Garment design factor-based arrival database creation method, system and terminal

Abstract

The invention relates to the field of costume design, in particular to a method, a system and a terminal for creating an arrival database based on costume design factors, and the method comprises the following steps: obtaining a style selection resource database of each platform; generating a current popularity trend based on the selected money resource database; generating consumer demands based on the popularity trend; generating an arrival type and a demand costume design factor based on consumer demands; and selecting a style selection resource database based on the person type and the required costume design factor to create and form a person resource library. The method has the effect of improving the design efficiency.

Description

Technical Field

[0001] The present invention relates to the field of clothing design, and more particularly to a method, system and terminal for creating a talent database based on clothing design factors. Background Art

[0002] Clothing design is a process of systematically conceiving and creating the style, color, fabric, structure and decoration of clothing by combining art and technology with the human body as the carrier.

[0003] In the prior art, clothing designers first obtain inspiration from various platforms to form a preliminary design concept, and then conduct market research to understand consumer needs, fashion trends and competitors' products to provide a basis for design. Then, they transform the inspiration and ideas into specific sketches, outlining the style, details and decorative elements of the clothing.

[0004] In view of the above related technologies, designers need to spend a lot of time and energy in obtaining inspiration and conducting market research on various platforms. At the same time, they cannot accurately capture market demand among many platforms, resulting in low design efficiency. Summary of the Invention

[0005] In order to improve design efficiency, the present invention provides a method, system and terminal for creating a talent database based on clothing design factors.

[0006] In a first aspect, the present invention provides a method for creating a talent database based on clothing design factors, adopting the following technical solutions: A method for creating a talent database based on clothing design factors includes: Obtaining the selected style resource databases of each platform; Generating the current fashion trend based on the selected style resource databases; Generating consumer needs based on the fashion trend; Generating talent types and required clothing design factors based on consumer needs; Selecting the selected style resource databases based on the talent types and required clothing design factors to create a talent resource database.

[0007] By adopting the above technical solutions, a talent database is created by selecting suitable talents on each platform to form talent database resources, breaking the platform boundaries. Designers and users can greatly reduce the time and energy spent on obtaining inspiration and conducting market research on many platforms through the talent database resources, and can more accurately capture market demand, thereby improving design efficiency.

[0008] Optionally, selecting the selected style resource databases based on the talent types and required clothing design factors to create a talent resource database includes: Select the fashion design factors based on the influencer type and requirements to select from the product selection resource database to form preliminary candidates; Generate the source platforms of the preliminary candidates based on the product selection resource databases of each platform; Obtain the invitation permission status based on the source platforms; Generate the candidates to be selected based on the invitation permission status; Send the preset invitation information to the candidates to be selected in sequence to obtain the feedback time values; Select the candidates to be selected with feedback time values within the preset reasonable feedback range as the alternative candidates, and define the feedback time values outside the preset reasonable feedback range as slow feedback time values; Retrieve the alternative candidate number value based on the alternative candidates; Determine whether the alternative candidate number value meets the preset required number value; If yes, retrieve from the product selection resource database based on the alternative candidates to form an influencer resource database; If no, generate backup candidates based on the alternative candidate number value and the slow feedback time value, and retrieve from the product selection resource database based on the backup candidates and the alternative candidates to form an influencer resource database.

[0009] By adopting the above technical solution, invite the preliminary candidates that meet the requirements and solicit the opinions of the influencers. The influencers who are invited within the reasonable time range are used as the alternative candidates. If the influencers exceed the reasonable time range, a new round of evaluation will be conducted to avoid missing suitable candidates. At the same time, each influencer database has a certain number requirement. Selecting influencers from the alternative candidates to form an influencer resource database must also meet the number requirement to avoid resource mixing caused by overage or insufficient number for reference.

[0010] Optionally, the method for selecting the preliminary candidates includes: Retrieve the activity status, professional skills, and clothing styles of the influencers from the product selection resource database based on the influencer type; Generate the influencer influence based on the activity status; Generate the influencer suitability based on the influencer influence and professional skills; Generate the required style based on the fashion design factors of the requirements; Compare the clothing style and the required style to obtain the style fit; Generate the influencer clothing matching degree based on the influencer suitability and the style fit; Select influencers from the product selection resource database based on the influencer clothing matching degree to form the preliminary candidates.

[0011] By adopting the above technical solution, through the double limitation of clothing matching degree and suitability, the preliminary candidates can be screened more refinedly, and more accurate recommendation results can be provided for users according to the formed preliminary candidates, optimizing the user experience.

[0012] Optionally, it further includes steps after generating the suitability of the talent: Retrieve the historical cooperation performance and industry reputation of the talent from the talent resource library; Generate a preliminary evaluation result based on the historical cooperation performance; Generate the content creation quality based on the preliminary evaluation result; Generate a comprehensive evaluation index based on the content creation quality and industry reputation; When the comprehensive evaluation index meets the preset comprehensive evaluation criteria, continue to output the suitability of the talent; When the comprehensive evaluation index does not meet the preset criteria, retrieve the improvement plan and resource investment situation of the talent from the talent resource library; Generate the index improvement effect based on the improvement plan and resource investment situation; Adjust and replace the suitability of the talent based on the index improvement effect.

[0013] By adopting the above technical solution, by evaluating the talents in the talent library, according to their performance, data changes, etc., it is decided whether the talents in the database need to be improved, ensuring the quality of the talents in the talent library, and guaranteeing more high-quality talent resources for the platform and partners.

[0014] Optionally, the method for generating alternative talents includes: Calculate the difference between the slow feedback time value and the preset reasonable feedback range as the feedback time deviation value; Calculate the difference between the alternative number value and the required number value as the alternative deviation number value; Generate a performance evaluation rate based on the historical cooperation performance; Sort the performance evaluation rate and the feedback time deviation value respectively to obtain the evaluation rate sorting value and the feedback time deviation sorting value; Calculate the sum value between the evaluation rate sorting value and the feedback time deviation sorting value as the sorting comprehensive value; Arrange in descending order based on the sorting comprehensive value, and select the candidates corresponding to the alternative deviation number value at the forefront of the sorting comprehensive value as the promoted talents; Merge the promoted talents and the alternative talents as the alternative talents.

[0015] By adopting the above technical solution, screening the alternative talents through the performance evaluation rate not only provides more opportunities for the talents, but also brings richer choices for the users. At the same time, it optimizes the resource structure of the talent library and realizes the efficient matching of talents and user needs.

[0016] Optionally, after selecting the candidates corresponding to the alternative deviation number value at the forefront of the sorting comprehensive value as the promoted talents, it further includes: Obtain the input style entered by the designer or user; Retrieve the wearing frequency, matching style, and comfort level based on the promoted talents; Generate a resource style based on the wearing frequency, matching style, and comfort level; Calculate the similarity score between the resource style of each promoted talent and the input style through a preset style matching algorithm; Determine whether the similarity score is less than a preset similarity benchmark score; If the similarity score is less than the preset similarity benchmark score, calculate the difference between the similarity score and the similarity benchmark score as the similarity deviation value; Select promoted talents based on the similarity deviation value and the wearing frequency to serve as new promoted talents; If the similarity score is not less than the preset similarity benchmark score, continue to output the promoted talents.

[0017] By adopting the above technical solution, by obtaining the input style of the user and optimizing the talent library according to the matching between the resource style and the input style, it can not only provide users with more personalized talent recommendations but also enhance the user experience through personalized customization and decision-making support.

[0018] Optionally, after merging the promoted talents and the alternative talents to serve as the standby talents, it further includes: Retrieve the initial platform weight ratio and the real-time live streaming sales conversion ratio based on the standby talents; Generate a demand weight ratio based on the live streaming sales conversion ratio within a preset unit time; Determine whether the demand weight ratio is consistent with the preset benchmark weight ratio; If they are consistent, no adjustment is made; If they are not consistent, calculate the difference between the demand weight ratio and the initial platform weight ratio as the weight difference; Retrieve the standby talent number value based on the standby talents; Calculate the difference between the standby talent number value and the demand talent number value as the standby deviation talent number value; Generate supplementary talents based on the weight difference and the standby deviation talent number value, and add the supplementary talents to the standby talents.

[0019] By adopting the above technical solution, real-time monitoring and analysis are carried out on data such as the live streaming, work release, and audience interaction of talents. It helps users predict the future development potential and sales ability of talents through the analysis of historical data and trends, and provides more powerful support for users' decision-making by adjusting the weight ratios of talents on each platform.

[0020] Optionally, it further includes: Generate research directions and key research points based on the input style; Generate the research scope based on the research directions and key research points; Generate research data based on the research scope and fashion design factors of the requirements; Generate research conclusions based on the research data; Generate design recommendation directions based on the research conclusions; Generate recommended influencers based on the design recommendation directions, and retrieve the recommended influencers from the influencer resource library for output recommendation.

[0021] By adopting the above technical solutions, data support and more accurate design suggestions are provided for designers, helping users quickly gain insights into market trends and consumer needs, ensuring that the recommended influencer resources can accurately hit the current consumption hotspots, and ensuring the maximum effect of content dissemination.

[0022] In a second aspect, the present application provides an influencer library creation system, adopting the following technical solutions: An influencer library creation system includes an acquisition module for acquiring the style selection resource databases of various platforms, invitation permission situations, feedback time values, and input styles; A memory for storing the program of a method for creating an influencer library based on fashion design factors as described in any one of the first aspect; A processor capable of loading and executing the program in the memory by the processor.

[0023] In a third aspect, the present application provides a terminal, adopting the following technical solutions: A terminal includes a memory and a processor, and a computer program is stored on the memory and can be loaded and executed by the processor to perform a method for creating an influencer library based on fashion design factors as described in any one of the first aspect above.

[0024] In summary, the present application includes at least one of the following beneficial technical effects: By selecting suitable influencers on various platforms to create an influencer library and forming influencer library resources, breaking the platform boundaries, designers and users can greatly reduce the time and effort spent on obtaining inspiration and market research on numerous platforms through the influencer library resources, and can more accurately capture market demands, thereby improving design efficiency; By evaluating the influencers in the influencer library and deciding whether the influencers in the database need to be improved based on their performance, data changes, etc., ensuring the quality of the influencers in the influencer library and guaranteeing more high-quality influencer resources for the platform and partners; By obtaining the input style of the user and optimizing the influencer library according to the matching of the resource style and the input style, not only can more suitable influencer recommendations be provided for the user, but also the user experience can be enhanced through personalized customization and decision support. Brief Description of the Drawings

[0025] Figure 1 is a flowchart of a method for creating a talent database based on fashion design factors according to an embodiment of the present invention; Figure 2 is a flowchart of a method for selecting a fashion design factor based on the talent type and requirements to select a selection resource database to create and form a talent resource database according to an embodiment of the present invention; Figure 3 is a flowchart of a method for selecting preliminary candidates according to an embodiment of the present invention; Figure 4 is a flowchart of a method after generating the talent suitability according to an embodiment of the present invention; Figure 5 is a flowchart of a method for generating alternative candidates according to an embodiment of the present invention; Figure 6 is a flowchart of a method after selecting the number of candidates corresponding to the alternative deviation number value at the forefront of the selection ranking comprehensive value as promoted candidates according to an embodiment of the present invention. Detailed Description of the Invention

[0026] The present invention will be further described in detail below with reference to the drawings and embodiments.

[0027] A method for creating a talent database based on fashion design factors, through the creation of the talent database, breaks the platform boundaries, promotes the cooperation and resource integration of talents between different platforms. It reduces the time for users to collect market demand-related information, more accurately captures market demand, realizes the efficient matching of talents and user needs, and improves the usage efficiency.

[0028] Refer to Figure 1 , an embodiment of the present invention discloses a method for creating a talent database based on fashion design factors, which includes: Step S100: Obtain the selection resource databases of each platform.

[0029] Each platform refers to the source channels for collecting selection resource data. The selection resource database refers to the collection of data such as clothing styles, design elements, and fashion trends collected from multiple channels such as the above platforms. The selection resource database crawls the resource data of relevant talents on each platform through data and integrates these data into a system for subsequent analysis and use.

[0030] Step S101: Generate the current fashion trend based on the selection resource database.

[0031] A fashion trend refers to the identification of the changing patterns of design elements such as clothing styles, colors, materials, and cuts that are currently preferred by consumers through the analysis of data in the style selection resource database. For example, "loose cuts", "retro styles", "eco-friendly materials", etc.

[0032] Technicians analyze the pictures, text descriptions, and user behavior data in the style selection resource database, such as usage frequency, wearing styles, clothing styles, etc., and input the analysis results into the system. Then, through the system algorithm, the style selection resource database is matched with the current fashion trends. The specific system algorithm is prior art and will not be elaborated here.

[0033] Step S102: Generate consumer demands based on fashion trends.

[0034] Consumer demands refer to the functionality and aesthetics of clothing desired by target consumers derived from fashion trends. Functionality includes the warmth retention, breathability, waterproofness, etc. of clothing; aesthetics includes the color combinations, cut styles, etc. of clothing. Different fashion trends correspond to different consumer demands, which are obtained by querying the consumer demand database. The consumer demand database pre-stores a comparison table of consumer demands corresponding to different fashion trends, and the consumer demand database is obtained by operators recording the consumer demands corresponding to different fashion trends.

[0035] Step S103: Generate influencer types and demand clothing design factors based on consumer demands.

[0036] Influencer types refer to the classification of influencers who can effectively promote clothing matched according to consumer demands, such as fashion influencers, sports influencers, etc. Demand clothing design factors refer to the key elements of clothing design extracted according to consumer demands, such as colors, materials, cuts, patterns, etc. Influencer types and demand clothing design factors are generated by extracting the frequently occurring style features in consumer demands and the influencers wearing these styles. For example, when the consumer demand is for sports, the corresponding influencer type is a sports influencer, and the corresponding clothing design factors are breathability, elasticity, etc.; when the consumer demand is for fashion, the corresponding influencer type is a fashion influencer, and the corresponding clothing design factors are novel styles, special design elements, etc.

[0037] Step S104: Select from the style selection resource database based on influencer types and demand clothing design factors to create an influencer resource database.

[0038] The talent resource library refers to the set of cooperative talents finally determined by screening suitable talents according to talent types and demand clothing design factors, including the historical cooperation performance and industry reputation of the talents. The formed talent resource library can be viewed and referenced by users. Match the relevant content in the database with the talent types and demand clothing design factors, and retrieve and form the talent resource library for the successfully matched talent types. This not only improves the screening efficiency but also uncovers talents that are easily missed by traditional methods, making the talent recommendations in the talent resource library more accurate.

[0039] Refer to Figure 2 , a method for creating a talent library based on clothing design factors, further includes selecting from the style selection resource database according to the talent types and demand clothing design factors to create and form a talent resource library, with the following steps: Step S200: Select from the style selection resource database based on the talent types and demand clothing design factors to form preliminary selected talents.

[0040] Preliminary selected talents refer to potential cooperation partners screened from the style selection resource database that meet specific talent types and demand clothing design factors. The preliminary selected talents are selected from the style selection resource database through keyword matching, talent types, and demand clothing design factors. This not only improves efficiency but also makes the summarized data more representative.

[0041] Step S201: Generate the source platforms of the preliminary selected talents based on the style selection resource databases of each platform.

[0042] The source platform refers to the e-commerce platform or social media platform where the preliminary selected talents are located. The source platforms of different preliminary selected talents are different. The source platforms of the preliminary selected talents are obtained by querying the preset source platform database. The source platform database pre-stores a comparison table of different preliminary selected talents and their corresponding source platforms of the preliminary selected talents. By automatically querying and recording the style selection resource databases of different platform information in sequence, the source platform database is formed.

[0043] Step S202: Obtain the invitation permission status based on the source platform.

[0044] The invitation permission status refers to the permissions and licenses required to invite talents to cooperate on the source platform, including but not limited to platform rules, talent cooperation policies, etc. These information are obtained through the back-end management of the source platform to ensure the compliance and effectiveness of the invitation process.

[0045] Step S203: Generate candidate talents based on the invitation permission status.

[0046] The candidate influencers refer to those influencers who can be further invited to cooperate under the condition of meeting the invitation permission. First, extract from the preliminary selected influencers those who clearly indicate that they allow the brand side or the cooperation side to send cooperation invitations, and automatically filter out those who have set "do not accept new cooperation invitations" or "only accept invitations from specific brands". Through these factors, finally screen out the candidate influencers who meet the invitation permission to form a list of influencers who can be further contacted and cooperated with to generate candidate influencers. This process ensures that influencers who meet the requirements and are willing to cooperate can be found efficiently, thereby improving the success rate and efficiency of cooperation.

[0047] Step S204: Based on the candidate influencers, sequentially send preset invitation information to obtain feedback time values.

[0048] The invitation information refers to the invitation content pre-designed according to the cooperation requirements, including project details, cooperation requirements, and expected feedback time values, etc. The invitation information is pre-set. Send invitations to the candidate influencers sequentially through an automated script, and record the feedback time of each influencer. The automated script is prior art and will not be elaborated here.

[0049] Step S205: Select the candidate influencers with feedback time values within the preset reasonable feedback range as alternative influencers, and define the feedback time values outside the preset reasonable feedback range as slow feedback time values.

[0050] The reasonable feedback range refers to the reasonable response time interval set according to industry standards and historical data, and the reasonable feedback range is pre-set. The slow feedback time value refers to the feedback time value that exceeds the preset reasonable feedback range. The candidate influencers with feedback time values within the reasonable feedback range are selected as alternative influencers, while the feedback time values outside this range are defined as slow feedback time values.

[0051] Step S206: Based on the alternative influencers, retrieve the alternative number value.

[0052] The alternative number value refers to the number of current alternative influencers. By counting the alternative influencers and taking the result of the count as the alternative number value to evaluate whether the cooperation requirements are met.

[0053] Step S207: Judge whether the alternative number value meets the preset required number value.

[0054] The required number value refers to the minimum number of cooperating influencers set in the plan, and the required number value is pre-set. By comparing the alternative number value with the required number value, judge whether further screening or supplementing of the influencer quantity is needed.

[0055] Step S208: If so, retrieve from the product selection resource database based on the alternative influencers to form an influencer resource database.

[0056] When the number of alternative talents meets the number of required talents, there is no need for further screening or supplementing the number of talents. Retrieve the detailed information of the alternative talents from the product selection resource database to form a complete talent resource database.

[0057] Step S209: If the answer is no, generate candidate talents based on the number of alternative talents and the feedback slow time value, and retrieve from the product selection resource database based on the candidate talents and alternative talents to form a talent resource database.

[0058] Candidate talents refer to the supplementary talents screened according to the feedback slow time value when the number of alternative talents is insufficient. When the number of alternative talents does not meet the number of required talents, it is necessary to further screen or supplement the number of talents, dynamically adjust the number of candidate talents according to the difference between the number of required talents and the number of alternative talents, and verify the rationality of the feedback time (such as whether the feedback time value of the candidate talents is within the acceptable range) to ensure that the number after supplementation meets the requirements. Finally, retrieve the alternative talents and candidate talents that pass the integration verification from the product selection resource database to form a talent resource database.

[0059] Refer to Figure 3 , A method for creating a talent database based on clothing design factors, further including the following steps for selecting preliminary talents: Step S300: Retrieve the activity status, professional skills, and clothing styles of talents from the product selection resource database based on the talent type.

[0060] The activity status refers to the activity frequency and interaction data of the talent on their platform, such as the frequency of posting content, the audience interaction rate, etc. Professional skills refer to the fields that the talent is good at, such as fashion matching, fabric evaluation, etc. Clothing style refers to the types and styles of clothing that the talent usually shows, such as casual style, Chinese new national style, etc. By retrieving the activity status, professional skills, and clothing styles of the talents corresponding to the talent type from the product selection resource database, it is convenient for subsequent use.

[0061] Step S301: Generate talent influence based on the activity status.

[0062] Talent influence refers to the magnitude of the influence of the talent on their audience, usually measured by the activity status. The activity status includes the frequency of posting content, the number of audiences, the interaction rate (such as likes, comments, shares), etc. Calculate the talent influence through the following formula: Influence = a × number of audiences + b × interaction rate + c × posting frequency, where a, b, and c are weight coefficients, and the specific coefficients are preset by the operator based on historical data and industry standards.

[0063] Step S302: Generate talent suitability based on talent influence and professional skills.

[0064] The suitability degree of a talent refers to the degree value indicating whether a talent is suitable for promoting a specific type of clothing. The suitability degree of a talent is obtained by querying a preset talent suitability database. The talent suitability database pre-stores a comparison table of different talent influences and professional skills and their corresponding suitability degrees, and the talent suitability database is formed after the operator sequentially analyzes and records the influences and professional skills of different talent information.

[0065] Step S303: Generate a required style based on the clothing design factors of the requirement.

[0066] The required style refers to the clothing style characteristics extracted according to the consumer requirements, such as "retro style", "sports casual style", "eco-fashion style", etc. The required style is obtained by querying a preset required style database. The required style database pre-stores a comparison table of different clothing design factors of the requirement (such as color, material, cutting, pattern) and their corresponding required styles, and the required style database is formed after the operator sequentially analyzes and records different clothing design factors of the requirement.

[0067] Step S304: Compare the clothing style and the required style to obtain a style matching degree.

[0068] The style matching degree refers to the matching degree between the clothing style of a talent and the required style. The style matching degree is determined by calculating the similarity between the two. First, the clothing style and the required style are respectively disassembled into basic characteristics such as color, style, material, pattern, etc., and then the similarity of these characteristics is compared one by one (for example, both use black color system, both contain floral patterns, etc.), and then weights are assigned according to the importance of different characteristics (such as color weight 30%, style weight 40%). Finally, the matching scores of each characteristic are weighted and added to obtain a total score. The higher the total score, the higher the style matching degree.

[0069] Step S305: Generate a talent-clothing matching degree based on the talent suitability degree and the style matching degree.

[0070] The talent-clothing matching degree refers to whether a talent is suitable for promoting clothing of a specific style, and is determined by comprehensively considering the talent suitability degree and the style matching degree. The matching degree calculation formula is as follows: matching degree = m × suitability degree + n × style matching degree, where m and n are weight coefficients, and the specific coefficients are preset by the operator according to historical data and industry standards.

[0071] Step S306: Select talents from the style selection resource database based on the talent-clothing matching degree to form a preliminary selected talent.

[0072] The preliminary selected talents refer to the preliminary selected cooperation objects selected from the selection resource database. By setting the matching degree, talents with a matching degree higher than the set matching degree are selected as the preliminary selected talents. For example, the matching degree is set to 0.8, and talents with a matching degree higher than 0.8 will be selected as preliminary selected talents. In this embodiment, qualified talents are extracted from the database through an automated screening algorithm to ensure that the quality and quantity of the preliminary selected talents meet the promotion needs. The automated screening algorithm is a technology that automatically analyzes, matches and sorts target objects through a computer program. It is a prior art and will not be described in detail.

[0073] Reference Figure 4 , a method for creating a talent database based on clothing design factors, further comprising the following steps after generating talent suitability: Step S400: retrieve the expert's historical cooperation performance and industry reputation from the expert resource library.

[0074] Historical cooperation performance refers to the comprehensive capabilities, performance quality and reliability records of specific parties (such as suppliers, partners, influencers, etc.) in the past cooperation process. Specifically, it includes the number of projects cooperated, the duration of cooperation, the effect of cooperation, etc. Industry reputation refers to the public evaluation and reputation of a company, individual or brand in a specific industry. Historical cooperation performance and industry reputation technicians achieve this by checking the social media evaluation of influencers, discussions on industry forums, and directly asking brands they have cooperated with.

[0075] Step S401: Generate preliminary evaluation results based on historical cooperation performance.

[0076] The preliminary evaluation results refer to the evaluation conclusions drawn by analyzing the historical cooperation performance of the experts. They include the success rate of cooperation, the completion of the project, whether it is delivered on time, whether the content quality is stable, etc. The preliminary evaluation results are obtained by querying the preset historical cooperation performance database. The historical cooperation performance database pre-stores a comparison table of different expert information and the corresponding historical cooperation performance. The historical cooperation performance database is obtained by the operator analyzing the historical cooperation data of different expert information to determine whether their performance in historical cooperation is satisfactory.

[0077] Step S402: Generate content creation quality based on the preliminary evaluation results.

[0078] The quality of content creation refers to the overall performance of the content output by the influencer, including aspects such as professionalism, visual effects, and interaction effects. It can be evaluated through multiple indicators, such as the production level of the video, the creativity of the content, and the writing ability. One can refer to the content created by the influencer in past collaborations, view the interaction data on social media (such as the number of likes, comments, and shares), as well as indicators such as the viewing duration and completion rate of the content, to comprehensively judge the quality of content creation. The method of comprehensive judgment is as follows: Comprehensive judgment score = α × Professionalism score + β × Visual effect score + γ × Interaction effect score, where α, β, and γ are weight coefficients, and the specific coefficients are preset by the operator based on historical data and industry standards. Through this method, the quality of the influencer's content creation can be systematically evaluated.

[0079] Step S403: Generate a comprehensive evaluation index based on the quality of content creation and industry reputation.

[0080] The comprehensive evaluation index refers to the index value obtained after comprehensively evaluating the content quality and reputation. Combining the information on the quality of content creation and industry reputation, a comprehensive evaluation index system is established. This system can include content quality scores and reputation scores. Through weighted average or other statistical methods, these dimensions are integrated into a comprehensive evaluation index to comprehensively reflect the overall performance of the influencer. The calculation formula for the comprehensive evaluation index is as follows: Comprehensive evaluation index = δ × Quality score + ε × Reputation score, where δ and ε are weight coefficients, and the specific coefficients are preset by the operator based on historical data and industry standards.

[0081] Step S404: When the comprehensive evaluation index meets the preset comprehensive evaluation criteria, continue to output the suitability of the influencer.

[0082] If the comprehensive evaluation index of the influencer reaches the preset standard, it indicates that the influencer has high suitability. At this time, the suitability of the influencer, that is, its suitability in a specific cooperation project, can be continued to be output to provide a reference for subsequent cooperation decisions.

[0083] Step S405: When the comprehensive evaluation index does not meet the preset standard, retrieve the improvement plan and resource investment situation of the influencer from the influencer resource library.

[0084] The improvement plan refers to the specific strategies and measures formulated to enhance the performance of the talent. It usually includes the analysis results of the deficiencies in the comprehensive evaluation indicators of the talent, the planning of improvement directions, and the implementation steps. The resource investment situation refers to the resources willing to be invested to implement these improvement plans, including time, energy, and funds. If the comprehensive evaluation indicators of the talent do not meet the preset standards, it means that there are some areas that need improvement. At this time, retrieve the improvement plan of this talent from the talent resource library to understand the strategies and measures formulated by it to improve performance. At the same time, check the talent's situation in terms of resource investment, such as whether it is willing to invest more time, energy, or funds to improve the cooperation effect.

[0085] Step S406: Generate the index improvement effect based on the improvement plan and the resource investment situation.

[0086] The index improvement effect refers to determining whether these improvement measures can effectively improve the comprehensive evaluation indicators of the talent by evaluating whether the improvement plan of the talent is feasible and whether the resource investment is sufficient. The index improvement effect is obtained by querying the preset improvement effect database. The improvement effect database pre-stores a comparison table of different improvement plans and resource investment situations and the corresponding index improvement effects, and the improvement effect database is formed after the operator analyzes and registers different improvement plans and resource investment situations item by item.

[0087] Step S407: Adjust and replace the suitability of the talent based on the index improvement effect.

[0088] According to the evaluation result of the index improvement effect, the suitability of the talent is adjusted accordingly. Adjust the suitability of the talent according to the index improvement effect, and determine whether to continue cooperation by judging whether the improvement is effective.

[0089] Refer to Figure 5 , a method for creating a talent library based on fashion design factors, further includes the following steps for generating alternative talents: Step S500: Calculate the difference between the slow feedback time value and the preset reasonable feedback range as the feedback time deviation value.

[0090] The reasonable feedback range refers to the reasonable difference between the slow feedback time value and the preset reasonable feedback range, which is a reasonable reply time interval set according to industry standards or historical data, such as within 7 working days. It is obtained by direct calculation.

[0091] Step S501: Calculate the difference between the alternative number value and the required number value as the alternative deviation number value.

[0092] The alternative deviation number value refers to the difference between the alternative number value and the required number value, which is used to evaluate whether additional influencers need to be supplemented. Through calculation, the system can systematically evaluate whether the current number of alternative influencers meets the requirements and provide a basis for subsequent influencer supplementation or adjustment.

[0093] Step S502: Generate a performance evaluation rate based on historical cooperation performance.

[0094] The performance evaluation rate refers to an evaluation index calculated by analyzing the historical cooperation data of influencers, which is used to measure the performance of influencers in past collaborations. The performance evaluation rate is calculated, and its calculation formula is: Performance evaluation rate = Number of successful collaborations / Total number of collaborations × 100%. The number of successful collaborations and the total number of collaborations are obtained by retrieving the influencer resource database.

[0095] By analyzing the historical cooperation data of influencers, calculate their performance evaluation rates to ensure the objectivity and accuracy of the evaluation.

[0096] Step S503: Sort the performance evaluation rate and the feedback time deviation value respectively to obtain the evaluation rate sorting value and the feedback time deviation sorting value.

[0097] The evaluation rate sorting value refers to the ranking value corresponding to each influencer after sorting the performance evaluation rate from high to low. The feedback time deviation sorting value refers to the ranking value corresponding to each influencer after sorting the feedback time deviation value from small to large. Sort these two indicators respectively through a sorting algorithm to obtain the sorting value of each influencer.

[0098] Step S504: Calculate the sum value between the evaluation rate sorting value and the feedback time deviation sorting value and use it as the comprehensive sorting value.

[0099] The comprehensive sorting value refers to the sum value of the evaluation rate sorting value and the feedback time deviation sorting value, which is used to comprehensively evaluate the performance and feedback efficiency of influencers. By summing the evaluation rate sorting value and the feedback time deviation, the two sorting values are integrated into a comprehensive value to ensure the comprehensiveness of the evaluation.

[0100] Step S505: Perform a descending order based on the comprehensive sorting value, and select the number of candidate influencers corresponding to the alternative deviation number value at the forefront of the comprehensive sorting value as the promoted influencers.

[0101] Promoted influencers refer to the influencers who can enter the next judgment link obtained through the screening of the above steps. After arranging them from high to low according to the comprehensive sorting value, select the number of candidate influencers corresponding to the alternative deviation number value. Through the descending order, ensure that the selected promoted influencers have high comprehensive performance and feedback efficiency.

[0102] Step S506: Combine the promoted influencers with the alternative influencers to serve as the candidate influencers.

[0103] By merging the promoted talents with the alternative talents, supplementation is carried out when the number of alternative talents is insufficient, ensuring the integrity and flexibility of the talent resource library.

[0104] Refer to Figure 6 , a method for creating a talent library based on clothing design factors, further includes the following steps after selecting the corresponding number of candidate talents corresponding to the value of the alternative deviation number at the forefront of the selection ranking comprehensive value as the promoted talents: Step S600: Obtain the input style entered by the designer or user.

[0105] The input style refers to the expected clothing style of the designer or user through the interface, such as "retro style", "sports casual style", "fashion style", etc. The input style can be obtained through methods such as text input boxes or style template selections.

[0106] Step S601: Retrieve the wearing frequency, matching method, and comfort level based on the promoted talents.

[0107] The wearing frequency refers to the frequency of the promoted talents wearing clothing of a specific style, obtained by retrieving the talent resource database. The matching method refers to how the promoted talents match their clothing, such as color matching, dressing style, etc. The comfort level refers to the evaluation of the comfort of the clothing by the promoted talents, usually obtained through questionnaire surveys or comment analyses. Retrieving this information through the talent resource database ensures the comprehensiveness and accuracy of the data.

[0108] Step S602: Generate a resource style based on the wearing frequency, matching method, and comfort level.

[0109] The resource style refers to the clothing style characteristics comprehensively evaluated based on the wearing frequency, matching method, and comfort level of the promoted talents. Calculate the weight of the resource style through the following formula: Resource style weight = e × wearing frequency + f × matching method score + g × comfort level score, where e, f, and g are weight coefficients, and the specific coefficients are preset by the operator according to historical data and industry standards. The wearing frequency reflects the preference degree of the talent for a certain style; the matching method reflects the wearing habits of the talent; the comfort level reflects the acceptance degree of the talent for the clothing material. Through linear calculation based on the preset weight coefficients (α, β, γ) corresponding to the wearing frequency, matching method, and comfort level, a unified and quantified resource style weight is finally formed. Then, by inputting the resource style weight into the preset resource style database, the resource style is obtained by matching. Different resource styles correspond to different wearing frequencies, matching methods, and comfort levels. The resource style is obtained by querying the resource style database. The resource style database pre-stores a comparison table of different resource styles, and the resource style database is obtained through pre-input by the operator.

[0110] Step S603: Calculate the similarity score between the resource style of each promoted talent and the input style through a preset style matching algorithm.

[0111] The similarity score is an evaluation index calculated through the style matching algorithm, which is used to measure the matching degree between the resource style of the promoted talent and the input style. The style matching algorithm is preset. By splitting both the resource style and the input style into small pieces such as color, style, and material, then comparing the similarity of each piece, marking the importance according to the similarity degree, and finally adding up the importance of all pieces. The higher the total score, the more similar the two styles are.

[0112] Step S604: Determine whether the similarity score is less than the preset similarity benchmark score.

[0113] The similarity benchmark score is the minimum similarity value set according to industry standards or historical data. By comparing the similarity score with the similarity benchmark score, it is judged whether further processing is required.

[0114] Step S605: If the similarity score is less than the preset similarity benchmark score, calculate the difference between the similarity score and the similarity benchmark score as the similarity deviation value.

[0115] The similarity deviation value is the difference between the similarity score and the benchmark score. By calculating the similarity deviation value, the degree of insufficient similarity is evaluated, providing a basis for subsequent adjustment.

[0116] Step S606: Select promoted talents based on the similarity deviation value and wearing frequency as the new promoted talents.

[0117] The similarity deviation value and wearing frequency are obtained from the talent resource database. The comprehensive score is calculated through the following formula: Comprehensive score = p × similarity deviation value + q × wearing frequency, where p and q are weight coefficients, and the specific coefficients are preset by the operator according to historical data and industry standards. Sort the comprehensive scores from low to high, and select the talent with the lowest score as the new promoted talent to ensure the improvement of the style matching degree.

[0118] Step S607: If the similarity score is not less than the preset similarity benchmark score, continue to output the promoted talents.

[0119] When the similarity score is not less than the preset similarity benchmark score, it means that the promoted talents are qualified. By updating the database, continue to output these promoted talents.

[0120] A method for creating a talent database based on clothing design factors further includes the following steps after merging the promoted talents and alternative talents as candidate talents: Step S700: Retrieve the initial platform weight ratio and the real-time live streaming sales conversion ratio based on the candidate influencers.

[0121] The initial platform weight ratio refers to the proportion of the number of candidate influencers on different platforms under initial conditions. The real-time live streaming sales conversion ratio refers to the actual sales conversion rate achieved by candidate influencers during the live stream, which is obtained through the influencer resource library.

[0122] Step S701: Generate a demand weight ratio based on the live streaming sales conversion ratio within a preset unit of time.

[0123] The demand weight ratio is the target weight set according to the live streaming sales conversion ratio within a preset unit of time (such as 24 hours). The demand weight ratio is obtained by querying a preset weight ratio database. The weight ratio database pre-stores a comparison table of different live streaming sales conversion ratios and their corresponding demand weight ratios within a unit of time. The weight ratio database is formed by the operator's sequential analysis and recording of different live streaming sales conversion ratios within a unit of time. The demand weight ratio is determined through data analysis and model prediction to ensure the scientificity and feasibility of the target.

[0124] Step S702: Determine whether the demand weight ratio and the preset benchmark weight ratio are both consistent.

[0125] The benchmark weight ratio is a reference weight ratio set according to industry standards or historical data. By comparing the demand weight ratio with the benchmark weight ratio, it is determined whether the two are consistent.

[0126] Step S703: If they are consistent, no adjustment is made.

[0127] If the demand weight ratio and the benchmark weight ratio are consistent, it indicates that the current weight setting can meet the promotion requirements and no further adjustment is needed.

[0128] Step S704: If they are inconsistent, calculate the difference between the demand weight ratio and the initial platform weight ratio and use it as the weight difference.

[0129] If the demand weight ratio and the benchmark weight ratio are inconsistent, it indicates that there is a deviation between the current demand weight ratio and the initial weight ratio, and the weights need to be reallocated.

[0130] Step S705: Retrieve the candidate number value based on the candidate influencers.

[0131] The candidate number value refers to the number of current candidate influencers. By counting the number of candidate influencers and using it as the candidate number value, it is convenient for subsequent use.

[0132] Step S706: Calculate the difference between the candidate number value and the demand number value and use it as the candidate deviation number value.

[0133] The candidate deviation number value refers to the number value corresponding to the situation where the number of candidates does not meet the demand. By calculating the difference between the candidate number value and the demand number value and using it as the candidate deviation number value, it is convenient for subsequent use.

[0134] Step S707: Generate supplementary influencers based on the weight difference and the candidate deviation number value, and add the supplementary influencers to the candidate influencers.

[0135] The supplementary influencers refer to the influencers that need to be supplemented determined according to the weight difference and the candidate deviation number value. Adding the supplementary influencers to the candidate influencers ensures that the quantity and quality of the candidate influencers meet the promotion requirements.

[0136] A method for creating an influencer library based on clothing design factors further includes the following steps: Step S800: Generate a research direction and research focus based on the input style.

[0137] The research direction refers to the direction that needs to be determined before design research. The research focus refers to the key points that need to be grasped in the research direction. Different input styles correspond to different research directions and research focuses. The research direction and research focus are obtained by querying the research database. The research database pre-stores a comparison table of different input styles and the corresponding research directions and research focuses, and the research database is obtained by the operator's pre-input.

[0138] Step S801: Generate a research scope based on the research direction and research focus.

[0139] The research scope refers to the specific data range required for conducting research determined according to the research direction and focus. Different research directions and research focuses correspond to different research scopes. The research scope is obtained by querying the research category database. The research category database pre-stores a comparison table of different research directions and research focuses and the corresponding research scopes, and the research direction and research focus are obtained by querying the research database.

[0140] Step S802: Generate research data based on the research scope and the required clothing design factors.

[0141] The research data refers to the specific data collected within the research scope, including consumer preferences, market trends, successful cases, etc. In this embodiment, the research data is collected through data crawling, API calls, and questionnaires to ensure the accuracy and timeliness of the data.

[0142] Step S803: Generate research conclusions based on the research data.

[0143] A research conclusion refers to the conclusion extracted from research data. Different research data correspond to different research conclusions. The research conclusion is obtained by querying the research conclusion database. The research conclusion database pre-stores a comparison table of different research conclusions, and the research data is obtained through operator input.

[0144] Step S804: Generate a design recommendation direction based on the research conclusion.

[0145] A design recommendation direction refers to a reference research direction recommended for users during design based on the research conclusion. Different research conclusions correspond to different design recommendation directions. The design recommendation direction is obtained by querying the design recommendation direction database. The design recommendation direction database pre-stores a comparison table of different recommendation directions, and the research conclusion is obtained through operator input.

[0146] Step S805: Generate recommended influencers based on the design recommendation direction, and retrieve the recommended influencers from the influencer resource library for output recommendation.

[0147] A recommended influencer refers to the most suitable influencer for promotion selected from the influencer resource library according to the design direction. By matching the tags of the recommendation direction with the preset tags in the influencer resource library. For example, if the design direction is "Chinese new national style", the recommended influencers include national style influencers and promoters of historical elements. In this embodiment, the recommended influencers are extracted from the influencer resource library through an automated screening algorithm and output through an API interface to ensure the accuracy and efficiency of the recommendation.

[0148] Based on the same inventive concept, an embodiment of the present invention provides a clothing design factor-based system, including: An acquisition module for acquiring the selected style resource database, invitation permission status, feedback time value, and input style of each platform; A memory for storing a program, such as the program of a method for creating an influencer library based on clothing design factors as described above; A processor capable of loading and executing the program in the memory.

[0149] Based on the same inventive concept, an embodiment of the present invention provides a terminal, including a memory and a processor, and a computer program stored on the memory can be loaded and executed by the processor, such as a method for creating an influencer library based on clothing design factors as described above.

[0150] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. For the specific working processes of the system, device, and unit described above, reference can be made to the corresponding processes in the foregoing method embodiments, which will not be elaborated here.

[0151] The above is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A method for creating a talent database based on clothing design factors, characterized in that: include: Obtain the selection resource database of each platform; Generate current fashion trends based on the selection resource database; Generate consumer demand based on popular trends; Generate talent types and demand clothing design factors based on consumer demand; Based on the talent type and required clothing design factors, the selection resource database is selected to create an talent resource library.

2. A method for creating a talent database based on clothing design factors according to claim 1, characterized in that: Based on the talent type and the required clothing design factors, the selection resource database is selected to create a talent resource library including: Based on the talent type and the required clothing design factors, the selection resource database is selected to form the preliminary selection talents; Generate the source platform of preliminary selection talents based on the selection resource database of each platform; Get invitation permission based on the source platform; Generate candidates based on invitation permission; Sending preset invitation information to the selected experts in turn to obtain feedback time values; Select candidates whose feedback time values ​​are within the preset reasonable feedback range as candidate candidates, and define the feedback time values ​​that are not within the preset reasonable feedback range as the slow feedback time value; Retrieve the candidate values ​​based on the candidate experts; Determine whether the candidate number meets the preset required number; If yes, then based on the candidate talents, the selection resource database is retrieved to form a talent resource library; If not, a candidate expert is generated based on the candidate value and the feedback slow time value, and the selection resource database is retrieved based on the candidate expert and the candidate expert to form an expert resource library.

3. A method for creating a talent database based on clothing design factors according to claim 2, characterized in that: The selection method for preliminary candidates includes: Based on the type of talent, retrieve the talent's activity status, professional skills and clothing style from the selection resource database; Generate influence of experts based on their activity; Generate talent suitability based on talent influence and professional skills; Generate demand styles based on demand clothing design factors; Compare the clothing style with the required style to get the style fit; Generate the matching degree of the expert's clothing based on the expert's suitability and style fit; Experts are selected from the selection resource database based on the matching degree of their clothing to form preliminary experts.

4. A method for creating a talent database based on clothing design factors according to claim 3, characterized in that: It also includes the steps after generating the talent suitability: Retrieve the influencer’s historical cooperation performance and industry reputation from the influencer resource library; Generate preliminary assessment results based on historical cooperation performance; Generate content creation quality based on preliminary assessment results; Generate comprehensive evaluation indicators based on content creation quality and industry reputation; When the comprehensive evaluation indicators meet the preset comprehensive evaluation standards, continue to output the talent suitability; When the comprehensive evaluation indicators do not meet the preset standards, the improvement plan and resource investment of the expert are retrieved from the expert resource library; Generate indicators based on improvement plans and resource inputs to improve results; Based on the indicator improvement effect, the suitability of the experts can be adjusted and replaced.

5. A method for creating a talent database based on clothing design factors according to claim 4, characterized in that: Methods for generating candidate experts include: Calculate the difference between the feedback slow time value and the preset feedback reasonable range and use it as the feedback time deviation value; Calculate the difference between the candidate number and the required number and use it as the candidate deviation number; Generate performance evaluation rates based on historical collaboration performance; The performance evaluation rate and the feedback time deviation value are sorted respectively to obtain an evaluation rate ranking value and a feedback time deviation ranking value; Calculate the sum of the evaluation rate ranking value and the feedback time deviation ranking value and use it as the ranking comprehensive value; Arrange in descending order based on the ranking comprehensive value, and select the candidates with the number of candidates corresponding to the candidate deviation value at the front of the ranking comprehensive value as the promoted candidates; The qualified talents will be combined with the reserve talents to form the reserve talents.

6. A method for creating a talent database based on clothing design factors according to claim 5, characterized in that: After the candidates with the number of candidates corresponding to the value of the candidate deviation at the front of the selection ranking comprehensive value are selected as the promoted candidates, the following are also included: Get the input style entered by the designer or user; Based on the promotion experts, the frequency of wearing, matching methods and comfort level are retrieved; Generate resource styles based on wearing frequency, matching methods and comfort level; The similarity score between the resource style of each qualified talent and the input style is calculated through the preset style matching algorithm; Determine whether the similarity score is less than a preset similarity benchmark score; If the similarity score is less than the preset similarity benchmark score, the difference between the similarity score and the similarity benchmark score is calculated and used as the similarity deviation value; Based on the similarity deviation value and wearing frequency, the promotion experts are selected and used as new promotion experts; If the similarity score is not less than the preset similarity benchmark score, the promoted experts will continue to be output.

7. A method for creating a talent database based on clothing design factors according to claim 6, characterized in that: After the qualified talents are merged with the candidates as the reserve talents, the following will also be included: Based on the candidate influencers, the initial platform weight ratio and real-time live streaming sales conversion ratio are retrieved; Generate a demand weight ratio based on the preset live streaming sales conversion ratio per unit time; Determine whether the demand weight ratio is consistent with the preset benchmark weight ratio; If they are consistent, no adjustment is made; If they are inconsistent, calculate the difference between the demand weight ratio and the platform initial weight ratio and use it as the weight difference; Retrieve candidate values ​​based on candidate experts; Calculate the difference between the candidate number and the required number and use it as the candidate deviation number; A supplementary expert is generated based on the weight difference and the candidate deviation value, and the supplementary expert is added to the candidate experts.

8. The method for creating a talent database based on clothing design factors according to claim 1, characterized in that: Also includes: Generate research directions and research focuses based on input style; Generate the research scope based on the research direction and research focus; Generate survey data based on survey scope and demand clothing design factors; Generate research conclusions based on research data; Generate design recommendations based on research conclusions; Generate recommendation experts based on the design recommendation direction, and retrieve recommendation experts based on the expert resource library to output recommendations.

9. A talent database creation system based on clothing design factors, characterized in that: include: The acquisition module is used to obtain the selection resource database, invitation permission status, feedback time value and input style of each platform; A memory for storing a program of a method for creating a talent library based on clothing design factors according to any one of claims 1 to 8; The program in the memory can be loaded and executed by the processor.

10. A terminal, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes a method for creating a talent library based on clothing design factors as described in any one of claims 1 to 8.

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