Marketing guide method and device of data center and electronic equipment

Abstract

The invention provides a marketing guide method and device for a data center and electronic equipment, and the method comprises the steps: carrying out the demand analysis of current user interaction data based on a demand analysis agent, and constructing a user demand portrait; outputting an initial marketing scheme matched with the user demand portrait based on the scheme recommendation agent; marketing guidance is carried out based on the initial marketing scheme, demand analysis is carried out on current user interaction data through a demand analysis agent, and a user demand portrait is constructed; receiving a user demand portrait based on the scheme recommendation agent, and outputting an initial marketing scheme matched with the user demand portrait; based on the initial marketing scheme, marketing guidance is carried out with the target user, so that accurate and personalized marketing guidance is realized, and the service experience of the user is improved. And moreover, a reliable decision basis is provided for subsequent service decisions, so that the service decision efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent marketing technology, and in particular to a marketing guidance method, apparatus, and electronic device for data centers. Background Technology

[0002] With the rapid development of digital technologies and the accelerated advancement of industrial digital transformation, data centers, as the core infrastructure supporting the digital economy, are increasingly highlighting their strategic importance. The surge in demand for the storage, processing, and transmission of massive amounts of data is driving the evolution of data centers towards large-scale, intelligent, and green technologies, ushering in a dual wave of technological innovation and market competition for the industry. Traditional data center marketing primarily relies on manual explanations, with the main process including: initially customizing a presentation plan based on customer needs; having professional staff provide on-site reception and guide visits to core areas such as the server room, power distribution room, and monitoring center; explaining technical parameters, service content, and successful cases using promotional materials and system demonstrations; collecting customer feedback through Q&A sessions; and afterwards, compiling requirements and coordinating with the technical team to develop targeted solutions, ultimately leading to business negotiations and contract signing.

[0003] However, relying on human explanations has many drawbacks. On the one hand, human explanations consume a lot of manpower and resources, have high training costs, and the effectiveness of the explanations is greatly affected by the quality and experience of the personnel. On the other hand, it is impossible to accurately perceive the real-time needs of customers, resulting in low supply and demand matching and low efficiency in business decision-making. Summary of the Invention

[0004] This invention provides a marketing guidance method, device, and electronic device for data centers, which solves the problem that the existing technology relies on manual explanation, resulting in a low degree of matching between supply and demand with users, and thus low efficiency in business decision-making.

[0005] This invention provides a marketing guide method for data centers, comprising: Obtain the target user's current user interaction data; Based on the demand analysis agent, the current user interaction data is analyzed to construct a user demand profile of the target user, and the user demand profile is transmitted to the solution recommendation agent. Based on the above scheme, the recommended intelligent agent receives the user demand profile and outputs an initial marketing plan that matches the user demand profile; Based on the initial marketing plan, conduct marketing guidance with the target users.

[0006] According to a marketing guidance method for a data center provided by the present invention, the step of performing demand analysis on the current user interaction data based on a demand analysis intelligent agent to construct a user demand profile of the target user includes: Based on the aforementioned demand analysis agent, keywords are extracted from the current user interaction data to obtain the user demand text. Extract the user demand features from the user demand text, and calculate the node similarity between the user demand features and each knowledge node in the business background knowledge graph. Based on the node similarity, the implicit requirement text of the user requirement text is determined; Based on the user requirement text and the implicit requirement text, the user requirement profile is constructed.

[0007] According to a marketing guidance method for a data center provided by the present invention, the step of guiding the target user with marketing based on the initial marketing plan includes: Acquire the pose data of the target user, and determine the matching language style of the target user based on the pose data; Determine personalized text that matches the user's needs profile; Based on the matching language style, the personalized text, and the initial marketing plan, a recommended marketing plan is generated. Based on the digital human application, the recommended marketing scheme guides the target users.

[0008] According to a marketing guidance method for a data center provided by the present invention, the step of guiding the target user with marketing based on the digital person and the recommended marketing plan includes: Obtain historical recommended marketing plans and historical user interaction data; Based on a comprehensive analysis of the historical recommended marketing plans, the historical user interaction data, and the market competition data, the data analysis results are obtained. The data analysis results are presented.

[0009] According to a marketing guidance method for data centers provided by the present invention, the data analysis results include at least one of customer demand trends, market share changes, and marketing effectiveness evaluation.

[0010] According to a marketing guidance method for a data center provided by the present invention, the step of obtaining the current user interaction data of the target user includes: Acquire and display digital twin models of data center business scenarios; The voice data of the target user is collected as the current user interaction data.

[0011] According to a marketing guidance method for data centers provided by the present invention, the steps for constructing a digital twin model of the business scenario include: By mapping the data center architecture, equipment operating status, and data traffic in a business scenario, a digital twin model of the business scenario is constructed.

[0012] The present invention also provides a marketing guide device for data centers, comprising: The acquisition unit acquires the current user interaction data of the target user; The demand analysis unit, based on the demand analysis intelligent agent, performs demand analysis on the current user interaction data, constructs a user demand profile of the target user, and transmits the user demand profile to the solution recommendation intelligent agent; The solution determination unit receives the user demand profile based on the solution recommendation agent and outputs an initial marketing solution that matches the user demand profile. The marketing guidance unit guides the target users based on the initial marketing plan.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a marketing guidance method for a data center as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the marketing guidance method for a data center as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements a marketing wizard method for a data center as described above.

[0016] The marketing guidance method, apparatus, and electronic device for data centers provided by this invention analyze current user interaction data through a demand analysis agent to construct a user demand profile. A solution recommendation agent receives the user demand profile and outputs an initial marketing plan matching the profile. Based on the initial marketing plan, marketing guidance is provided to target users, achieving precise and personalized marketing guidance and improving the user service experience. Furthermore, it provides a reliable basis for subsequent business decisions, thereby improving the efficiency of business decision-making. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the marketing guide method for data centers provided by the present invention; Figure 2 This is a schematic diagram of the marketing guide device for data centers provided by the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention 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 invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0020] To address the aforementioned issues, this invention provides a marketing guidance method for data centers to improve the matching degree between supply and demand with users, achieve accurate and personalized marketing guidance, and thereby improve business efficiency. Figure 1 This is a flowchart illustrating the marketing guide method for data centers provided by the present invention, as shown below. Figure 1 As shown, the method includes: Step 110: Obtain the current user interaction data of the target user.

[0021] Here, current user interaction data refers to the interaction data between the target user and the marketing guide in any business scenario's data center at the current moment. The interaction data can include the target customer's text data, action data, and facial expression data, etc.

[0022] Specifically, the voice data of the target user can be acquired through a voice pickup device, and the acquired voice data can be used as the target user's current user interaction data.

[0023] Step 120: Based on the demand analysis agent, perform demand analysis on the current user interaction data, construct a user demand profile of the target user, and transmit the user demand profile to the solution recommendation agent.

[0024] Here, the demand analysis agent refers to an agent used to analyze user needs based on current user interaction data. This agent can be built using deep learning algorithms. Additionally, the user demand profile describes the characteristics of user needs, which may include business requirements, technical requirements, budget constraints, etc. Finally, the solution recommendation agent recommends marketing solutions and can be built using AI models.

[0025] Specifically, firstly, a demand analysis agent can convert current user interaction data into user interaction text. Then, demand analysis can be performed on the converted user interaction text to construct a user demand profile for the target user. For example, a pre-trained demand analysis model can be built to analyze the user interaction text, extract features from the analysis results, and construct a user demand profile for the target user. This demand analysis model can be built based on deep learning algorithms and fine-tuned using training data on a general-purpose large-scale language model.

[0026] Alternatively, keywords can be extracted from current user interaction data, and with the help of a pre-built knowledge graph, the extracted key text can be refined to meet user needs, and then the refined user needs can be used to build a user needs profile.

[0027] Furthermore, after constructing the user demand profile, the user demand profile can be transmitted to the solution recommendation agent to make solution recommendations in the data center.

[0028] It should be noted that by using a demand analysis agent to analyze current user interaction data, accurate and personalized solution recommendations can be achieved, significantly improving the matching degree between supply and demand with users, reducing resource waste, and thus increasing the business conversion rate.

[0029] Step 130: Based on the above scheme, the recommending agent receives the user demand profile and outputs an initial marketing plan that matches the user demand profile.

[0030] Here, the initial marketing plan refers to preliminary marketing content that aligns with user needs profiles, and may include product recommendations, technological highlights, case studies, and other related content.

[0031] Specifically, the solution recommendation agent can retrieve initial marketing solutions that match user needs profiles from a pre-built marketing solution library according to predefined rules and user needs profiles. For example, business needs, technical requirements, and budget constraints from the user needs profile can be used as matching keywords to retrieve matching initial marketing solutions from the pre-built marketing solution library, thus achieving accurate and personalized solution recommendations. It should be noted that the solution recommendation agent and the needs analysis agent communicate and collaborate with each other to achieve information sharing and task coordination, ensuring the efficiency and accuracy of the entire marketing process.

[0032] Step 140: Based on the initial marketing plan, conduct a marketing guide with the target users.

[0033] Specifically, this can be achieved by constructing a digital human and using the initial marketing plan as the interactive text of the digital human, or by combining a digital twin model of the business scenario to interact with target users, thus realizing marketing guidance for target users. It should be noted that by providing marketing guidance to target users, it is possible to capture changes in user needs and concerns in a timely manner, providing customers with accurate and personalized marketing guidance, and thus providing a reliable basis for subsequent marketing guidance and business decisions.

[0034] The method provided in this invention uses a demand analysis agent to analyze current user interaction data and construct a user demand profile. A solution recommendation agent receives the user demand profile and outputs an initial marketing plan matching it. Based on the initial marketing plan, a marketing guide is provided to the target user, achieving precise and personalized marketing guidance and improving the user's service experience. Furthermore, it provides a reliable basis for subsequent business decisions, thereby improving the efficiency of business decision-making.

[0035] Based on any of the above embodiments, the step of performing demand analysis on the current user interaction data based on the demand analysis intelligent agent to construct a user demand profile of the target user includes: Based on the aforementioned demand analysis agent, keywords are extracted from the current user interaction data to obtain the user demand text. Extract the user demand features from the user demand text, and calculate the node similarity between the user demand features and each knowledge node in the business background knowledge graph. Based on the node similarity, the implicit requirement text of the user requirement text is determined; Based on the user requirement text and the implicit requirement text, the user requirement profile is constructed.

[0036] Specifically, firstly, the current user interaction data can be input into a demand analysis agent, which then converts the speech data into text, obtaining transcribed text data. Next, keyword extraction can be performed on the transcribed text data to extract the user demand text. It is understood that this user demand text can be text that directly or indirectly expresses the user's needs.

[0037] Next, to more accurately capture users' actual needs, semantic features can be extracted from the user's requirement text to obtain user requirement features. Then, the node similarity between the user requirement features and each knowledge node in the business background knowledge graph can be calculated. For example, node similarity can be obtained by calculating the cosine similarity between two semantic features.

[0038] Furthermore, the text corresponding to nodes with a similarity greater than a preset similarity threshold can be selected as the implicit requirement text. For example, the text corresponding to the node with a similarity greater than the preset similarity threshold for the user's requirement text, such as "compliance", could be "Level 3 Information Security Protection Certification".

[0039] It should be noted that the business background knowledge graph here refers to a graph constructed using background knowledge from specific business scenarios corresponding to the data center. Therefore, this business background knowledge graph can provide standardized, professional, and direct business background knowledge. Compared to vague or non-standardized user requests, by calculating the node similarity between user request characteristics and knowledge nodes in the business background knowledge graph, the implicit user requests are obtained, thereby further improving the accuracy of the constructed user request profile.

[0040] Finally, the corresponding content in the user requirement text can be replaced with the implicit requirement text. Then, a user requirement profile can be constructed using the replaced requirement text.

[0041] The method provided in this invention extracts user requirement features from user requirement text, calculates the node similarity between the user requirement features and each knowledge node in the business background knowledge graph, determines the implicit requirement text of the user requirement text based on the node similarity, and constructs an accurate user requirement profile that conforms to professional expression based on the user requirement text and the implicit requirement text, providing a reliable basis for subsequent accurate and professional solution guidance.

[0042] Based on any of the above embodiments, step 140 includes: Acquire the pose data of the target user, and determine the matching language style of the target user based on the pose data; Determine personalized text that matches the user's needs profile; Based on the matching language style, the personalized text, and the initial marketing plan, a recommended marketing plan is generated. Based on the digital human application, the recommended marketing scheme guides the target users.

[0043] Here, digital human refers to a virtual character created based on artificial intelligence and computer graphics technology, possessing natural language interaction capabilities, emotional expression, and professional business knowledge.

[0044] Specifically, firstly, the target user's posture data, including facial expressions and movements, can be acquired. For example, image data containing the target user's facial expressions and movements can be captured using a camera. Then, image recognition can be performed on the captured image data to identify the user's emotions. For instance, if the user currently appears frustrated and their arms are crossed, their emotion could be disappointment or dissatisfaction. Therefore, the identified user emotion can be mapped to a corresponding matching language style. For example, when a user is dissatisfied, a gentler language style can be used to communicate, improving the user's interactive experience.

[0045] Additionally, personalized text can be generated by matching the user's needs profile. For example, if the user needs profile includes "financial industry" and "high availability," then the corresponding personalized text could be "For the financial industry, our solution supports Level 3 Information Security Protection Certification," thus achieving personalized marketing guidance.

[0046] Furthermore, personalized text can be inserted into the initial marketing campaign template. Then, the inserted text can be adjusted according to the matching language style to refine the language style and obtain a recommended marketing campaign that matches the language style and closely meets user needs.

[0047] Finally, pre-built digital humans can read out recommended marketing plans in a tone that matches their language style, enabling interaction with target users. Simultaneously, within a digital twin data center scenario, the digital human can guide target users through a tour and output recommended marketing plans in voice, providing marketing guidance to the target user.

[0048] The method provided in this invention enhances the friendliness, trustworthiness, and professionalism of marketing guides through digital human avatars, user-recognizable language styles, and personalized text. Furthermore, the digital human can vividly introduce customers to the business characteristics, advantages, and success stories of data centers, answer frequently asked questions, and guide customers through marketing scenarios within a digital twin context, thereby increasing customer engagement and understanding, and ultimately improving the success rate of subsequent business decisions.

[0049] Based on any of the above embodiments, and based on the digital person and the aforementioned recommendation marketing scheme, a marketing guide is provided to the target user, which then includes: Obtain historical recommended marketing plans and historical user interaction data; Based on a comprehensive analysis of the historical recommended marketing plans, the historical user interaction data, and the market competition data, the data analysis results are obtained. The data analysis results are presented.

[0050] Here, "historical recommendation marketing plan" refers to a recommendation marketing plan that is adjusted in real time based on the target user's current user interaction data throughout the historical interaction rounds. "Historical user interaction data" refers to the user's interaction data during the historical interaction rounds with the target user.

[0051] Specifically, this can be achieved by acquiring historical recommended marketing campaigns and historical user interaction data. Then, a comprehensive analysis can be conducted using these historical campaigns, user interaction data, and market competition data to obtain the data analysis results. For example, big data analytics and data mining techniques can be used to apply historical recommended marketing campaigns, user interaction data, and market competition data to perform a comprehensive analysis and obtain the data analysis results.

[0052] Alternatively, historical recommended marketing campaigns, historical user interaction data, and market competition data can be input into the analysis model, which will then output data analysis results. This analysis model can be fine-tuned and trained using a general-purpose, large-scale language model. Through this fine-tuning, the model gains the ability to perform predictive analysis based on historical recommended marketing campaigns, historical user interaction data, and market competition data, thus outputting accurate data analysis results. These results can include at least one of the following: customer demand trends, market share changes, and marketing effectiveness evaluation.

[0053] Then, the data analysis results can be displayed. For example, the results can be displayed as icons on a visualization interface.

[0054] It should be noted that digital humans and intelligent agents can work around the clock and can automatically complete most marketing tasks without human intervention, greatly improving marketing efficiency and coverage.

[0055] The method provided in this invention comprehensively analyzes historical recommended marketing plans, historical user interaction data, and market competition data to obtain data analysis results, providing marketers and management with comprehensive and accurate decision-making information. Combined with market competition data, it predicts future market trends and customer needs, helping to formulate forward-looking marketing strategies and business development plans.

[0056] Based on any of the above embodiments, the data analysis results include at least one of customer demand trends, market share changes, and marketing effectiveness evaluation.

[0057] Specifically, customer demand trends are the core basis for marketing strategy formulation, directly influencing the direction of promotion. Market share is a core indicator for measuring competitiveness, directly affecting resource allocation, competitive strategies, and brand positioning. Marketing effectiveness evaluation is key to optimizing marketing investment, directly impacting budget allocation and strategy iteration. Therefore, data analysis results can be used as a basis for decision-making, leading to the final decision-making solution, ensuring that the marketing plan matches customer needs, enhances user resonance, and guarantees the accuracy of competitive and resource strategies.

[0058] Based on any of the above embodiments, step 110 includes: Acquire and display digital twin models of data center business scenarios; The voice data of the target user is collected as the current user interaction data.

[0059] Specifically, a data twin model of the data center's business scenarios can be pre-built. The specific methods for building this model include: highly realistic dynamic mapping of data center architecture, equipment operating status, data traffic, etc. Furthermore, through multi-source data collection and fusion, including performance indicators of servers, storage devices, network devices, operational data, and business application data, the accuracy and real-time performance of the digital twin model are ensured, providing customers with an intuitive and comprehensive display of data center business scenarios.

[0060] When guiding marketing efforts to target users, digital humans can be used to guide them to interact with a digital twin model of the business scenario. Voice data from target users can be collected as current user interaction data to improve customer engagement and understanding.

[0061] It should be noted that by showcasing a digital twin model of the data center's business scenarios, target users are guided to interact with the realistic digital twin model of the business scenarios, thereby obtaining comprehensive and professional current user interaction data, which lays the foundation for the accuracy of subsequent marketing guidance.

[0062] Based on any of the above embodiments, the steps for constructing the digital twin model for the business scenario include: By mapping the data center architecture, equipment operating status, and data traffic in a business scenario, a digital twin model of the business scenario is constructed.

[0063] Specifically, by mapping the data center architecture, equipment operating status, and data traffic under a business scenario, a digital twin model of the business scenario is constructed. This is then synchronized with real business data to create a realistic digital twin model. It should be noted that the lifelike demonstration of the digital twin scenario and the vivid explanation by the digital human allow customers to experience the business appeal of the data center firsthand, increasing their interest and ultimately improving marketing effectiveness.

[0064] Based on any of the above embodiments Figure 2 This is a schematic diagram of the marketing guidance device for data centers provided by the present invention, as shown below. Figure 2 As shown, it includes: Acquisition unit 210 acquires the current user interaction data of the target user; The demand analysis unit 220 performs demand analysis on the current user interaction data based on the demand analysis intelligent agent, constructs a user demand profile of the target user, and transmits the user demand profile to the solution recommendation intelligent agent; The scheme determination unit 230 receives the user demand profile based on the scheme recommendation agent and outputs an initial marketing scheme that matches the user demand profile. Marketing guidance unit 240 guides the target user in a marketing manner based on the initial marketing plan.

[0065] The apparatus provided in this invention uses a demand analysis agent to analyze current user interaction data and construct a user demand profile. A solution recommendation agent receives the user demand profile and outputs an initial marketing plan matching it. Based on the initial marketing plan, it provides marketing guidance to target users, achieving precise and personalized marketing guidance and improving the user's service experience. Furthermore, it provides a reliable basis for subsequent business decisions, thereby improving the efficiency of business decision-making.

[0066] Based on any of the above embodiments, the requirement analysis unit is specifically used for: Based on the aforementioned demand analysis agent, keywords are extracted from the current user interaction data to obtain the user demand text. Extract the user demand features from the user demand text, and calculate the node similarity between the user demand features and each knowledge node in the business background knowledge graph. Based on the node similarity, the implicit requirement text of the user requirement text is determined; Based on the user requirement text and the implicit requirement text, the user requirement profile is constructed.

[0067] Based on any of the above embodiments, the marketing guide unit is specifically used for: Acquire the pose data of the target user, and determine the matching language style of the target user based on the pose data; Determine personalized text that matches the user's needs profile; Based on the matching language style, the personalized text, and the initial marketing plan, a recommended marketing plan is generated. Based on the digital human application, the recommended marketing scheme guides the target users.

[0068] Based on any of the above embodiments, a decision-making unit is included after the marketing guide unit, and the decision-making unit is specifically used for: Obtain historical recommended marketing plans and historical user interaction data; Based on a comprehensive analysis of the historical recommended marketing plans, the historical user interaction data, and the market competition data, the data analysis results are obtained. The data analysis results are presented.

[0069] Based on any of the above embodiments, the data analysis results include at least one of customer demand trends, market share changes, and marketing effectiveness evaluation.

[0070] Based on any of the above embodiments, the acquisition unit is specifically used for: Acquire and display digital twin models of data center business scenarios; The voice data of the target user is collected as the current user interaction data.

[0071] Based on any of the above embodiments, the device further includes a digital twin model construction unit, which is specifically used for: By mapping the data center architecture, equipment operating status, and data traffic in a business scenario, a digital twin model of the business scenario is constructed.

[0072] Figure 3 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 3 As shown, the electronic device may include a processor 310, a communications interface 320, a memory 330, and a communication bus 340, wherein the processor 310, communications interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can invoke logical instructions in the memory 330 to execute a marketing guidance method for a data center. This method includes: acquiring current user interaction data of a target user; performing demand analysis on the current user interaction data based on a demand analysis agent to construct a user demand profile of the target user, and transmitting the user demand profile to a solution recommendation agent; receiving the user demand profile based on the solution recommendation agent and outputting an initial marketing plan matching the user demand profile; and conducting marketing guidance with the target user based on the initial marketing plan.

[0073] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a 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.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. 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.

[0074] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the marketing guidance method for a data center provided by the above methods. The method includes: acquiring current user interaction data of a target user; performing demand analysis on the current user interaction data based on a demand analysis agent to construct a user demand profile of the target user, and transmitting the user demand profile to a solution recommendation agent; receiving the user demand profile based on the solution recommendation agent and outputting an initial marketing plan matching the user demand profile; and conducting marketing guidance with the target user based on the initial marketing plan.

[0075] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements a marketing guidance method for a data center provided by the methods described above. This method includes: acquiring current user interaction data of a target user; performing demand analysis on the current user interaction data based on a demand analysis agent to construct a user demand profile of the target user, and transmitting the user demand profile to a solution recommendation agent; receiving the user demand profile based on the solution recommendation agent and outputting an initial marketing solution matching the user demand profile; and conducting marketing guidance with the target user based on the initial marketing solution.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0077] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A marketing guide method of a data center, characterized by, The method comprises the following steps: obtaining current user interaction data of a target user; based on a demand analysis agent, performing demand analysis on the current user interaction data to construct a user demand portrait of the target user, and transmitting the user demand portrait to a scheme recommendation agent; based on the scheme recommendation agent receiving the user demand portrait, outputting an initial marketing scheme matched with the user demand portrait; based on the initial marketing scheme, conducting marketing guidance with the target user.

2. The marketing guide method of a data center according to claim 1, wherein, The demand analysis agent performs demand analysis on the current user interaction data to construct a user demand portrait of the target user, which comprises the following steps: based on the demand analysis agent, performing keyword extraction on the current user interaction data to obtain user demand text; extracting user demand features of the user demand text, and calculating node similarity between the user demand features and each knowledge node in a business background knowledge graph; based on the node similarity, determining implicit demand text of the user demand text; based on the user demand text and the implicit demand text, constructing the user demand portrait.

3. The marketing guide method of a data center according to claim 1, wherein, The initial marketing scheme is used to conduct marketing guidance with the target user, which comprises the following steps: obtaining posture data of the target user to determine a matching language style of the target user based on the posture data; determining personalized text matched with the user demand portrait; based on the matching language style, the personalized text and the initial marketing scheme, generating a recommended marketing scheme; based on a digital human applying the recommended marketing scheme, conducting marketing guidance with the target user.

4. The marketing guide method of a data center according to claim 3, wherein, After the digital human and the recommended marketing scheme are used to conduct marketing guidance with the target user, the following steps are included: obtaining historical recommended marketing schemes and historical user interaction data; based on comprehensive analysis of the historical recommended marketing schemes, the historical user interaction data and market competition data, obtaining data analysis results; displaying the data analysis results.

5. The method of claim 4, wherein, The data analysis results include at least one of customer demand trends, market share changes and marketing effect evaluation.

6. The marketing guide method of a data center according to any one of claims 1 to 5, characterized by, The current user interaction data of the target user is obtained, which comprises the following steps: obtaining and displaying a business scenario digital twin model of a data center; collecting voice data of the target user as the current user interaction data.

7. The marketing guide method of a data center according to claim 6, wherein, The construction steps of the business scenario digital twin model comprise: mapping data center architecture, device running status and data traffic under a business scenario to construct a business scenario digital twin model.

8. A marketing guide device of a data center, characterized by, The method comprises the following steps: an obtaining unit obtains current user interaction data of a target user; a demand analysis unit, based on a demand analysis agent, performs demand analysis on the current user interaction data to construct a user demand portrait of the target user, and transmits the user demand portrait to a scheme recommendation agent; a scheme determination unit, based on the scheme recommendation agent receiving the user demand portrait, outputs an initial marketing scheme matched with the user demand portrait; a marketing guidance unit, based on the initial marketing scheme, conducts marketing guidance with the target user.

9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The computer program is executed by the processor to implement the marketing guide method of the data center as claimed in any one of claims 1 to 7. 10.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the marketing guide method of the data center as claimed in any one of claims 1 to 7.

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