An AI digital human information processing method and system based on intelligent document generation

Abstract

This application discloses an AI digital human information processing method and system based on intelligent document generation. The method includes: obtaining live commentary content displayed on the live broadcast interface; determining the first clothing product information to be displayed based on the live commentary content; obtaining the first oral copy content corresponding to the first clothing product information; using the first clothing product information and the first oral copy content to update the character appearance data and voice data in the first intelligent document to obtain a second intelligent document; and updating and adjusting the current AI anchor's clothing and oral content based on the second intelligent document. By using this application solution, the interactive rate and the intuitive viewing experience of the audience are improved, thereby improving the effectiveness and activity of the live broadcast.

Description

Technical Field

[0001] The present invention relates to digital human information processing technology, and in particular to an AI digital human information processing method and system based on intelligent document generation. Background Art

[0002] AI digital humans are virtual characters created using artificial intelligence (AI). They can interact with humans through AI technologies such as natural language processing, speech recognition, and machine learning (e.g., in intelligent customer service scenarios, they can communicate with consumers and respond to their questions). They can also run on a variety of devices and platforms. These virtual characters possess not only a high degree of interactivity but also the ability to express emotions and learn personalized skills, thereby providing more personalized service and support. Consequently, AI digital humans have been applied in a variety of scenarios, such as intelligent customer service (both online and over the phone), education and training, smart guides, virtual idols, and livestreaming.

[0003] Currently, using AI digital humans to promote sales through live streaming requires simply inputting the specified product introduction and promotional content, the live streamer's video / image information, appearance information, and audio information into the AI ​​digital human's multimodal feature fusion generation model. The generated AI digital human then simulates the live streamer's appearance, voice, expressions, and behavior, and "speaks" the product introduction and promotional content verbally, thereby introducing and promoting the product. However, this approach predetermines the content of the verbal broadcast and the content / sequence of the product display. Consequently, it lacks interaction with the live stream audience and is unable to display products according to their needs. This reduces the efficiency of the live stream and also reduces interactivity. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems existing in the prior art. To this end, this application proposes an AI digital human information processing method and system based on intelligent document generation.

[0005] In a first aspect, an embodiment of the present application provides an AI digital human information processing method based on intelligent document generation, the method comprising:

[0006] S1. Obtain the live comment content displayed on the live interface;

[0007] S2. Determine first clothing product information to be displayed based on the live broadcast comment content;

[0008] S3. Obtaining a first spoken copy content corresponding to the first clothing product information;

[0009] S4. Update the character appearance data and voice data in the first smart document using the first clothing product information and the first spoken copy content to obtain a second smart document;

[0010] S5. Update and adjust the current AI anchor's clothing and spoken content based on the second intelligent document.

[0011] In some embodiments, step S3 specifically includes:

[0012] A301. Extracting a plurality of first keywords from the first clothing product information;

[0013] A302. Generate corresponding first spoken copy content using a plurality of first keywords;

[0014] Alternatively, a first piece of clothing product information is configured with at least two corresponding oral copy files, and step S3 specifically includes:

[0015] B301. Randomly select a spoken copy file from at least two corresponding spoken copy files;

[0016] B302. The content of the selected spoken copy file is used as the first spoken copy content.

[0017] In some embodiments, step S3 specifically includes:

[0018] C301. When it is determined that the number of times all the spoken copy files are selected exceeds the first number threshold, extract a plurality of first keywords from the first clothing product information;

[0019] C302. Generate corresponding first spoken copy content using a plurality of first keywords.

[0020] In some embodiments, step S4 specifically includes:

[0021] S401: Obtain a first display behavior action corresponding to first clothing product information;

[0022] S402: After updating the character appearance data, voice data, and behavior data in the first smart document using the first clothing product information, the first spoken copy content, and the first display behavior action, a second smart document is obtained.

[0023] In some embodiments, the method further comprises the following steps:

[0024] S6. Acquire a first background image corresponding to the first clothing product information, wherein the timestamp corresponding to the first clothing product information is synchronized with the timestamp of the first background image;

[0025] S7. Use the first background image to replace the live video background.

[0026] In some embodiments, step S2 specifically includes:

[0027] S201: Determine the number of occurrences of different clothing product labels from the acquired live commentary content according to a set data statistical time period, wherein the clothing product labels correspond one-to-one to the clothing product information;

[0028] S202: Determine a first priority value corresponding to the clothing product label according to the number of occurrences;

[0029] S203, displaying the labels of different clothing items on the backend central control interface in descending order according to the size of the first priority selected value;

[0030] S204. In response to the first click information input from the backend central control interface, determine the clothing product label that is clicked and selected, and determine the clothing product information corresponding to the clothing product label that is clicked and selected as the first clothing product information to be displayed.

[0031] In some embodiments, step S202 specifically includes:

[0032] S2021. Identify the sentiment score corresponding to the clothing product label from the acquired live comment content;

[0033] S2022: Determine a corresponding first priority value based on the number of occurrences of the clothing product label and the sentiment score.

[0034] In some embodiments, step S2022 specifically includes:

[0035] S20221. Obtain the display time interval corresponding to the clothing product label;

[0036] S20222. Determine a first adjustment coefficient corresponding to the display time interval;

[0037] S20223. Determine a corresponding first priority value based on the number of occurrences of the clothing product label, the sentiment score, and the first adjustment coefficient.

[0038] In some embodiments, step S2 further specifically includes:

[0039] S205: Obtain the display time interval corresponding to the clothing product label, and display the display time interval as the label information of the clothing product label on the background central control interface.

[0040] In a second aspect, an embodiment of the present application provides an AI digital human information processing system based on intelligent document generation, the system comprising:

[0041] The first display interface includes the live broadcast interface and the backend central control interface;

[0042] The background processing system includes at least one processor for executing the steps of implementing the above-mentioned AI digital human information processing based on intelligent document generation.

[0043] This application can achieve at least one of the following technical effects: This application solution identifies the content of user comment data in the live broadcast room to determine the clothing product information to be displayed, and then uses the content and the corresponding oral copy content to update and adjust the corresponding type of information content of the smart document to obtain a new smart document. Finally, based on the information in the new smart document, the current AI anchor is updated and rendered so that the currently displayed clothing and oral content correspond to the clothing product information to be displayed, so as to achieve the goal of displaying clothing products while voice reading the corresponding product promotion introduction copy. It can be seen that this solution can adjust product display and introduction according to the real-time needs of active users in the current live broadcast room, which can greatly improve the interaction rate and the intuitive viewing experience of the audience, thereby improving the effectiveness and activity of the live broadcast. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0045] Figure 1 A schematic flow chart of the steps of a first embodiment of an AI digital human information processing method based on intelligent document generation is provided for the embodiment of the present application;

[0046] Figure 2 A schematic flow chart of the steps of a second embodiment of an AI digital human information processing method based on intelligent document generation is provided for the embodiment of the present application;

[0047] Figure 3 A schematic structural diagram of a first embodiment of an AI digital human information processing system based on intelligent document generation is provided for the embodiment of the present application;

[0048] Figure 4 A structural schematic diagram of a second embodiment of an AI digital human information processing system based on intelligent document generation is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of this application more clear, the following will refer to the drawings in the embodiments of this application to clearly and completely describe the technical solutions of this application through implementation methods. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] In order to extend the duration of live streaming with goods, increase brand exposure, and save manpower and operating costs, more and more live streaming rooms are currently using AI digital human technology to implement live streaming with goods. However, for existing live streaming with goods technical solutions based on AI digital human technology, they generally use pre-set solidified oral broadcast content and the order of product display as input information for the AI ​​anchor to generate a rendering model, so that the AI ​​anchor can simulate the appearance, voice, expression, and behavior of the real anchor, and read out the relevant product recommendations and introductions in oral form in sequence. It can be seen that this method lacks interaction with the live broadcast audience and is unable to recommend and introduce corresponding products according to the actual needs of most current live broadcast viewers. This greatly reduces the live broadcast activity and poor interactivity, resulting in a low consumer experience, thereby reducing the efficiency of the live broadcast. To this end, the embodiment of the present application designs an AI digital human information processing solution based on intelligent document generation, which can improve the interactivity with the current live broadcast audience, increase the live broadcast room activity, and also improve the consumer viewing experience.

[0051] Reference Figure 1 , an embodiment of the present application provides an AI digital human information processing method based on intelligent document generation, the method comprising the following steps.

[0052] S1. Obtain live commentary content displayed on the live broadcast interface. This live commentary content is primarily input and sent by live broadcast viewers and displayed on the live broadcast interface. Specifically, this live commentary content may include at least information about clothing products that the host needs to display and introduce, the intensity of this need, and satisfaction with the current clothing product display and introduction.

[0053] S2. Determine the first clothing product information to be displayed based on the live broadcast comment content.

[0054] Specifically, given that there are many live broadcast viewers and their needs are inconsistent, it is necessary to identify the needs of the live broadcast viewers so that the first clothing product information to be displayed can meet the needs of most live broadcast viewers. Therefore, step S2 can specifically include the following steps.

[0055] S201. According to a set data statistics time period, determine the number of occurrences of different clothing product labels from the acquired live broadcast comment content, wherein the clothing product labels correspond one-to-one to the clothing product information.

[0056] Specifically, in this embodiment, the clothing product number primarily refers to the serial number of the clothing product link, for example, the clothing product of the first link, the clothing product of the second link, and so on. Different clothing product numbers correspond to different clothing product information, wherein the clothing product information may at least include the following: clothing type, such as tops (long / short-sleeved T-shirts, shirts, chiffon blouses, etc.), summer wear (long / short skirts, pants, culottes, etc.); material type; color; style type, such as retro, artistic, minimalist, etc. Therefore, by counting the number of occurrences of clothing product numbers in live broadcast comments, the viewing needs of most live broadcast viewers can be determined. The more frequently a clothing product number appears, the more likely it is that the live broadcast viewers want to know about the clothing product information. The data statistics time period can be determined based on the frequency of live broadcast comments, or it can be set as a fixed value. This setting can be based on actual conditions and is not specifically limited here.

[0057] In some embodiments, the step of determining the number of occurrences of different clothing product labels from the acquired live comment content may specifically include:

[0058] S2011. After using the NLP (natural language processing) model to identify and process the acquired live comment content, a corresponding recognition result is obtained, and the recognition result includes the clothing product label;

[0059] S2012: Count the number of occurrences of different clothing product labels. Since NLP technology is used to achieve content recognition, the recognition result is highly accurate, thus further improving the recognition accuracy of clothing product labels.

[0060] S202: Determine a corresponding first priority value according to the number of occurrences of the clothing product label.

[0061] Specifically, the method for determining the first priority selection value can be: directly using the number of occurrences of a clothing product label as the first priority selection value corresponding to the clothing product label (i.e., the first clothing product information); or it can be: adjusting the number of occurrences of a clothing product label as needed, and using the adjusted result as the first priority selection value corresponding to the clothing product label.

[0062] In some embodiments, if the live comment content indicates that the live viewer has a strong desire for the display of a certain clothing product, for example, a comment such as "I really want to see the display of the nth linked clothing product, urgent!", it indicates that the live viewer is highly interested in the clothing product, and the order conversion rate will be higher. Therefore, based on this factor, the priority value can be adjusted to ensure that the clothing product is displayed as soon as possible, thereby further improving interactivity and increasing the order conversion rate. Therefore, step S202 can specifically include the following steps.

[0063] S2021. Identify the sentiment score corresponding to the clothing item number from the acquired live comment content, where a higher sentiment score indicates a stronger purchasing intent of the live viewer. The sentiment score can also be obtained by processing the acquired live comment content using an NLP (natural language processing) model, and will not be repeated here.

[0064] S2022: Determine a corresponding first priority value based on the number of occurrences of the clothing product label and the sentiment score.

[0065] Specifically, a second adjustment coefficient k2 corresponding to the emotion score is obtained, wherein the emotion score and the second adjustment coefficient are in positive proportion, that is, the higher the emotion score, the larger the value of the corresponding second adjustment coefficient; then, the first product result between the number of occurrences M of the clothing product label and the second adjustment coefficient k2 is calculated, and this first product result is used as the first priority selection value corresponding to the clothing product label.

[0066] In some embodiments, to consider merchant needs, the completeness of product displays, and to avoid the problem of low live broadcast satisfaction due to excessive content repetition, the priority selection value is also considered in the process of determining the display interval. This display interval refers to whether the time interval between two consecutive displays of clothing products with the same clothing product number meets the requirements. If so, the priority selection value of the clothing product with the current clothing product number is unchanged or increased. Otherwise, the priority selection value is reduced. Therefore, the step S2022 can specifically include the following steps.

[0067] S20221. Obtain the display time interval corresponding to the clothing product label. Specifically, the display time interval refers to the time interval between the current moment and the end time of the last display of the same clothing product. For example, if the priority value corresponding to the i-th clothing product label is the largest after counting the number of times, then the time interval between the current moment and the end time of the last display of the clothing product information corresponding to the i-th clothing product label is calculated, and this is the display time interval.

[0068] S20222. Determine a first adjustment coefficient corresponding to the display time interval.

[0069] Specifically, if it is found after statistics that the display time interval corresponding to the current i-th clothing product label is greater than the preset time threshold, then there is no need to adjust the priority selection value. Otherwise, the priority selection value needs to be lowered. Therefore, step S20222 may specifically include the following steps:

[0070] When it is determined that the display time interval is greater than or equal to the preset time threshold, the first adjustment sub-value is used as the first adjustment coefficient. In this case, the first adjustment coefficient is 1;

[0071] When it is determined that the presentation time interval is less than the preset time threshold, the second adjustment sub-value is used as the first adjustment coefficient, wherein the second adjustment sub-value is less than the first adjustment sub-value.

[0072] S20223. Determine a corresponding first priority value based on the number of occurrences of the clothing product label, the sentiment score, and the first adjustment coefficient.

[0073] Specifically, the first priority value corresponding to the clothing product label is determined based on the number of occurrences of the clothing product label, the second adjustment coefficient k2, and the first adjustment coefficient k1. Furthermore, this step may specifically include calculating a second product of the number of occurrences M of the clothing product label, the second adjustment coefficient k2, and the first adjustment coefficient k1, and using this second product as the first priority value corresponding to the clothing product label.

[0074] S203. Display different clothing product labels on the backend central control interface in order from large to small according to the size of the first priority selected value.

[0075] S204. In response to the first click information input from the backend central control interface, determine the clothing product label that is clicked and selected, and determine the clothing product information corresponding to the clothing product label that is clicked and selected as the first clothing product information to be displayed.

[0076] Specifically, in order to further ensure and improve the suitability and accuracy of the displayed content, it can be displayed in order from large to small on the background central control interface according to the size of the priority value. This is convenient for the central control personnel to view, and can be combined with the historical experience of the central control personnel and other currently known information (such as current inventory, merchant requirements, etc.) to determine whether to display the clothing product information corresponding to the clothing product number with the largest priority value. If so, the central control personnel can click on the corresponding clothing product number. It can be seen that by determining the clothing product information that needs to be displayed in this way, the traditional problem of low accuracy caused by manual counting of the number of times can be avoided, and under the premise of taking other considerations (such as inventory, merchant requirements, etc.), it can accurately meet the needs of most live broadcast viewers, and also bring great convenience to the work of the central control personnel and improve work efficiency.

[0077] In some embodiments, step S2 further specifically includes: S205, obtaining the display time interval corresponding to the clothing product label, and displaying the display time interval as label information for the clothing product label on the backend central control interface. This method allows the central control personnel to further determine whether to prioritize and display the clothing product with the maximum value based on the display time interval, thereby ensuring that the display content and order are more in line with actual conditions.

[0078] S3. Obtain the first spoken copy content corresponding to the first clothing product information. Among them, for the spoken copy content corresponding to the first clothing product information, it can recommend and introduce the product according to the preset spoken content, or it can generate the corresponding spoken copy content based on the keywords of the clothing product information using AI document generation technology. The advantage of the former is that the requirements for system performance are low and the information processing efficiency is high, while the advantage of the latter is that for the same clothing product, the spoken content is different each time, avoiding the reduction in the watchability of the live broadcast content due to repeated and outdated spoken content, thereby reducing its activity and / or the number of viewers. Therefore, for the above-mentioned step S3, it can specifically include the following steps.

[0079] ①. First Implementation Method of Step S3

[0080] A301. Extracting a plurality of first keywords from the first clothing product information;

[0081] A302. Generate corresponding first spoken copy content using a plurality of first keywords.

[0082] Specifically, the above step S302 can use NLG (natural language generation) technology to process the input of several first keywords and generate corresponding first spoken copy content.

[0083] ② Second Implementation Method of Step S3

[0084] B301. Randomly select a spoken copy file from at least two corresponding spoken copy files; wherein a first piece of clothing product information is configured with at least two corresponding spoken copy files;

[0085] B302. The content of the selected spoken copy file is used as the first spoken copy content.

[0086] Specifically, for a clothing product, at least two corresponding oral copy files are pre-configured, that is, for a clothing product, there are at least two sets of oral copy files with different expression forms and contents. Then, when it is necessary to oral-read the content of a certain clothing product, the oral copy file can be randomly selected to allow the AI ​​anchor to read out the corresponding content. This improves the watchability and fun of the live broadcast while taking into account the processing efficiency.

[0087] In some embodiments, considering that the number of oral text files is limited, if the live broadcast time of the AI ​​anchor is very long, if it is broadcast non-stop for 24 hours, there will still be problems such as low viewing and low activity due to the high repetitiveness of the oral content. Therefore, for the above-mentioned step S3, it can also specifically include the following implementation steps, that is, the third embodiment.

[0088] C301. When it is determined that the number of times all the spoken copy files are selected exceeds the first number threshold, extract a plurality of first keywords from the first clothing product information;

[0089] C302. Generate corresponding first spoken copy content using a plurality of first keywords.

[0090] Specifically, during a live broadcast, if all pre-set audio copy files have been retrieved and used, and the number of times they have been used exceeds a threshold, keyword generation can be used to generate corresponding audio copy content, allowing the AI ​​anchor to introduce and sell clothing products based on this content. This approach allows for the acquisition of audio copy content, further improving the viewing experience and fun of live broadcasts while balancing processing efficiency.

[0091] S4. After updating the character appearance data and voice data in the first smart document using the first clothing product information and the first spoken copy content, a second smart document is obtained.

[0092] Specifically, the smart document contains various information data required for the subsequent generation of AI digital people, such as character appearance data (such as the character's clothing, appearance, etc.), behavioral actions, voice data (which is the voice data obtained after voice conversion based on the text content read out by the AI ​​anchor), voice features (including the speed, tone, timbre, etc. configured when the AI ​​anchor reads out the voice, and these voice features are extracted from the audio files of the real anchor), expression data, etc. Therefore, by adjusting and updating the data stored in the smart document, the AI ​​anchor's clothing, voice-read content, expression, actions, speed, tone, timbre, etc. can be adjusted. Therefore, the AI ​​anchor can be adjusted by updating the corresponding type data in the smart document based on the clothing product information to be displayed and the product introduction and sales information that needs to be read out by the AI ​​anchor.

[0093] In some embodiments, given that the present application primarily displays and sells clothing products, when the AI ​​anchor's clothing is changed to the desired clothing item, corresponding display actions can be configured to display the clothing item from all angles, including the front, back, left, and right. Furthermore, the display actions can be tailored to the style of the clothing item to further highlight its advantages and thereby increase the sales rate. Therefore, step S4 can specifically include the following steps.

[0094] S401: Acquire a first display behavior action corresponding to first clothing product information.

[0095] Specifically, the first display behavior action may form a mapping relationship with the clothing style information included in the first clothing product information, and then the corresponding first display behavior action may be obtained according to the clothing style included in the first clothing product information.

[0096] Furthermore, a clothing style is configured with at least two different display behavior actions, and the step S401 may specifically include: after obtaining the clothing style information contained in the first clothing product information, randomly selecting a display behavior action from the corresponding at least two different display behavior actions as the first display behavior action.

[0097] Furthermore, the step S401 may specifically include: obtaining corresponding at least two different display behavior actions based on the clothing style information contained in the first clothing product information; then configuring corresponding display timestamps for at least two different display behavior actions, the display timestamp is mainly used to characterize the display duration or display start and / or end time of a display behavior action, so that when the AI ​​anchor performs clothing display, for example, a total of 3 display behavior actions are configured, the AI ​​anchor's behavior action is first configured as the first display behavior action for display, and when the display time is over, the AI ​​anchor's behavior action is first configured as the second display behavior action for display, and similarly, when its display time is over, the AI ​​anchor's behavior action is first configured as the third display behavior action for display, and when these 3 display behavior actions are executed, the introduction of the current clothing product also ends, or ends slightly (this can be selected according to actual conditions and is not specifically limited). Alternatively, by setting the display timestamp, the 3 display behavior actions can be displayed in sequence according to the set order. This can increase the diversity of the display, thereby further enhancing viewing interest and improving the user viewing experience.

[0098] S402: After updating the character appearance data, voice data, and behavior data in the first smart document using the first clothing product information, the first spoken copy content, and the first display behavior action, a second smart document is obtained.

[0099] S5. Update and adjust the current AI anchor's clothing and spoken content based on the second intelligent document.

[0100] In some embodiments, reference Figure 2 , the method of the embodiment of the present application further includes the following steps:

[0101] S6. Acquire a first background image corresponding to the first clothing product information, wherein the timestamp corresponding to the first clothing product information is synchronized with the timestamp of the first background image;

[0102] S7. Use the first background image to replace the live video background.

[0103] Specifically, since different clothing items have different clothing styles, in order to highlight the atmosphere of the clothing, the current video live broadcast background can be converted into a first background image that matches the style of the clothing item. This can further improve the watchability, fun and interactivity of the live broadcast, thereby improving the efficiency of the live broadcast.

[0104] Reference Figure 3 , the embodiment of the present application also provides an AI digital human information processing system based on intelligent document generation, the system comprising:

[0105] The first display interface includes the live broadcast interface and the backend central control interface;

[0106] The background processing system includes at least one processor for executing the steps of implementing the above-mentioned AI digital human information processing method based on intelligent document generation.

[0107] Since the background processing system of this system embodiment includes at least one processor for executing the steps of the above method embodiment, the beneficial effects of the system of this embodiment are the same as those of the above method embodiment and will not be elaborated here.

[0108] Reference Figure 4 , the embodiment of the present application also provides an AI digital human information processing system based on intelligent document generation, the system comprising:

[0109] A first acquisition processing unit is used to acquire live comment content displayed on the live interface;

[0110] A first determination processing unit, configured to determine first clothing product information to be displayed based on the live broadcast comment content;

[0111] A second acquisition processing unit is used to acquire first spoken copy content corresponding to the first clothing product information;

[0112] A first update processing unit is configured to update the character appearance data and voice data in the first smart document using the first clothing product information and the first spoken copy content to obtain a second smart document;

[0113] The second update processing unit is used to update and adjust the current AI anchor's clothing and oral content according to the second intelligent document.

[0114] In some embodiments, the second acquisition processing unit specifically includes:

[0115] a first extraction processing module, configured to extract a plurality of first keywords from the first clothing product information;

[0116] A first generation processing module is used to generate corresponding first oral copy content using a plurality of first keywords;

[0117] Alternatively, a first piece of clothing product information is configured with at least two corresponding oral copy files, and the second acquisition processing unit specifically includes:

[0118] A first selection processing module is used to randomly select a spoken copy file from at least two corresponding spoken copy files;

[0119] The first determination processing module is used to use the content included in the selected oral copy file as the first oral copy content.

[0120] In some embodiments, the second obtaining processing unit in step specifically includes:

[0121] A second extraction processing module is configured to extract a plurality of first keywords from the first clothing product information when it is determined that the number of times all the spoken copy files are selected exceeds a first number threshold;

[0122] The second generation processing module is used to generate corresponding first oral copy content using a plurality of first keywords.

[0123] In some embodiments, the first update processing unit specifically includes:

[0124] A first acquisition processing module, configured to acquire a first display behavior action corresponding to the first clothing product information;

[0125] The first update processing module is used to update the character appearance data, voice data and behavior data in the first smart document using the first clothing product information, the first spoken copy content and the first display behavior action to obtain the second smart document.

[0126] In some embodiments, the system further includes:

[0127] a third acquisition processing unit, configured to acquire a first background image corresponding to the first clothing product information, wherein a timestamp corresponding to the first clothing product information is synchronized with a timestamp of the first background image;

[0128] The third update processing unit is used to replace the live video background with the first background image.

[0129] In some embodiments, the first determining processing unit specifically includes:

[0130] A first statistical processing module is configured to determine the number of occurrences of different clothing product labels from the acquired live comment content according to a set data statistical time period, wherein the clothing product labels correspond to the clothing product information in a one-to-one manner;

[0131] A second determination processing module is used to determine the corresponding first priority selection value according to the number of occurrences of the clothing product label;

[0132] A first display processing module is used to display different clothing product labels on the backend central control interface in descending order according to the size of the first priority selection value;

[0133] The third determination processing module is used to determine the clothing product label that is clicked and selected in response to the first click information input from the background central control interface, and determine the clothing product information corresponding to the clothing product label that is clicked and selected as the first clothing product information to be displayed.

[0134] In some embodiments, the second determination processing module specifically includes:

[0135] The first recognition processing submodule is used to identify the sentiment score corresponding to the clothing product label from the acquired live comment content;

[0136] The first determination processing submodule is used to determine the corresponding first priority selection value according to the number of occurrences of the clothing product label and the emotion score.

[0137] In some embodiments, the first determination processing submodule specifically includes:

[0138] The first acquisition processing submodule is used to obtain the display time interval corresponding to the clothing product label;

[0139] A second determination processing submodule is configured to determine a first adjustment coefficient corresponding to the display time interval;

[0140] The third determination processing submodule is used to determine the corresponding first priority selection value according to the number of occurrences of the clothing product label, the emotion score and the first adjustment coefficient.

[0141] In some embodiments, the first determining processing unit further specifically includes:

[0142] The first display processing unit is used to obtain the display time interval corresponding to the clothing product label, and display the display time interval as the label information of the clothing product label on the background central control interface.

[0143] Since the functional modules of the system embodiment correspond one-to-one with the steps of the above-mentioned method embodiment, the beneficial effects of the system of the present embodiment are the same as those of the above-mentioned method embodiment, and will not be elaborated on in detail here.

[0144] In addition, the present application also provides a computer-readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the above method embodiment.

[0145] For the processors mentioned in the above storage medium embodiment and system embodiment, the number can be at least one, and at least any step in the above method embodiment can be executed. When the number is at least two, at least two processors can be connected to each other for communication, not limited to wired or wireless communication connection, and the at least one processor can be connected to various intelligent terminal devices for communication. In addition, the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0146] Finally, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0147] Note that the above are only preferred embodiments of the present application and the technical principles employed. Those skilled in the art will understand that the present application is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the appended claims.

Claims

1. An AI digital human information processing method based on intelligent document generation, characterized in that: The method includes: S1. Obtain the live comment content displayed on the live interface; S2. Determine first clothing product information to be displayed based on the live broadcast comment content; The step S2 specifically includes: S201: Determine the number of occurrences of different clothing product labels from the acquired live commentary content according to a set data statistical time period, wherein the clothing product labels correspond one-to-one to the clothing product information; S202: Determine a first priority value corresponding to the clothing product label according to the number of occurrences of the clothing product label; wherein step S202 specifically includes: S2021. Identify the sentiment score corresponding to the clothing product label from the acquired live comment content; S2022: Determine a corresponding first priority value based on the number of occurrences of the clothing product label and the emotion score; wherein step S2022 specifically includes: S20221. Obtain the display time interval corresponding to the clothing product label; S20222. Determine a first adjustment coefficient corresponding to the presentation time interval; wherein, when it is determined that the presentation time interval is greater than or equal to a preset time threshold, use the first adjustment sub-value as the first adjustment coefficient; and when it is determined that the presentation time interval is less than the preset time threshold, use the second adjustment sub-value as the first adjustment coefficient, where the second adjustment sub-value is less than the first adjustment sub-value. S20223. Calculate a second product of the number of occurrences of the clothing product label, the second adjustment coefficient, and the first adjustment coefficient, and use the second product as the first priority value corresponding to the clothing product label; wherein the second adjustment coefficient corresponds to the emotion score and the two are in direct proportion; S203, displaying the labels of different clothing items on the backend central control interface in descending order according to the size of the first priority selected value; S204: In response to the first click information input from the backend central control interface, determining the clothing product label that is clicked and selected, and determining the clothing product information corresponding to the clicked clothing product label as the first clothing product information to be displayed; S3. Obtaining a first spoken copy content corresponding to the first clothing product information; S4. Update the character appearance data and voice data in the first smart document using the first clothing product information and the first spoken copy content to obtain a second smart document; S5. Update and adjust the current AI anchor's clothing and spoken content based on the second intelligent document.

2. The method according to claim 1, wherein The step S3 specifically includes: A301. Extracting a plurality of first keywords from the first clothing product information; A302. Generate corresponding first spoken copy content using a plurality of first keywords; Alternatively, a first piece of clothing product information is configured with at least two corresponding oral copy files, and step S3 specifically includes: B301. Randomly select a spoken copy file from at least two corresponding spoken copy files; B302. The content of the selected spoken copy file is used as the first spoken copy content.

3. The method according to claim 1, wherein The step S3 specifically includes: C301. When it is determined that the number of times all the spoken copy files are selected exceeds the first number threshold, extract a plurality of first keywords from the first clothing product information; C302. Generate corresponding first spoken copy content using a plurality of first keywords.

4. The method according to claim 1, wherein The step S4 specifically includes: S401: Obtain a first display behavior action corresponding to first clothing product information; S402: After updating the character appearance data, voice data, and behavior data in the first smart document using the first clothing product information, the first spoken copy content, and the first display behavior action, a second smart document is obtained.

5. The method according to claim 1, wherein The method further comprises the following steps: S6. Acquire a first background image corresponding to the first clothing product information, wherein the timestamp corresponding to the first clothing product information is synchronized with the timestamp of the first background image; S7. Use the first background image to replace the live video background.

6. The method according to claim 1, wherein The step S2 further specifically includes: S205: Obtain the display time interval corresponding to the clothing product label, and display the display time interval as the label information of the clothing product label on the background central control interface.

7. An AI digital human information processing system based on intelligent document generation, characterized in that: The system includes: The first display interface includes the live broadcast interface and the backend central control interface; A background processing system comprises at least one processor for executing the method according to any one of claims 1 to 6.

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