Direct broadcasting room automatic operation method, system and equipment using AIGC technology

Abstract

The invention discloses a live broadcast room automatic operation method, system and equipment using an AIGC technology, and relates to the technical field of live broadcast operation, the system comprises a data acquisition unit, an AIGC operation unit, a resource scheduling unit and a terminal display unit, AIGC operation data is monitored through the data acquisition unit, NLP emotion analysis and user behavior modeling are carried out through the AIGC operation unit, and the AIGC operation unit is used for monitoring the AIGC operation data. The user value level is predicted in real time, and an optimal operation recommendation strategy is generated, so that dynamic strategy adjustment is supported, data real-time analysis and decision making are realized, and AIGC-driven automatic content is optimized; the resource scheduling unit is combined with the cost and income model to realize automatic optimal configuration of hardware resources, and the terminal display unit is used for AIGC live broadcast display and multi-terminal interaction optimization, so that the rendering resolution of each terminal is dynamically adjusted, the user experience and the cost control are considered, the service quality is ensured, and the operation cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of live broadcast operation technology, and in particular to a method, system and device for automated operation of a live broadcast room using AIGC technology. Background Art

[0002] The AIGC technology live broadcast room refers to a virtual live broadcast room created using artificial intelligence generated content (AIGC) technology. It uses artificial intelligence technology to drive virtual characters to conduct live broadcast activities. It can simulate human voice, expressions and movements to conduct live broadcasts, reports and performances. These virtual anchors can broadcast live 24 hours a day, saving labor costs.

[0003] However, traditional live broadcast operations lack real-time, multi-dimensional data analysis capabilities, making it difficult to accurately capture users' dynamic needs. They also suffer from extensive resource allocation. Traditional recommendation strategies are based on static tags and cannot dynamically match users' real-time behavior with the suitability of live broadcast content, such as click frequency and emotional tendencies. This leads to data processing lags and insufficient personalization. Furthermore, hardware resource configurations are fixed and cannot be dynamically adjusted based on real-time costs and user value levels. This can cause unstable services for high-value users or waste of resources. In view of the above technical defects, a solution is now proposed. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of traditional AIGC live broadcast room operations, such as lack of real-time multi-dimensional data analysis capabilities, difficulty in accurately capturing users' dynamic needs, and extensive resource allocation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: The automated operation method of a live broadcast room using AIGC technology includes the following steps: Step 1: The data collection unit is used to monitor AIGC operation data: AIGC operation data includes user profile parameters, real-time behavior parameters, live content parameters, and system resource parameters; Step 2: The AIGC operation unit is used to analyze AIGC operation data: the AIGC operation unit includes a real-time processing module, a dynamic generation module, and a recommendation and distribution module; A user behavior analysis model is established through the real-time processing module, combining user portrait parameters, real-time behavior parameters, and live content parameters to predict user value levels; Through the dynamic generation module, multimodal fusion of text and voice commands is performed to drive 3D virtual human behavior. By monitoring the fluency of text language, relevance to user interests, historical comparison novelty, and interactive sentiment scores, the AIGC generation quality is evaluated in multiple dimensions to generate the optimal operation recommendation strategy. The recommendation distribution module outputs the recommendation priority of live broadcast room products based on user value level and optimal operation recommendation strategy; Step 3: The resource scheduling unit is used to balance the allocation of AIGC control resources: it analyzes the total cost of live broadcast operations through system resource parameters, dynamically coordinates with the total transaction revenue of the live broadcast room, and outputs a resource elasticity configuration plan; Step 4: The terminal display unit is used for AIGC live broadcast and multi-terminal interaction optimization: adaptive rendering resolution is achieved through resource elastic configuration solution.

[0006] Furthermore, the specific process of establishing a user behavior analysis model to predict user value levels is as follows: Synchronously pre-process the user portrait parameters, real-time behavior parameters, and live content parameters. The specific process is as follows: User profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; historical behavior includes historical average order value and number of purchases; and spending behavior includes spending amount and frequency. Preprocessing user profile parameters: Obtain the consumption level index Clv and category preference probability P(c) through the user profile parameters. Then, combine the consumption level index Clv and category preference probability P(c) to obtain the user profile score F1. Real-time behavior parameters include product page click frequency Cf, live broadcast room stay time Ts, behavior density index Bdi and sentiment tendency value Snt; Preprocessing real-time behavior parameters: Obtain the purchase intention index PI through real-time behavior parameters; obtain the emotional excitement index EH by combining the behavior density index Bdi and the emotional tendency value Snt; obtain the real-time behavior score F2 by combining the purchase intention index PI and the emotional excitement index EH; Live content parameters include product attributes, scene context, user matching, and promotion sensitivity; product attributes include selling price, cost price, size, and material; Preprocessing live content parameters: Obtain user matching degree Um and promotion sensitivity Ps through product attributes and scene context; Obtain live content score F3 by combining user matching degree Um and promotion sensitivity Ps; Then, the user portrait score F1, real-time behavior score F2 and live content score F3 are combined to generate the user value prediction index JZ; Set the evaluation interval of the user value prediction index JZ, determine the user value level by interval comparison and mark it.

[0007] Furthermore, the specific process of preprocessing user portrait parameters is as follows: User profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; historical behavior includes historical average order value and number of purchases; and spending behavior includes spending amount and frequency. Mark the number of all users in the live broadcast room as N0, and calculate the average customer order value based on the historical customer order value of N0 users. and the standard deviation of average order value ; Mark any user as i, and mark the historical average order value of user i as , and thus the historical average order value of user i is calculated through Z-Score Standardize; add up the consumption amounts of N0 users to get the total consumption amount , and then calculate the average value to obtain the average consumption ; Mark the consumption frequency of user i as , and then obtain the consumption level index Clv; By clustering users' historical purchases, we determine the categories of their products. We label the total number of product categories as Nm, any product category as c, the number of purchases by user i in category c as Nc, and the total number of purchases across all categories in the user's history as Ni. We then use Laplace smoothing to correct and calculate the probability P(c) of user i's preference for category c. Then, by combining the consumption level index Clv and the category preference probability P(c), we can obtain the user portrait score F1.

[0008] Furthermore, the specific process of preprocessing real-time behavior parameters is as follows: Real-time behavior parameters include product page click frequency Cf, live broadcast room stay time Ts, behavior density index Bdi and sentiment tendency value Snt; The purchase intention index PI is obtained by weighted summing the product page click frequency Cf, the live broadcast room stay time Ts, the behavior density index Bdi and the emotional tendency value Snt; Through the behavior density index Bdi and sentiment tendency value Snt in the data collection period The emotional excitement index EH is obtained by combining the change rate of The real-time behavior score F2 is obtained by combining the purchase intention index PI and the emotional excitement index EH.

[0009] Furthermore, the specific process of preprocessing live content parameters is as follows: Live content parameters include product attributes, scene context, user matching, and promotion sensitivity; product attributes include selling price, cost price, size, and material; Through the product price Pj and historical customer unit price of any product j Compare and obtain the user matching degree Um; obtain the live scene countdown length Td through the scene context, mark the total number of user behavior indicators as Nv, mark any user behavior indicator as v, and mark the difference in the change of user behavior indicator v before and after the live scene countdown length Td as the time-varying amplitude Av; Set the threshold of time-varying amplitude Av , when the variation amplitude Av exceeds the threshold , then it is determined that the user behavior indicator v has generated 1 mutation; thus, the number of mutations of Nv user behavior indicators is accumulated and marked as Na; then the promotion sensitivity Ps is obtained by the ratio of the number of mutations of the user behavior indicator Na to the countdown length Td of the live broadcast scene; By combining the user matching degree Um and the promotion sensitivity Ps, the live content score F3 is obtained.

[0010] Furthermore, the specific operation process of dynamically generating the module is as follows: By using the proportion of each user value level of all users in the live broadcast room, a dynamic instruction library of the 3D virtual person is set up, thereby calling the preset text instructions and voice instructions in the dynamic instruction library; Through text instructions and voice instructions, the text feature vector Qw and the voice feature vector Qy are extracted respectively; By integrating the text feature vector Qw and the speech feature vector Qy, a multimodal fusion feature vector Gq is constructed; The speech output and action sequence of the 3D virtual human are driven by the multimodal fusion feature vector Gq.

[0011] Furthermore, the specific process of multi-dimensional evaluation of AIGC generation quality is as follows: The text content of the live broadcast output is tested using text language detection software to obtain the number of grammatical errors d1 and the coherence score d2, and then weighted and comprehensively calculated to obtain the fluency score D1, thereby evaluating the fluency of the text language; By monitoring the emotional excitement index (EH) and category preference probability (P(c)) through historical data of user behavior indicators, and performing weighted comprehensive analysis to obtain the interest score (D2), we can evaluate the relevance of user interests. By comparing the speech text content generated by the current live broadcast with the speech text content generated by the historical live broadcast, the novelty score D3 is calculated to evaluate the historical comparison novelty; The average value and standard deviation of the historical data of the emotional tendency value Snt are calculated to obtain the overall emotional coefficient d3 and the emotional fluctuation coefficient d4 in turn, and then weighted with the behavior density index Bdi to obtain the interactive emotional score D4; The AIGC comprehensive quality index QD is obtained by weighted summing the fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4; By integrating the AIGC comprehensive quality index QD, fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4, an AIGC quality evaluation vector is generated, thereby evaluating the AIGC generation quality in multiple dimensions and generating the optimal operation recommendation strategy.

[0012] Furthermore, the process of obtaining the resource elastic configuration plan is as follows: The service health score Rf is obtained by weighting GPU utilization, response latency, and network bandwidth. The service health score Rf is multiplied by the live broadcast service time Tf to obtain the operating service cost R0 of the live broadcast room. The total transaction revenue R1 of the live broadcast room is monitored in real time through the GMV model. The objective function Pr of the live broadcast operation profit is calculated by the difference between the operating service cost R0 of the live broadcast room and the total transaction revenue R1. By maximizing the objective function Pr of the live broadcast operation profit, the resource elasticity configuration plan of the current live broadcast is obtained and output.

[0013] An automated live broadcast room operation system utilizing AIGC technology includes a data collection unit, an AIGC operation unit, a resource scheduling unit, and a terminal display unit. The AIGC operation unit includes a real-time processing module, a dynamic generation module, and a recommendation and distribution module. The system applies the automated live broadcast room operation method utilizing AIGC technology. Communication connection between the data collection unit, AIGC operation unit, resource scheduling unit and terminal display unit; communication connection between the real-time processing module, dynamic generation module and recommendation distribution module; The data collection unit is used to monitor AIGC operation data: the data collection unit monitors and collects AIGC operation data through the AIGC operation unit and resource scheduling unit; The AIGC operation unit is used to analyze AIGC operation data: the real-time processing module is used to establish a user behavior analysis model and predict user value levels; the dynamic generation module is used to drive 3D virtual human behavior through multimodal fusion, and to evaluate the quality of AIGC generation in multiple dimensions to generate the optimal operation recommendation strategy; the recommendation distribution module is used to output the recommendation priority of products in the live broadcast room; The resource scheduling unit is used to balance the allocation of AIGC control resources; The terminal display unit is used for AIGC live broadcast and multi-terminal interaction optimization.

[0014] The live broadcast room automatic operation device using AIGC technology includes a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the above-mentioned live broadcast room automatic operation method using AIGC technology.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: The present invention monitors AIGC operational data through a data acquisition unit and performs NLP sentiment analysis and user behavior modeling through the AIGC operation unit to predict user value levels in real time, achieving integrated processing of user portraits, real-time behavior, and live broadcast content, and generating optimal operational recommendation strategies, thereby supporting dynamic strategy adjustments, achieving real-time data analysis and decision-making, and optimizing AIGC-driven automated content. The present invention combines the cost and benefit model with the resource scheduling unit to realize the automatic optimization configuration of hardware resources, balance the AIGC control resource allocation, and optimize the AIGC live broadcast display and multi-terminal interaction through the terminal display unit, thereby dynamically adjusting the rendering resolution of each terminal, taking into account user experience and cost control, ensuring service quality and reducing operating costs. It is suitable for the intelligent upgrade of e-commerce live broadcast room scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram showing the steps of the workflow of the present invention is shown; Figure 2 A connection diagram of the system modules of the present invention is shown. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention. Example

[0018] like Figure 1-Figure 2 As shown in FIG, the live broadcast room automated operation system using AIGC technology includes a data collection unit, an AIGC operation unit, a resource scheduling unit, and a terminal display unit. The AIGC operation unit includes a real-time processing module, a dynamic generation module, and a recommendation distribution module. Communication connection between the data collection unit, AIGC operation unit, resource scheduling unit and terminal display unit; communication connection between the real-time processing module, dynamic generation module and recommendation distribution module; The data collection unit is used to monitor AIGC operation data; the AIGC operation unit is used to analyze AIGC operation data; the resource scheduling unit is used to balance AIGC control resource allocation; and the terminal display unit is used to display AIGC live broadcasts and perform multi-terminal interaction optimization.

[0019] The working steps are as follows: S1, the data collection unit monitors AIGC operation data: AIGC operation data includes user portrait parameters, real-time behavior parameters, live content parameters and system resource parameters; S1-1, user profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; consumption level and category preferences are quantified through user profile parameters; S1-2, real-time behavior parameters include product page click frequency, live broadcast room stay time, behavior density index, and sentiment tendency value. The behavior density index is obtained by weighted synthesis of user behavior indicators per unit time, including the number of comments and add-to-carts. The sentiment tendency value is quantified through NLP sentiment analysis. Real-time behavior parameters are used to identify purchase intention and emotional excitement. S1-3, live content parameters include product attributes, scene context, user matching, and promotion sensitivity. User matching is determined by comparing product prices with the user's historical average order value. Promotion sensitivity is determined by detecting the frequency of user behavior changes during the live scene countdown. Product adaptability and promotion responsiveness are dynamically evaluated using live content parameters. S1-4, system resource parameters include GPU utilization, response delay time, and network bandwidth; system resource parameters are used to ensure service stability for high-value users.

[0020] S2, AIGC operation unit analyzes AIGC operation data: AIGC operation unit includes real-time processing module, dynamic generation module and recommendation distribution module; S2-1, establish a user behavior analysis model through the real-time processing module, combine user portrait parameters, real-time behavior parameters and live content parameters to predict user value level; The specific process of establishing a user behavior analysis model to predict user value levels is as follows: Synchronously pre-process user portrait parameters, real-time behavior parameters, and live content parameters; S2-101, the specific process of preprocessing user portrait parameters is as follows: User profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; Historical behavior includes historical average order value and number of purchases; consumption behavior includes consumption amount and consumption frequency; Mark the number of all users in the live broadcast room as N0, and calculate the average customer order value based on the historical customer order value of N0 users. and the standard deviation of average order value ; Mark any user as i, and mark the historical average order value of user i as , and thus the historical average order value of user i is calculated through Z-Score Standardization; Get the total consumption amount by adding up the consumption amounts of N0 users , and then calculate the average value to obtain the average consumption ; Mark the consumption frequency of user i as ; Then obtain the consumption level index Clv: ;in, 、 and are the weight coefficients of user's average order value, consumption amount and consumption frequency respectively, and 、 and After calculating the experimental data, the preset is obtained to meet the Preset conditions: When the consumption level index Clv is higher, the consumption level of user i is evaluated to be higher; By clustering users' historical purchases, we determine the categories of the products they purchased. We mark the number of categories of all product categories as Nm, any product category as c, the number of historical purchases of user i in category c as Nc, and the total number of purchases of all categories in the user's historical behavior as Ni. From this, the category preference probability P(c) of user i for category c is calculated through Laplace smoothing correction: ;in, is the smoothing parameter, and ; By combining the consumption level index Clv and the category preference probability P(c), we can obtain the user portrait score F1: ; S2-102, the specific process of preprocessing real-time behavior parameters is as follows: Real-time behavior parameters include product page click frequency Cf, live broadcast room stay time Ts, behavior density index Bdi and sentiment tendency value Snt; Behavior density index Bdi: ;in, is the data collection period; Lc is the number of comments, La is the number of added purchases; and The weight coefficients of the number of comments and the number of purchases are respectively and It is to fit the preset by performing regression analysis after measuring a large amount of historical data; Sentiment tendency value Snt: This is a sentiment analysis method that classifies user comments through NLP sentiment analysis. The classified sentiment is assigned a value according to the category and mapped to the interval [-1, 1], where -1 represents the most negative sentiment, 1 represents the most positive sentiment, and 0 represents neutral sentiment. The purchase intention index PI is obtained by weighted summing the product page click frequency Cf, the live broadcast room stay time Ts, the behavior density index Bdi and the emotional tendency value Snt; Through the behavior density index Bdi and sentiment tendency value Snt in the data collection period The emotional excitement index EH is obtained by combining the change rate of ;in, For the data collection cycle The difference between the behavioral density index Bdi before and after; For the data collection cycle The difference between the emotional tendency values Snt before and after; By combining the purchase intention index PI and the emotional excitement index EH, we can obtain the real-time behavior score F2: ; S2-103, the specific process of pre-processing live content parameters is as follows: Live content parameters include product attributes, scene context, user matching, and promotion sensitivity; Product attributes include product selling price, cost price, size and material; Through the product price Pj and historical customer unit price of any product j Compare and obtain the user matching degree Um: ; When the user matching degree Um is higher, it means that the product price is more consistent with the historical average order value; Obtain the live scene countdown length Td through the scene context, mark the total number of user behavior indicators as Nv, mark any user behavior indicator as v, and mark the difference in the change of the user behavior indicator v before and after the live scene countdown length Td as the time-varying amplitude Av; Set the threshold of time-varying amplitude Av , when the variation amplitude Av exceeds the threshold , then it is determined that the user behavior indicator v has produced 1 mutation; thus, the number of mutations of Nv user behavior indicators is accumulated and marked as Na; Then, we can obtain the promotion sensitivity Ps by the ratio of the number of mutations Na of the user behavior indicator to the countdown time Td of the live broadcast scene: ; By combining user matching Um and promotion sensitivity Ps, we can obtain the live content score F3: ; S2-104 combines the user portrait score F1, the real-time behavior score F2, and the live content score F3 to generate the user value prediction index JZ: ; in, 、 and are the weight coefficients of user portrait score F1, real-time behavior score F2, and live content score F3, respectively, and 、 and are greater than 0, ; When the user portrait score F1, real-time behavior score F2 and live content score F3 are higher, the user value prediction index JZ is higher, and thus the predicted user value level is higher; Set the evaluation interval of the user value prediction index JZ, determine the user value level by interval comparison and mark it. The user value level includes high, medium and low, and focus on conversion for users with high value level.

[0021] S2-2, through the dynamic generation module, multimodally fuses text and voice commands to drive 3D virtual human behavior. By monitoring the fluency of text language, user interest relevance, historical comparison novelty, and interactive sentiment score, the AIGC generation quality is evaluated in multiple dimensions to generate the optimal operation recommendation strategy. S2-201, setting a dynamic instruction library of the 3D virtual person based on the proportion of each user value level of all users in the live broadcast room, thereby calling preset text instructions and voice instructions in the dynamic instruction library; Through text instructions and voice instructions, the text feature vector Qw and the voice feature vector Qy are extracted respectively; By integrating the text feature vector Qw and the speech feature vector Qy, a multimodal fusion feature vector Gq is constructed; The multimodal fusion feature vector Gq is used to drive the speech output and action sequence of the 3D virtual human. The speech output includes the intonation, speaking speed and timbre of the speech; the action sequence includes joint angles and body posture.

[0022] S2-202 monitors text language fluency, user interest relevance, historical comparison novelty, and interactive sentiment score. The specific process is as follows: The text content of the live broadcast output is tested using text language detection software to obtain the number of grammatical errors d1 and the coherence score d2, and then weighted and comprehensively calculated to obtain the fluency score D1, thereby evaluating the fluency of the text language; By monitoring the emotional excitement index (EH) and category preference probability (P(c)) through historical data of user behavior indicators, and performing weighted comprehensive analysis to obtain the interest score (D2), we can evaluate the relevance of user interests. S2-202-2, by comparing the voice and text content generated by the current live broadcast with the voice and text content generated by the historical live broadcast, and calculating the novelty score D3, thereby evaluating the historical comparative novelty; The current content is marked as c, and a set H is constructed by using the historical content corresponding to m periods. The historical content corresponding to any period is marked as hc. The text similarity algorithm is then used to calculate the similarity S(c, hc) between the current content c and the historical content hc of any period, and then the novelty score D3 is obtained: ; The average value and standard deviation of the historical data of the emotional tendency value Snt are calculated to obtain the overall emotional coefficient d3 and the emotional fluctuation coefficient d4 in turn, and then weighted with the behavior density index Bdi to obtain the interactive emotional score D4; S2-203, obtain the AIGC comprehensive quality index QD by weighted summing the fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4; The AIGC quality evaluation vector is generated by integrating the AIGC comprehensive quality index QD, fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4; S2-204, through the AIGC comprehensive quality index QD and the specific performance of each dimension, to evaluate the AIGC generation quality in multiple dimensions and generate the optimal operation recommendation strategy; For example, when the text language fluency is low, it is recommended to optimize the natural language processing model; when the user interest relevance is insufficient, it is recommended to personalize the content based on the user portrait; when the historical comparison novelty is low, it is encouraged to add innovative elements; when the interactive emotion score is low, it is recommended to optimize the emotional expression and interactive design of the content; By establishing a policy library and rule engine, we can automatically match corresponding recommendation strategies according to different evaluation results to improve the quality of AIGC-generated content and operational effectiveness.

[0023] S2-3, output the recommendation priority of live broadcast room products based on user value level and optimal operation recommendation strategy through the recommendation distribution module; For example, when the proportion of high-value users is high, high-gross-profit new products will be recommended first; when the proportion of medium-value users is high, hot-selling repeat purchase products and potential new products will be recommended first; when the proportion of low-value users is high, traffic-generating products and promotional products will be recommended first; This allows the live broadcast behavior of the 3D virtual person to be dynamically adjusted, including live broadcast operation language and action expression status.

[0024] S3, the resource scheduling unit balances AIGC control resource allocation: It analyzes the total cost of live broadcast operations through system resource parameters, dynamically coordinates with the total transaction revenue of the live broadcast room, and outputs a resource elasticity configuration plan; S3-1, obtain the service health score Rf by weighted comprehensive calculation of GPU utilization, response delay time, and network bandwidth, and multiply the service health score Rf by the live service time Tf to obtain the operating service cost R0 of the live broadcast room; S3-2, monitor the total transaction revenue R1 of the live broadcast room in real time through the GMV model; the GMV model refers to the pricing or revenue calculation method based on the gross merchandise value (Gross Merchandise Value) in e-commerce; S3-3, calculating the objective function Pr of the live broadcast operation profit by the difference between the operating service cost R0 and the total transaction revenue R1 of the live broadcast room, and maximizing the objective function Pr of the live broadcast operation profit to output the resource elasticity allocation plan for the current live broadcast; The resource elasticity configuration solution includes dynamic adjustment of GPU utilization, response delay time, network bandwidth resource allocation, and multi-terminal interaction optimization operations, thereby making live broadcast operations more efficient.

[0025] S4: The terminal display unit optimizes AIGC live broadcasting and multi-terminal interaction: It uses flexible resource configuration to adapt rendering resolution, ultimately increasing new user conversion rates, reducing overall live broadcast costs, and improving automated operational efficiency in live broadcast rooms. For example, when the GPU computing power and bandwidth are insufficient, the AIGC resolution of the display port is reduced, thereby reducing the total cost of live broadcast operations.

[0026] The live broadcast room automation operation equipment using AIGC technology includes a processor and a memory storing a computer program. When the computer program is run by the processor, the above-mentioned live broadcast room automation operation method using AIGC technology is executed.

[0027] In summary, the present invention monitors AIGC operational data through a data acquisition unit, and performs NLP sentiment analysis and user behavior modeling through the AIGC operation unit, predicting user value levels in real time, achieving integrated processing of user portraits, real-time behavior, and live broadcast content, and generating optimal operational recommendation strategies, thereby supporting dynamic strategy adjustments, achieving real-time data analysis and decision-making, and optimizing AIGC-driven automated content. The present invention combines the cost and benefit model with the resource scheduling unit to realize the automatic optimization configuration of hardware resources, balance the AIGC control resource allocation, and optimize the AIGC live broadcast display and multi-terminal interaction through the terminal display unit, thereby dynamically adjusting the rendering resolution of each terminal, taking into account user experience and cost control, ensuring service quality and reducing operating costs. It is suitable for the intelligent upgrade of e-commerce live broadcast room scenarios.

[0028] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will appreciate that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0029] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0030] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The automated operation method of a live broadcast room using AIGC technology is characterized by: The following steps are involved: Step 1: The data collection unit is used to monitor AIGC operation data: AIGC operation data includes user profile parameters, real-time behavior parameters, live content parameters, and system resource parameters; Step 2: The AIGC operation unit is used to analyze AIGC operation data: the AIGC operation unit includes a real-time processing module, a dynamic generation module, and a recommendation and distribution module; A user behavior analysis model is established through the real-time processing module, combining user portrait parameters, real-time behavior parameters, and live content parameters to predict user value levels; Through the dynamic generation module, multimodal fusion of text and voice commands is performed to drive 3D virtual human behavior. By monitoring the fluency of text language, relevance to user interests, historical comparison novelty, and interactive sentiment scores, the AIGC generation quality is evaluated in multiple dimensions to generate the optimal operation recommendation strategy. The recommendation distribution module outputs the recommendation priority of live broadcast room products based on user value level and optimal operation recommendation strategy; Step 3: The resource scheduling unit is used to balance the allocation of AIGC control resources: it analyzes the total cost of live broadcast operations through system resource parameters, dynamically coordinates with the total transaction revenue of the live broadcast room, and outputs a resource elasticity configuration plan; Step 4: The terminal display unit is used for AIGC live broadcast and multi-terminal interaction optimization: adaptive rendering resolution is achieved through resource elastic configuration solution.

2. The method for automated operation of a live broadcast room using AIGC technology according to claim 1, characterized in that: The specific process of establishing a user behavior analysis model to predict user value levels is as follows: Synchronously pre-process the user portrait parameters, real-time behavior parameters, and live content parameters. The specific process is as follows: User profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; historical behavior includes historical average order value and number of purchases; and spending behavior includes spending amount and frequency. Preprocessing user profile parameters: Obtain the consumption level index Clv and category preference probability P(c) through the user profile parameters. Then, combine the consumption level index Clv and category preference probability P(c) to obtain the user profile score F1. Real-time behavior parameters include product page click frequency Cf, live broadcast room stay time Ts, behavior density index Bdi and sentiment tendency value Snt; Preprocessing real-time behavior parameters: Obtain the purchase intention index PI through real-time behavior parameters; obtain the emotional excitement index EH by combining the behavior density index Bdi and the emotional tendency value Snt; obtain the real-time behavior score F2 by combining the purchase intention index PI and the emotional excitement index EH; Live content parameters include product attributes, scene context, user matching, and promotion sensitivity; product attributes include selling price, cost price, size, and material; Preprocessing live content parameters: Obtain user matching degree Um and promotion sensitivity Ps through product attributes and scene context; Obtain live content score F3 by combining user matching degree Um and promotion sensitivity Ps; Then, the user portrait score F1, real-time behavior score F2 and live content score F3 are combined to generate the user value prediction index JZ; Set the evaluation interval of the user value prediction index JZ, determine the user value level by interval comparison and mark it.

3. The method for automated operation of a live broadcast room using AIGC technology according to claim 2, characterized in that: The specific process of preprocessing user portrait parameters is as follows: User profile parameters include age, gender, device model, historical behavior, spending power, and interest clustering; historical behavior includes historical average order value and number of purchases; and spending behavior includes spending amount and frequency. Mark the number of all users in the live broadcast room as N0, and calculate the average customer order value based on the historical customer order value of N0 users. and standard deviation of average order value ; Mark any user as i, and mark the historical average order value of user i as , and thus the historical average order value of user i is calculated through Z-Score Standardize; add up the consumption amounts of N0 users to get the total consumption amount Then calculate the average value to obtain the average consumption ; Mark the consumption frequency of user i as , and then obtain the consumption level index Clv; By clustering users' historical purchases, we determine the categories of their products. We label the total number of product categories as Nm, any product category as c, the number of purchases by user i in category c as Nc, and the total number of purchases across all categories in the user's history as Ni. We then use Laplace smoothing to correct and calculate the probability P(c) of user i's preference for category c. Then, by combining the consumption level index Clv and the category preference probability P(c), we can obtain the user portrait score F1.

4. The method for automated operation of a live broadcast room using AIGC technology according to claim 3, characterized in that: The specific process of preprocessing real-time behavior parameters is as follows: Real-time behavior parameters include product page click frequency Cf, live broadcast room stay time Ts, behavior density index Bdi and sentiment tendency value Snt; The purchase intention index PI is obtained by weighted summing the product page click frequency Cf, the live broadcast room stay time Ts, the behavior density index Bdi and the emotional tendency value Snt; Through the behavior density index Bdi and sentiment tendency value Snt in the data collection period The emotional excitement index EH is obtained by combining the change rate of The real-time behavior score F2 is obtained by combining the purchase intention index PI and the emotional excitement index EH.

5. The method for automated operation of a live broadcast room using AIGC technology according to claim 4, characterized in that: The specific process of preprocessing live content parameters is as follows: Live content parameters include product attributes, scene context, user matching, and promotion sensitivity; product attributes include selling price, cost price, size, and material; Through the product price Pj and historical customer unit price of any product j Perform comparison and obtain user matching degree Um; Obtain the live scene countdown length Td through the scene context, mark the total number of user behavior indicators as Nv, mark any user behavior indicator as v, and mark the difference in the change of the user behavior indicator v before and after the live scene countdown length Td as the time-varying amplitude Av; Set the threshold of time-varying amplitude Av , when the variation amplitude Av exceeds the threshold , then it is determined that the user behavior indicator v has generated 1 mutation; thus, the number of mutations of Nv user behavior indicators is accumulated and marked as Na; then the promotion sensitivity Ps is obtained by the ratio of the number of mutations of the user behavior indicator Na to the countdown length Td of the live broadcast scene; By combining the user matching degree Um and the promotion sensitivity Ps, the live content score F3 is obtained.

6. The method for automated operation of a live broadcast room using AIGC technology according to claim 5, characterized in that: The specific operation process of dynamically generating modules is as follows: By using the proportion of each user value level of all users in the live broadcast room, a dynamic instruction library of the 3D virtual person is set up, thereby calling the preset text instructions and voice instructions in the dynamic instruction library; Through text instructions and voice instructions, the text feature vector Qw and the voice feature vector Qy are extracted respectively; By integrating the text feature vector Qw and the speech feature vector Qy, a multimodal fusion feature vector Gq is constructed; The speech output and action sequence of the 3D virtual human are driven by the multimodal fusion feature vector Gq.

7. The method for automated operation of a live broadcast room using AIGC technology according to claim 6, characterized in that: The specific process of multi-dimensional evaluation of AIGC generation quality is as follows: The text content of the live broadcast output is tested using text language detection software to obtain the number of grammatical errors d1 and the coherence score d2, and then weighted and comprehensively calculated to obtain the fluency score D1, thereby evaluating the fluency of the text language; By monitoring the emotional excitement index (EH) and category preference probability (P(c)) through historical data of user behavior indicators, and performing weighted comprehensive analysis to obtain the interest score (D2), we can evaluate the relevance of user interests. By comparing the speech text content generated by the current live broadcast with the speech text content generated by the historical live broadcast, the novelty score D3 is calculated to evaluate the historical comparison novelty; The average value and standard deviation of the historical data of the emotional tendency value Snt are calculated to obtain the overall emotional coefficient d3 and the emotional fluctuation coefficient d4 in turn, and then weighted with the behavior density index Bdi to obtain the interactive emotional score D4; The AIGC comprehensive quality index QD is obtained by weighted summing the fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4; By integrating the AIGC comprehensive quality index QD, fluency score D1, interest score D2, novelty score D3 and interactive emotion score D4, an AIGC quality evaluation vector is generated, thereby evaluating the AIGC generation quality in multiple dimensions and generating the optimal operation recommendation strategy.

8. The method for automated operation of a live broadcast room using AIGC technology according to claim 7, characterized in that: The process of obtaining a resource elastic configuration plan is as follows: The service health score Rf is obtained by weighting the GPU utilization, response delay time, and network bandwidth, and the service health score Rf is multiplied by the live service time Tf to obtain the operating service cost R0 of the live broadcast room; Monitor the total transaction revenue R1 of the live broadcast room in real time through the GMV model; The objective function Pr of the live broadcast operation profit is calculated by the difference between the operating service cost R0 of the live broadcast room and the total transaction revenue R1. By maximizing the objective function Pr of the live broadcast operation profit, the resource elasticity configuration plan of the current live broadcast is obtained and output.

9. The automated live broadcast room operation system using AIGC technology is characterized by: The system comprises a data collection unit, an AIGC operation unit, a resource scheduling unit and a terminal display unit, wherein the AIGC operation unit comprises a real-time processing module, a dynamic generation module and a recommendation distribution module. The system applies the live broadcast room automated operation method using AIGC technology as described in any one of claims 1 to 8 above; Communication connection between the data collection unit, AIGC operation unit, resource scheduling unit and terminal display unit; communication connection between the real-time processing module, dynamic generation module and recommendation distribution module; The data collection unit is used to monitor AIGC operation data: the data collection unit monitors and collects AIGC operation data through the AIGC operation unit and resource scheduling unit; The AIGC operation unit is used to analyze AIGC operation data: the real-time processing module is used to establish a user behavior analysis model and predict user value levels; the dynamic generation module is used to drive 3D virtual human behavior through multimodal fusion, and to evaluate the quality of AIGC generation in multiple dimensions to generate the optimal operation recommendation strategy; the recommendation distribution module is used to output the recommendation priority of products in the live broadcast room; The resource scheduling unit is used to balance the allocation of AIGC control resources; The terminal display unit is used for AIGC live broadcast and multi-terminal interaction optimization.

10. Live broadcast room automated operation equipment using AIGC technology, characterized by: It includes a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the live broadcast room automatic operation method using AIGC technology as described in any one of claims 1 to 8.

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