is strongly associated with the specific brand. Based on the intonation information in the second identification rule, a stylized narrative model corresponding to the specific brand is constructed. The stylized narrative model is used to infer the narrative structure and emotional tone parameters that are adapted to the specific brand. The core concept data is constrained by the third and fourth identification rules to generate corresponding target expression data; and a target content strategy for the specific brand is generated based on the target expression data and the stylized narrative model, so as to guide the marketing creation process of the specific brand through the target content strategy.

[0006] Secondly, based on the content of this disclosure, a brand content strategy reasoning apparatus is provided, comprising: A creation module is used to create a brand identification rule set by performing structured parsing of relevant information of a specific brand. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of the specific brand, the second identification rule is used to identify the language information corresponding to the specific brand, the third identification rule is used to identify the visual information corresponding to the specific brand, and the fourth identification rule is used to impose expression constraints on the specific brand. The first generation module is used to perform semantic mapping on the task target through the first identification rule and generate core concept data that is strongly associated with the specific brand. The construction module is used to construct a stylized narrative model corresponding to the specific brand based on the intonation information in the second identification rule. The stylized narrative model is used to infer the narrative structure and emotional tone parameters that are adapted to the specific brand. The second generation module is used to constrain the core concept data through the third and fourth identification rules to generate corresponding target expression data; and to generate a target content strategy for the specific brand based on the target expression data and the stylized narrative model, so as to guide the marketing creation process of the specific brand through the target content strategy.

[0007] Thirdly, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the brand content strategy reasoning method as described in any of the above embodiments.

[0008] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the brand content strategy reasoning method as described in any of the above embodiments.

[0009] The brand content strategy reasoning method provided in this application embodiment creates a brand identification rule set by performing structured analysis of relevant information of a specific brand. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of a specific brand; the second identification rule is used to identify the linguistic information corresponding to the specific brand; the third identification rule is used to identify the visual information corresponding to the specific brand; and the fourth identification rule is used to constrain the expression of the specific brand. The first identification rule is used to semantically map the task objective, generating core theme data strongly associated with the specific brand. Based on the intonation information in the second identification rule, a stylized narrative model corresponding to the specific brand is constructed. This stylized narrative model is used to reason about the narrative structure and emotional tone parameters adapted to the specific brand. The third and fourth identification rules are used to constrain the core theme data, generating corresponding target expression data. Finally, based on the target expression data and the stylized narrative model, a target content strategy corresponding to the specific brand is generated to guide the marketing creation process of the specific brand. In this way, by structurally modeling the brand identification rules and performing multi-layered strategy reasoning "from rules to creativity" based on the rule modeling, content strategies can be obtained, which can help AI or human to quickly understand complex brand documents when creating brand content, effectively improving the efficiency of brand content creation.

[0010] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure, wherein: Figure 1 This is a flowchart illustrating a brand content strategy reasoning method disclosed herein.

[0012] Figure 2 This is a schematic diagram of the structure of a brand content strategy reasoning device disclosed herein.

[0013] Figure 3 This is a schematic diagram of the structure of a computer device provided in this disclosure.

[0014] It should be noted that the elements in the attached diagram are schematic and not drawn to scale. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are also within the scope of protection of this disclosure.

[0016] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the specification and in the relevant art, and shall not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. As used herein, the statement of “connecting” or “coupling” two or more parts together shall mean that these parts are directly joined together or joined through one or more intermediate components.

[0017] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B exist simultaneously, or B exists. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Terms such as "first" and "second" are only used to distinguish one component (or part of a component) from another component (or another part of a component).

[0019] In the description of this application, unless otherwise stated, "multiple" means two or more (including two), and similarly, "multiple groups" means two or more (including two groups).

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating a brand content strategy reasoning method provided in this embodiment of the disclosure, such as... Figure 1As shown, the specific process of the brand content strategy reasoning method includes: S110. By performing structured analysis of relevant information about a specific brand, a brand identification rule set is created. The brand identification rule set includes: the first identification rule, the second identification rule, the third identification rule, and the fourth identification rule.

[0022] The first identification rule is used to express the value of a specific brand, such as its value proposition, mission and vision, and core narrative archetypes. The second identification rule is used to identify the linguistic information corresponding to a specific brand, such as tone ("professional," "friendly"), keyword library, prohibited expressions, and language style constraints. The third identification rule is used to identify the visual information corresponding to a specific brand, such as brand colors, brand fonts, logo usage guidelines, and graphic elements. The fourth identification rule is used to impose expression constraints on a specific brand, such as hard constraints (must comply with legal red lines, logo misuse) and soft constraints (prioritize recommended rhetorical devices).

[0023] In some embodiments, a brand identification rule set is created by performing structured analysis on relevant information of a specific brand, including: performing value expression analysis on relevant information of a specific brand to generate a first identification rule; performing linguistic feature analysis on relevant information of a specific brand to generate a second identification rule; performing visual feature analysis on relevant information of a specific brand to generate a third identification rule; and performing expression constraint analysis on relevant information of a specific brand to generate a fourth identification rule.

[0024] Among them, natural language processing, multimodal analysis and rule extraction techniques can be used to generate the first recognition rule, the second recognition rule, the third recognition rule and the fourth recognition rule.

[0025] Specifically, natural language processing (NLP) technology can be used to conduct deep semantic analysis on textual materials such as official website introductions, annual reports, and brand declarations of specific brands to extract core expressions related to brand value propositions, such as "technological innovation." Through thematic modeling and sentiment analysis, the mission and vision of characteristic brands can be identified, such as "creating value for society." Furthermore, the core narrative archetypes of specific brands, such as "guardian," can be summarized to construct the first identification rule. By analyzing the language features of specific brands' advertising copy and product manuals, the commonly used tone of specific brands can be identified. A keyword database unique to information, including industry terminology, brand-specific vocabulary, and high-frequency positive words, can be constructed. By analyzing negative cases or expressions explicitly avoided by the brand, prohibited expressions can be identified, such as "avoid using: XXX." Language style constraints, such as "vivid and descriptive," can be summarized to construct the second identification rule. Multimodal analysis technology can be used to analyze visual materials such as logo designs and promotional posters of specific brands to extract brand colors, brand fonts, logo usage guidelines, and graphic elements to construct the third identification rule. By analyzing the expressive constraints of legal provisions, industry standards, and other materials, we can identify the hard and soft constraints that brands must strictly adhere to, and construct the fourth identification rule.

[0026] S120. Semantically map the task objective using the first identification rule to generate core concept data that is strongly associated with a specific brand.

[0027] Among them, core message data that is strongly associated with a specific brand can be used to express the brand's core value proposition, brand personality, and emotional appeal. For example, if the brand's core value is "exploring the unknown," the task objective "new product launch" can be mapped to the narrative theme of "starting a new journey" to obtain a core message that is strongly associated with the brand's spirit and solve the problem of "what to say."

[0028] In some embodiments, the task objective is semantically mapped using a first identification rule to generate core conceptual data strongly associated with a specific brand. This includes: parsing the deep semantic information contained in the task objective to obtain multiple core semantic elements related to a specific brand; matching each core semantic element with the multidimensional semantic features of a specific brand based on a preset brand knowledge graph to obtain the association strength between each core semantic element and the specific brand; selecting one or more specific semantic elements from the multiple core semantic elements based on the association strength between each core semantic element and the specific brand; and integrating the core conceptual data strongly associated with the specific brand based on the one or more specific semantic elements.

[0029] The brand knowledge graph can contain multi-dimensional semantic feature data such as brand history, core values, product characteristics, target audience profiles, and market positioning. The semantic feature data is stored in the brand knowledge graph in a structured manner, which can provide a comprehensive reference system for matching core semantic elements.

[0030] The strength of the association between each core semantic element and a specific brand can be measured by the semantic similarity between the two. After identifying the specific semantic elements, multiple elements can be organically integrated according to the narrative logic of the specific brand. For example, the specific semantic elements "environmentally friendly materials," "sustainable production," and "green living concept" can be integrated into the following core message: "Practicing green commitments and creating a sustainable future." This ensures that the core message data not only highly aligns with the brand's characteristics but also clearly conveys the brand's core proposition.

[0031] S130. Construct a stylized narrative model corresponding to a specific brand based on the intonation information in the second identification rule.

[0032] Among them, the stylized narrative model is used to reason about the narrative structure and emotional tone parameters that are suitable for a specific brand. The reasoning layer is constructed through the stylized narrative model to solve the problem of "how to tell the story".

[0033] In some embodiments, constructing a stylized narrative model corresponding to a specific brand based on intonation information in the second identification rule includes: constraining the language state of a specific brand based on intonation information in the second identification rule using a narrative reasoning engine to obtain a language expression benchmark corresponding to the specific brand; determining the narrative structure and emotional tone parameters corresponding to the specific brand based on the language expression benchmark corresponding to the specific brand; and constructing a corresponding stylized narrative model based on the narrative structure and emotional tone parameters corresponding to the specific brand.

[0034] Among them, the language expression benchmarks corresponding to a specific brand are a set of language norms and style guidelines that the brand should follow when expressing content. For example, if the second identification rule is "rational and rigorous," then the corresponding language expression benchmarks could be: using objective statement sentence structures and ensuring logical clarity in content expression. The narrative structure corresponding to a specific brand is such as "problem-analysis-solution," and the emotional tone parameters are such as "positive, calm, and reliable."

[0035] This allows brands to maintain a high degree of consistency and uniqueness in the content creation process, from the logical structure of the narrative to the intensity of emotional delivery, enabling the target audience to quickly perceive and identify with the brand's personality and emotional proposition when they come into contact with the brand's content.

[0036] S140. The core concept data is constrained by the third and fourth identification rules to generate corresponding target expression data; and a target content strategy for a specific brand is generated based on the target expression data and the stylized narrative model, so as to guide the marketing creation process of a specific brand through the target content strategy.

[0037] Among them, the core theme data is constrained by the third and fourth identification rules to achieve the divergence and convergence of the core theme, so as to solve the problem of "what materials to use to tell the story".

[0038] In some embodiments, the core concept data is constrained by a third and a fourth identification rule to generate corresponding target expression data. This includes: applying a soft constraint to the core concept data using the fourth identification rule to generate associative supplementary data corresponding to the core concept data; applying a hard constraint to the core concept data using the fourth identification rule and constraining the key visual elements in the core concept data using the third identification rule to converge the restricted standard data corresponding to the core concept data; and determining the target expression data based on the associative supplementary data and restricted standard data corresponding to the core concept data.

[0039] In applying soft constraints, relevant scenarios and metaphors can be associated with the brand keyword database. In applying hard constraints, "prohibited expressions" can be used to filter creative ideas that do not conform to the brand's tone, and "visual guidelines" (the third identification rule) can be used to determine key visual elements, thereby obtaining specific creative angles, suggested key images, and language styles—that is, target expression data. Thus, through the "dual control of soft and hard constraints," the brand's bottom line (hard constraints) is maintained, while creative space is preserved under the guidance of style (soft constraints).

[0040] In some embodiments, generating a target content strategy corresponding to a specific brand based on target expression data and a stylized narrative model includes: generating a strategy inference chain corresponding to a specific brand based on the target expression data and the stylized narrative model; and integrating the strategy inference chain corresponding to the specific brand into a structured and stored target content strategy.

[0041] The strategy reasoning chain consists of multiple reasoning nodes and logical connections between them. Each reasoning node corresponds to a key decision-making stage in the brand content strategy generation process, such as interpreting the core message and accurately targeting the target audience. The target content strategy may include the core message and narrative model, a brand consistency checklist, recommended creative angles, style and tone guidelines, and warnings against prohibited items. Thus, through the layered mapping of the reasoning chain, it ensures that all content planning originates from the brand's core values, rather than being randomly generated. This makes brand specifications no longer static documents, but dynamic logic that can be used by the calculation system to actively deduce creative ideas.

[0042] In some embodiments, the method further includes: obtaining social media platform information associated with a specific brand; and fine-tuning the target content strategy based on the social media platform information associated with the specific brand.

[0043] Among these, strategies can be fine-tuned for specific platforms (such as social media, official websites, and press releases) to enhance the dissemination attributes of content strategies across different channels while ensuring the consistency of the brand's core values.

[0044] In this embodiment, a brand identification rule set is created by structured analysis of relevant information about a specific brand. This rule set includes a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of the specific brand; the second identification rule is used to identify the linguistic information corresponding to the specific brand; the third identification rule is used to identify the visual information corresponding to the specific brand; and the fourth identification rule is used to constrain the expression of the specific brand. The first identification rule is used to semantically map the task objective, generating core theme data strongly associated with the specific brand. Based on the intonation information in the second identification rule, a stylized narrative model corresponding to the specific brand is constructed. This stylized narrative model is used to infer the narrative structure and emotional tone parameters suitable for the specific brand. The third and fourth identification rules constrain the core theme data, generating corresponding target expression data. Finally, based on the target expression data and the stylized narrative model, a target content strategy corresponding to the specific brand is generated to guide the marketing creation process of the specific brand. In this way, by structurally modeling the brand identification rules and performing multi-layered strategy reasoning "from rules to creativity" based on the rule modeling, content strategies can be obtained, which can help AI or human to quickly understand complex brand documents when creating brand content, effectively improving the efficiency of brand content creation.

[0045] In addition, this embodiment provides a specific example. The scenario is as follows: A hardcore technology brand (brand rules: rationality, restraint, minimalism, technology empowering life) is planning content for "Mother's Day marketing." Rule retrieval: The system identifies the task "Mother's Day" and retrieves the brand's Language Identification Rule (LIR) (restraint, no excessive embellishment, avoidance of sentimentality) and Value Expression Rule (VER) (core value: technology improves efficiency). Value mapping reasoning: Input: Mother's Day + brand value (technology improves efficiency); Reasoning: Instead of using the common "gratitude / sentimentality" theme, it maps to the theme of "liberating time / focusing on oneself"; Judgment: The theme is "reducing mothers' housework time through technology, giving time back to them." Narrative model construction: Based on LIR (rationality), the "data-driven expression" narrative model is selected; Strategy output: Instead of writing a sentimental poem, it lists data comparisons of "how many hours were saved." Creative Approach: Expanding Ideas: Think of robotic vacuum cleaners and smart home devices; Converging Ideas (Soft Constraints): Eliminate "warm and cozy" image styles, recommending a visual style of "cool colors + minimalist lines"; Eliminate clichéd copywriting such as "Mom, you've worked so hard," recommending copywriting styles such as "Love is restrained and rational support." Final Output Strategy Document: Includes the theme "Rational Love," visual reference images (minimalist style), and key copywriting logic (data-driven), for use by the execution team.

[0046] In summary, this embodiment structures brand values ​​and language norms into reasoning factors, enabling it to proactively deduce creative angles that align with the brand's tone, achieving a qualitative shift from "rule constraints" to "rule inspiration." Brand rules are divided into "hard constraints (red lines)" and "soft constraints (style guidelines)." During the reasoning process, hard constraints are used for pruning (eliminating erroneous strategies), while soft constraints act as weighting factors in the selection of creative paths, endowing the system with the flexible thinking capabilities of a seasoned planner. Each strategy suggestion output by the system establishes a mapping relationship with brand identity rules, solving the problem of the "black box" nature of AI-generated content strategies and allowing brand managers to clearly understand which brand norms each strategy is based on.

[0047] Figure 2 This is a schematic diagram of a brand content strategy reasoning device provided in this embodiment. The brand content strategy reasoning device may include: Module 210 is used to create a brand identification rule set by performing structured analysis of relevant information of a specific brand. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of a specific brand, the second identification rule is used to identify the language information corresponding to a specific brand, the third identification rule is used to identify the visual information corresponding to a specific brand, and the fourth identification rule is used to impose expression constraints on a specific brand.

[0048] The first generation module 220 is used to perform semantic mapping on the task target through the first recognition rule to generate core concept data that is strongly associated with a specific brand.

[0049] Module 230 is used to construct a stylized narrative model corresponding to a specific brand based on the intonation information in the second recognition rule. The stylized narrative model is used to infer the narrative structure and emotional tone parameters that are adapted to the specific brand.

[0050] The second generation module 240 is used to constrain the core concept data through the third and fourth identification rules to generate corresponding target expression data; and to generate target content strategies for specific brands based on the target expression data and stylized narrative models, so as to guide the marketing creation process of specific brands through the target content strategies.

[0051] In this embodiment, optionally, the first generation module 220 is specifically used for: The process involves analyzing the deep semantic information contained in the task objectives to obtain multiple core semantic elements related to a specific brand; matching each core semantic element with the multidimensional semantic features of the specific brand based on a pre-defined brand knowledge graph to obtain the correlation strength between each core semantic element and the specific brand; selecting one or more specific semantic elements from the multiple core semantic elements based on the correlation strength between each core semantic element and the specific brand; and integrating the core concept data that is strongly associated with the specific brand based on one or more specific semantic elements.

[0052] In this embodiment, optionally, the construction module 230 is specifically used for: Using a narrative reasoning engine, language state constraints are applied to specific brands based on intonation information in the second recognition rule to obtain the language expression benchmark corresponding to the specific brand; based on the language expression benchmark corresponding to the specific brand, the narrative structure and emotional tone parameters corresponding to the specific brand are determined; and based on the narrative structure and emotional tone parameters corresponding to the specific brand, a corresponding stylized narrative model is constructed.

[0053] In this embodiment, optionally, the second generation module 240 is specifically used for: The core concept data is subjected to soft constraints through the fourth identification rule to generate the associated supplementary data corresponding to the core concept data; the core concept data is subjected to hard constraints through the fourth identification rule, and the key visual elements in the core concept data are constrained through the third identification rule to converge the restricted standard data corresponding to the core concept data; the target expression data is determined based on the associated supplementary data and restricted standard data corresponding to the core concept data.

[0054] In this embodiment, optionally, the second generation module 240 is specifically used for: Based on the target expression data and stylized narrative model, generate a strategy reasoning chain corresponding to a specific brand; and integrate the strategy reasoning chain corresponding to the specific brand into a structured target content strategy.

[0055] In this embodiment, optionally, the creation module 210 is specifically used for: Value expression analysis is performed on relevant information of a specific brand to generate the first recognition rule; linguistic feature analysis is performed on relevant information of a specific brand to generate the second recognition rule; visual feature analysis is performed on relevant information of a specific brand to generate the third recognition rule; and expression constraint analysis is performed on relevant information of a specific brand to generate the fourth recognition rule.

[0056] In this embodiment, optionally, a fine-tuning module is also included.

[0057] The fine-tuning module is used to obtain social media platform information associated with a specific brand and to fine-tune the target content strategy based on this information.

[0058] The brand content strategy reasoning device provided in this disclosure can execute the above method embodiments. Its specific implementation principle and technical effects can be found in the above method embodiments, and will not be repeated here.

[0059] This application also provides a computer device. Please refer to the following for details. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0060] The computer device includes a memory 310 and a processor 320 that are interconnected via a system bus. It should be noted that only a computer device with memory 310 and processor 320 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0061] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0062] The memory 310 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. RAM may include static RAM or dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, or flash card equipped on the computer device. Of course, the memory 310 may include both internal storage units and external storage devices of the computer device. In this embodiment, the memory 310 is typically used to store the operating system and various application software installed on the computer device, such as the program code of the method described above. In addition, the memory 310 can also be used to temporarily store various types of data that have been output or will be output.

[0063] Processor 320 is typically used to perform overall operations of a computer device. In this embodiment, memory 310 is used to store program code or instructions, including computer operation instructions, and processor 320 is used to execute the program code or instructions stored in memory 310 or process data, such as program code that runs the methods described above.

[0064] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus system can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0065] Another embodiment of this application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer-readable program code stored in the computer-readable medium, enabling the processor to execute the functional actions specified in each step or combination of steps in the above method; and to generate means for implementing the functional actions specified in each block or combination of blocks in the block diagram.

[0066] Computer-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any suitable combination thereof, wherein the memory is used to store program code or instructions, the program code including computer operation instructions, and the processor is used to execute the program code or instructions of the above-described methods stored in the memory.

[0067] The definitions of memory and processor can be found in the description of the foregoing computer device embodiments, and will not be repeated here.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] In the various embodiments of this application, the functional units or modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0070] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" as described in this application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims listing several means, several units of these means may be embodied by the same item of hardware. The use of "first," "second," and "third," etc., does not indicate any order and these words should be interpreted as names. Unless otherwise specified, the steps in the above embodiments should not be construed as limiting the order of execution.

[0072] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A brand content strategy reasoning method, characterized in that, include: By performing structured analysis of relevant information about a specific brand, a brand identification rule set is created. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of the specific brand, the second identification rule is used to identify the language information corresponding to the specific brand, the third identification rule is used to identify the visual information corresponding to the specific brand, and the fourth identification rule is used to impose expression constraints on the specific brand. The first identification rule is used to perform semantic mapping on the task target to generate core concept data that is strongly associated with the specific brand. Based on the intonation information in the second identification rule, a stylized narrative model corresponding to the specific brand is constructed. The stylized narrative model is used to infer the narrative structure and emotional tone parameters that are adapted to the specific brand. The core concept data is constrained by the third and fourth identification rules to generate corresponding target expression data; and a target content strategy for the specific brand is generated based on the target expression data and the stylized narrative model, so as to guide the marketing creation process of the specific brand through the target content strategy.

2. The method according to claim 1, characterized in that, The task objective is semantically mapped using the first identification rule to generate core conceptual data strongly associated with the specific brand, including: The deep semantic information contained in the task objective is analyzed to obtain multiple core semantic elements related to the specific brand. Based on the preset brand knowledge graph, each core semantic element is matched with the multidimensional semantic features of the specific brand to obtain the association strength between each core semantic element and the specific brand. Based on the strength of association between each core semantic element and the specific brand, one or more specific semantic elements are selected from the multiple core semantic elements; based on the one or more specific semantic elements, core concept data with a strong association with the specific brand is integrated.

3. The method according to claim 1, characterized in that, Constructing a stylized narrative model corresponding to the specific brand based on the intonation information in the second recognition rule, including: Using the narrative reasoning engine, the language state constraint is applied to the specific brand based on the intonation information in the second recognition rule to obtain the language expression benchmark corresponding to the specific brand. Based on the language expression benchmark corresponding to the specific brand, determine the narrative structure and emotional tone parameters corresponding to the specific brand; construct the corresponding stylized narrative model based on the narrative structure and emotional tone parameters corresponding to the specific brand.

4. The method according to claim 1, characterized in that, The core concept data is constrained by the third and fourth identification rules to generate corresponding target expression data, including: The core idea data is subject to soft constraints through the fourth identification rule, so as to generate the associated supplementary data corresponding to the core idea data. The core concept data is subjected to hard constraints by the fourth identification rule, and the key visual elements in the core concept data are constrained by the third identification rule, so as to converge the restricted standard data corresponding to the core concept data. The target expression data is determined based on the associated supplementary data and restricted normative data corresponding to the core concept data.

5. The method according to claim 1, characterized in that, Based on the target expression data and the stylized narrative model, a target content strategy corresponding to the specific brand is generated, including: Based on the target expression data and the stylized narrative model, a strategy reasoning chain corresponding to the specific brand is generated; and the strategy reasoning chain corresponding to the specific brand is integrated into a structured and stored target content strategy.

6. The method according to claim 1, characterized in that, By performing structured analysis of relevant information about a specific brand, a set of brand identification rules is created, including: Value expression analysis is performed on relevant information of a specific brand to generate the first identification rule; language feature analysis is performed on relevant information of a specific brand to generate the second identification rule; visual feature analysis is performed on relevant information of a specific brand to generate the third identification rule; and expression constraint analysis is performed on relevant information of a specific brand to generate the fourth identification rule.

7. The method according to claim 1, characterized in that, Also includes: Obtain information about social media platforms associated with the specific brand; and fine-tune the target content strategy based on the information about social media platforms associated with the specific brand.

8. A brand content strategy reasoning device, characterized in that, include: A creation module is used to create a brand identification rule set by performing structured parsing of relevant information of a specific brand. The brand identification rule set includes: a first identification rule, a second identification rule, a third identification rule, and a fourth identification rule. The first identification rule is used to express the value of the specific brand, the second identification rule is used to identify the language information corresponding to the specific brand, the third identification rule is used to identify the visual information corresponding to the specific brand, and the fourth identification rule is used to impose expression constraints on the specific brand. The first generation module is used to perform semantic mapping on the task target through the first identification rule and generate core concept data that is strongly associated with the specific brand. The construction module is used to construct a stylized narrative model corresponding to the specific brand based on the intonation information in the second identification rule. The stylized narrative model is used to infer the narrative structure and emotional tone parameters that are adapted to the specific brand. The second generation module is used to constrain the core concept data through the third and fourth identification rules to generate corresponding target expression data; and to generate a target content strategy for the specific brand based on the target expression data and the stylized narrative model, so as to guide the marketing creation process of the specific brand through the target content strategy.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the brand content strategy reasoning method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the brand content strategy reasoning method as described in any one of claims 1 to 7.

Images