Cross-platform content distribution intelligent adaptation method and system

Abstract

The invention discloses a cross-platform content distribution intelligent adaptation method and system. The method comprises the following steps: acquiring multi-modal original input content; performing feature extraction on the multi-modal original input content to obtain a content feature group; obtaining platform feature parameters of the target distribution platform; obtaining a feature matching degree between the content feature group and the platform feature parameter; generating a content transformation scheme according to the platform feature parameters and the feature matching degree; and according to the content transformation scheme, carrying out transformation adjustment on the multi-modal original input content to obtain transformation content. According to the technical scheme provided by the invention, the original content is subjected to intelligent transformation adaptive to each platform; various content forms are supported, and the application scene is wide; quality evaluation and optimization are carried out on the transformation content, and the quality and the effect are improved; a publishing strategy is formulated, and content value maximization is achieved; fast access of a new platform is supported, a feature library is continuously optimized and updated, and a content modification scheme is dynamically adjusted; and the multi-platform operation efficiency is obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of media technology, and in particular to a cross-platform content distribution intelligent adaptation method and system. Background Art

[0002] In the current internet marketing landscape, businesses need to distribute and operate content across multiple social platforms (such as WeChat, Douyin, Xiaohongshu, and Kuaishou) simultaneously to reach diverse user groups and increase their reach. However, each platform has unique content rules, user demographics, and display requirements, necessitating adjustments and adaptations to the content distributed across different platforms.

[0003] At present, there are the following problems and defects in the prior art:

[0004] (1) There is a lack of an accurate and efficient cross-platform content adaptation method. Existing technologies usually require content to be produced and adjusted separately for each platform, which has high labor costs. In addition, the manual adaptation process is prone to subjective judgment bias and errors, and cannot guarantee the consistency and quality of cross-platform content.

[0005] (2) Existing content distribution tools have significant limitations, mainly limited to simple format conversion and batch publishing. They are unable to be intelligently transformed according to platform characteristics and lack the ability to evaluate and optimize content effects.

[0006] (3) The platform characteristics are not accurately grasped. The content styles of different platforms vary greatly, and the platform rules and user preferences change dynamically. It is difficult for humans to accurately grasp the characteristics of each platform.

[0007] (4) Operational efficiency is low, a lot of time is spent on repetitive content modification work, while the quality and effectiveness of the content are difficult to guarantee, and there is a lack of systematic evaluation and optimization mechanisms. Summary of the Invention

[0008] In view of the above-mentioned deficiencies in current technology, the present invention provides a cross-platform content distribution intelligent adaptation method, which extracts features from multimodal original input content, generates a content modification plan based on platform feature parameters, and intelligently transforms the original input content to adapt it to each platform.

[0009] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0010] A cross-platform content distribution intelligent adaptation method includes the following steps:

[0011] Get multimodal raw input content;

[0012] Extract features from the multimodal original input content to obtain a content feature group;

[0013] Obtain the platform characteristic parameters of the target distribution platform;

[0014] Obtaining the feature matching degree between the content feature group and the platform feature parameters;

[0015] Determine the transformation dimensions and parameters based on the platform's characteristic parameters and the degree of feature matching, and generate a content transformation plan based on the transformation dimensions and parameters;

[0016] According to the content transformation plan, the multimodal original input content is transformed and adjusted to obtain the transformed content.

[0017] According to one aspect of the present invention, the multimodal original input content includes: text, picture, video and audio.

[0018] According to one aspect of the present invention, the feature extraction of the multimodal original input content includes:

[0019] Perform word segmentation and part-of-speech tagging on text, extract keywords and topics, analyze sentiment, and identify text style;

[0020] Identify subjects in images or videos, extract composition, color, and texture features, analyze scenes and object categories, and calculate image quality scores;

[0021] Extract waveform features from audio, analyze volume and rhythm, identify background music type, and evaluate audio quality.

[0022] According to one aspect of the present invention, the cross-platform content distribution intelligent adaptation method further includes:

[0023] The content feature group is standardized according to preset rules.

[0024] According to one aspect of the present invention, the cross-platform content distribution intelligent adaptation method further includes:

[0025] Collect content rules from various platforms, analyze user preference characteristics, extract popular content features, establish a feature index system, and build a platform feature library;

[0026] Dynamically update the platform feature library.

[0027] According to one aspect of the present invention, obtaining the feature matching degree of the content feature group and the platform feature parameter includes:

[0028] Based on the deep learning model, a feature extractor is constructed to process the content feature group and platform feature parameters to obtain the feature vectors corresponding to the content feature group and platform feature parameters;

[0029] Calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

[0030] According to one aspect of the present invention, obtaining the feature matching degree of the content feature group and the platform feature parameter further includes:

[0031] Integrate multiple feature extractors and use the Boosting method to improve matching accuracy.

[0032] According to one aspect of the present invention, the cross-platform content distribution intelligent adaptation method further includes:

[0033] Conduct quality assessment on the transformation content and obtain quality assessment indicators;

[0034] According to the quality assessment indicators, the quality of the transformation content is optimized to obtain the optimized transformation content.

[0035] According to one aspect of the present invention, the cross-platform content distribution intelligent adaptation method further includes:

[0036] Develop a release strategy and publish the transformed content.

[0037] A cross-platform content distribution intelligent adaptation system, based on the cross-platform content distribution intelligent adaptation method described above, comprises:

[0038] The original content acquisition module is used to obtain multimodal original input content;

[0039] A feature extraction module is used to extract features from the multimodal original input content to obtain a content feature group;

[0040] The platform parameter acquisition module is used to obtain the platform characteristic parameters of the target distribution platform;

[0041] A matching degree acquisition module is used to obtain the feature matching degree of the content feature group and the platform feature parameters;

[0042] A transformation plan acquisition module is used to determine the transformation dimensions and transformation parameters based on the platform characteristic parameters and the characteristic matching degree, and to generate a content transformation plan based on the transformation dimensions and transformation parameters;

[0043] The transformation module is used to transform and adjust the multimodal original input content according to the content transformation plan to obtain the transformed content.

[0044] Advantages of the present invention:

[0045] The present invention provides a cross-platform content distribution intelligent adaptation method, which extracts features from multimodal original input content, generates a content transformation plan based on platform characteristic parameters, performs intelligent transformation on the original input content to adapt to each platform, and then distributes it to the corresponding platform.

[0046] The present invention establishes a complete content feature analysis system, conducts intelligent transformation of distributed content based on platform features, and automatically adapts to the content display requirements of each platform; it realizes that the content intelligently matches the display rules and user habits of each platform while maintaining the original core information; it supports multiple content formats and is applicable to a wide range of scenarios; it ensures the quality of the transformed content, conducts quality assessment and optimization on the transformed content, and improves the quality and effect of the distributed content; it formulates a publishing strategy for the transformed content to maximize the value of the content; it supports rapid access to new platforms, continuous optimization and updating of the feature library, and dynamic adjustment of content transformation plans; it significantly improves the operational efficiency of multiple platforms and reduces labor costs; it solves the problems of time-consuming and unstable effects of traditional manual adaptation. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 This is a flow chart of a cross-platform content distribution intelligent adaptation method according to the first embodiment of the present invention;

[0049] Figure 2 This is a flowchart of a cross-platform content distribution intelligent adaptation method described in Example 2 of the present invention. DETAILED DESCRIPTION

[0050] 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 creative efforts are within the scope of protection of the present invention.

[0051] Example 1

[0052] like Figure 1 As shown, a cross-platform content distribution intelligent adaptation method includes the following steps:

[0053] S1: Obtain multimodal original input content.

[0054] In practical applications, the multimodal original input content includes: text, pictures, videos, audio, etc.

[0055] S2: Extract features from the multimodal original input content to obtain a content feature group.

[0056] Specifically, the feature extraction of the multimodal original input content includes:

[0057] Perform word segmentation and part-of-speech tagging on text, extract keywords and topics, analyze sentiment, and identify text style;

[0058] Identify subjects in images or videos, extract features such as composition, color, and texture, analyze scenes and object categories, and calculate image quality scores;

[0059] Extract waveform features from audio, analyze volume and rhythm, identify background music type, and evaluate audio quality.

[0060] In practical applications, a deep learning network can be constructed to perform multi-dimensional feature extraction and fusion on the original input content. The visual feature vector dimension of the final image or video is 512, the text feature vector dimension is 256, and the audio feature vector dimension is 128.

[0061] Preferably, the method further comprises:

[0062] The content feature group is standardized according to preset rules.

[0063] Typically, feature normalization is used to standardize the content feature group.

[0064] S3: Obtain the platform characteristic parameters of the target distribution platform.

[0065] Specifically, platform characteristic parameters include key parameters such as content rules, user preferences, and display requirements of the target platform.

[0066] Preferably, the method further comprises:

[0067] Collect content rules from various platforms, analyze user preference characteristics, extract popular content features, establish a feature index system, and build a platform feature library;

[0068] Dynamically update the platform feature library.

[0069] The platform's rules, user preferences, and popular features are not static. Dynamic updates to the platform's feature library can be set up, typically every seven days, to maintain continuous optimization and updating. This dynamic update allows for dynamic adjustments to content modification plans based on real-time hot topics.

[0070] By pre-building a platform feature library, the platform feature parameters of the target distribution platform can be directly obtained from the platform feature library, which can improve processing efficiency.

[0071] When a new platform needs to distribute content, the rules and features of the new platform are collected and added to the platform feature library to support rapid access to the new platform.

[0072] S4: Obtain the feature matching degree between the content feature group and the platform feature parameters.

[0073] In practical applications, similarity calculation methods such as cosine similarity, Euclidean distance, and Pearson correlation coefficient can be used to calculate the feature similarity of content feature groups and platform feature parameters corresponding to each dimension. The different feature similarities are then normalized and weighted to sum them, ultimately yielding a feature matching score. Weights can also be set based on the importance of different features.

[0074] Preferably, the feature matching degree between the content feature group and the platform feature parameters can also be obtained according to the following method:

[0075] Based on the deep learning model, a feature extractor is constructed to process the content feature group and platform feature parameters to obtain the feature vectors corresponding to the content feature group and platform feature parameters;

[0076] Calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

[0077] In actual applications, the content feature group and platform feature parameters may have different dimensions and are not one-to-one corresponding feature data. We can construct a Transformer model, a multi-layer perceptron, a feature pyramid for multi-scale feature extraction, etc., to extract features from both separately and convert them into dimensionally aligned and content-corresponding feature vectors; then, use similarity calculation methods such as cosine similarity and Euclidean distance to calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

[0078] For example, the Transformer model is used to obtain feature vectors by constructing different Transformer model feature extractors for content feature groups and platform feature parameters, concatenating the content feature groups and platform feature parameters into a single vector, inputting it into the corresponding Transformer model for processing, and finally outputting a dimensionally aligned feature vector corresponding to the content. By converting the content feature group or platform feature parameters into an input vector and inputting it into the Transformer model's encoder, the encoder can capture the contextual information and long-range dependencies in the input data and generate a context-rich representation. The Transformer encoder then outputs the extracted feature vector.

[0079] Preferably, in order to improve the accuracy of feature matching calculation, multiple feature extractors can be integrated and a Boosting method can be used to improve the matching accuracy.

[0080] Specifically, feature vectors are extracted from the output of each feature extraction network and combined into a larger feature set as the input of the Boosting model. The weak learners in the Boosting model are trained. In each iteration, the sample weights are adjusted based on the performance of the weak learners, so that subsequent weak learners pay more attention to the previously erroneous samples. After training is completed, the outputs of all weak learners are weighted and combined according to their weights to form the final strong learner. Boosting adjusts the weights of each weak learner during training, and the output is a feature vector that combines the features extracted by multiple networks and the weighted combination of Boosting. Finally, the feature vector obtained by the Boosting method is used to calculate the feature matching degree.

[0081] S5: Determine the transformation dimensions and transformation parameters based on the platform feature parameters and feature matching degree, and generate a content transformation plan based on the transformation dimensions and transformation parameters.

[0082] Step S5 includes:

[0083] S51: Determine the transformation dimension based on the platform characteristic parameters and characteristic matching degree.

[0084] In practical applications, the transformation dimensions generally include:

[0085] Visual transformation includes brightness, contrast, composition, filters, image format, pixel size, video playback rate, etc. In practical applications, in addition to adjusting the image style, format conversion and image cropping are often required.

[0086] Copywriting modification includes length, style, coding format, punctuation, etc.

[0087] Audio modification includes volume, rhythm, background sound effects, audio format, etc.

[0088] S52: Calculate transformation parameters of the transformation dimension.

[0089] S53: Generate transformation steps according to the transformation dimensions and transformation parameters.

[0090] S54: Set the protection rules for the transformation steps.

[0091] During the transformation process, it is necessary to ensure that the core information remains unchanged and that the rules are compliant.

[0092] S6: According to the content transformation plan, the multimodal original input content is transformed and adjusted to obtain the transformed content.

[0093] Specifically, step S6 includes:

[0094] Adjust the original multimodal input content according to the content transformation plan. When adjusting, keep the core information unchanged, ensure compliance with the rules, and optimize the display effect.

[0095] In actual applications, the original input content is intelligently transformed according to the content transformation plan, including format conversion, image cropping, composition optimization, text rewriting, audio adjustment, etc.

[0096] Preferably, the method further comprises:

[0097] S7: Develop a release strategy and publish the transformed content.

[0098] Specifically, formulating a release strategy includes: recommending release time, generating tag suggestions and tag combinations, designing strategies for interacting with users, formulating monitoring plans, etc.

[0099] Finally, according to the formulated release strategy, publish content that is intelligently transformed and adapted for the platform, and then interact with users to maximize the value of the content.

[0100] Preferably, after the content is released, the generation of content transformation plans and the formulation of release strategies can be adjusted based on user feedback.

[0101] The beneficial effects of this embodiment are: this method establishes a complete content feature analysis system, performs intelligent transformation of distributed content based on platform features, and automatically adapts to the content display requirements of each platform; it realizes that the content is intelligently matched with the display rules and user habits of each platform while maintaining the original core information; it supports multiple content formats and is applicable to a wide range of scenarios; it ensures the quality of the transformed content and improves the content effect; it formulates a publishing strategy for the transformed content to maximize the content value; it supports the rapid access of new platforms, continuous optimization and updating of the feature library, and dynamic adjustment of content transformation plans; it significantly improves the efficiency of multi-platform operations and reduces labor costs; it solves the problem of time-consuming and unstable effects of traditional manual adaptation.

[0102] Example 2

[0103] like Figure 2 As shown, a cross-platform content distribution intelligent adaptation method includes the following steps:

[0104] S1: Obtain multimodal original input content.

[0105] In practical applications, the multimodal original input content includes: text, pictures, videos, audio, etc.

[0106] S2: Extract features from the multimodal original input content to obtain a content feature group.

[0107] Specifically, the feature extraction of the multimodal original input content includes:

[0108] Perform word segmentation and part-of-speech tagging on text, extract keywords and topics, analyze sentiment, and identify text style;

[0109] Identify subjects in images or videos, extract features such as composition, color, and texture, analyze scenes and object categories, and calculate image quality scores;

[0110] Extract waveform features from audio, analyze volume and rhythm, identify background music type, and evaluate audio quality.

[0111] In practical applications, multi-dimensional feature extraction and fusion are performed on the original input content. The dimension of the visual feature vector of the final image or video is 512, the dimension of the text feature vector is 256, and the dimension of the audio feature vector is 128.

[0112] Preferably, the method further comprises:

[0113] The content feature group is standardized according to preset rules.

[0114] Typically, feature normalization is used to standardize the content feature group.

[0115] S3: Obtain the platform characteristic parameters of the target distribution platform.

[0116] Specifically, platform characteristic parameters include key parameters such as content rules, user preferences, and display requirements of the target platform.

[0117] Preferably, the method further comprises:

[0118] Collect content rules from various platforms, analyze user preference characteristics, extract popular content features, establish a feature index system, and build a platform feature library;

[0119] Dynamically update the platform feature library.

[0120] The platform's rules, user preferences, and popular features are not static. Dynamic updates to the platform's feature library can be set up, typically every seven days, to maintain continuous optimization and updating. This dynamic update allows for dynamic adjustments to content modification plans based on real-time hot topics.

[0121] By pre-building a platform feature library, the platform feature parameters of the target distribution platform can be directly obtained from the platform feature library, which can improve processing efficiency.

[0122] When a new platform needs to distribute content, the rules and features of the new platform are collected and added to the platform feature library to support rapid access to the new platform.

[0123] S4: Obtain the feature matching degree between the content feature group and the platform feature parameters.

[0124] In practical applications, similarity calculation methods such as cosine similarity, Euclidean distance, and Pearson correlation coefficient can be used to calculate the feature similarity of content feature groups and platform feature parameters corresponding to each dimension. The different feature similarities are then normalized and weighted to sum them, ultimately yielding a feature matching score. Weights can also be set based on the importance of different features.

[0125] Preferably, the feature matching degree between the content feature group and the platform feature parameters can also be obtained according to the following method:

[0126] Based on the deep learning model, a feature extractor is constructed to process the content feature group and platform feature parameters to obtain the feature vectors corresponding to the content feature group and platform feature parameters;

[0127] Calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

[0128] In actual applications, the content feature group and platform feature parameters may have different dimensions and are not one-to-one corresponding feature data. We can construct a Transformer model, a multi-layer perceptron, a feature pyramid for multi-scale feature extraction, etc., to extract features from both separately and convert them into dimensionally aligned and content-corresponding feature vectors; then, use similarity calculation methods such as cosine similarity and Euclidean distance to calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

[0129] For example, the Transformer model is used to obtain feature vectors by constructing different Transformer model feature extractors for content feature groups and platform feature parameters, concatenating the content feature groups and platform feature parameters into a single vector, inputting it into the corresponding Transformer model for processing, and finally outputting a dimensionally aligned feature vector corresponding to the content. By converting the content feature group or platform feature parameters into an input vector and inputting it into the Transformer model's encoder, the encoder can capture the contextual information and long-range dependencies in the input data and generate a context-rich representation. The Transformer encoder then outputs the extracted feature vector.

[0130] Preferably, in order to improve the accuracy of feature matching calculation, multiple feature extractors can be integrated and a Boosting method can be used to improve the matching accuracy.

[0131] Specifically, feature vectors are extracted from the output of each feature extraction network and combined into a larger feature set as the input of the Boosting model. The weak learners in the Boosting model are trained. In each iteration, the sample weights are adjusted based on the performance of the weak learners, so that subsequent weak learners pay more attention to the previously erroneous samples. After training is completed, the outputs of all weak learners are weighted and combined according to their weights to form the final strong learner. Boosting adjusts the weights of each weak learner during training, and the output is a feature vector that combines the features extracted by multiple networks and the weighted combination of Boosting. Finally, the feature vector obtained by the Boosting method is used to calculate the feature matching degree.

[0132] S5: Determine the transformation dimensions and transformation parameters based on the platform feature parameters and feature matching degree, and generate a content transformation plan based on the transformation dimensions and transformation parameters.

[0133] Step S5 includes:

[0134] S51: Determine the transformation dimension based on the platform characteristic parameters and characteristic matching degree.

[0135] In practical applications, the transformation dimensions generally include:

[0136] Visual transformation includes brightness, contrast, composition, filters, image format, pixel size, video playback rate, etc. In practical applications, in addition to adjusting the image style, format conversion and image cropping are often required.

[0137] Copywriting modification, including length, style, coding format, punctuation, etc.

[0138] Audio modification includes volume, rhythm, background sound effects, audio format, etc.

[0139] S52: Calculate transformation parameters of the transformation dimension.

[0140] S53: Generate transformation steps according to the transformation dimensions and transformation parameters.

[0141] S54: Set the protection rules for the transformation steps.

[0142] During the transformation process, it is necessary to ensure that the core information remains unchanged and that the rules are compliant.

[0143] In actual applications, in addition to the above-mentioned transformation dimensions, content transformation solutions also include format conversion, image cropping, etc.

[0144] S6: According to the content transformation plan, the multimodal original input content is transformed and adjusted to obtain the transformed content.

[0145] Specifically, step S6 includes:

[0146] Adjust the original multimodal input content according to the content transformation plan. When adjusting, keep the core information unchanged, ensure compliance with the rules, and optimize the display effect.

[0147] In actual applications, the original input content is intelligently transformed according to the content transformation plan, including format conversion, image cropping, composition optimization, text rewriting, audio adjustment, etc.

[0148] S7: Conduct quality assessment on the transformation content and obtain quality assessment indicators.

[0149] Specifically, step S7 includes:

[0150] Calculate compliance scores for transformed content, evaluate content quality, predict dissemination effects, analyze user experience, and obtain quality assessment indicators.

[0151] In practical applications, quality assessment indicators include:

[0152] Compliance indicators include sensitive words, prohibited content, format specifications, etc.

[0153] Quality indicators include clarity, completeness, consistency, etc.

[0154] Effectiveness indicators include attractiveness, spreadability, interactivity, etc.

[0155] S8: Optimize the quality of the transformation content according to the quality assessment indicators to obtain optimized transformation content.

[0156] Specifically, based on the quality assessment indicators, it is determined whether the modified content needs to be optimized. A threshold requirement for the quality assessment indicator can be set, and the indicator score obtained in step S7 is compared with the threshold to identify the problem points that do not meet the requirements. A quality report is then output, and optimization suggestions are given to optimize the quality of the modified content.

[0157] S9: Develop a release strategy and publish the transformed content.

[0158] Specifically, formulating a release strategy includes: recommending release time, generating tag suggestions and tag combinations, designing strategies for interacting with users, formulating monitoring plans, etc.

[0159] Finally, according to the formulated release strategy, publish content that is intelligently transformed and adapted for the platform, and then interact with users to maximize the value of the content.

[0160] Preferably, after the content is released, the generation of content transformation plans and the formulation of release strategies can be adjusted based on user feedback.

[0161] The beneficial effect of this embodiment is that: this method also performs quality assessment and optimization on the transformed content during the transformation process, improves the quality of the distributed content, ensures that the converted content complies with platform specifications and maintains its original attractiveness, and maximizes its value.

[0162] Example 3

[0163] A cross-platform content distribution intelligent adaptation system, based on the cross-platform content distribution intelligent adaptation method as described in Embodiment 1 or 2, includes:

[0164] The original content acquisition module is used to obtain multimodal original input content;

[0165] A feature extraction module is used to extract features from the multimodal original input content to obtain a content feature group;

[0166] The platform parameter acquisition module is used to obtain the platform characteristic parameters of the target distribution platform;

[0167] A matching degree acquisition module is used to obtain the feature matching degree of the content feature group and the platform feature parameters;

[0168] A transformation plan acquisition module is used to determine the transformation dimensions and transformation parameters based on the platform characteristic parameters and the characteristic matching degree, and to generate a content transformation plan based on the transformation dimensions and transformation parameters;

[0169] The transformation module is used to transform and adjust the multimodal original input content according to the content transformation plan to obtain the transformed content.

[0170] Preferably, the system further includes:

[0171] The quality assessment module is used to conduct quality assessment on the transformation content and obtain quality assessment indicators;

[0172] The optimization module is used to optimize the quality of the transformation content according to the quality evaluation indicators to obtain the optimized transformation content.

[0173] Preferably, the system further includes:

[0174] The release module is used to formulate release strategies and publish the transformed content.

[0175] Example 4

[0176] A computer program, which, when executed, implements the steps of the cross-platform content distribution intelligent adaptation method as described in embodiment one or two.

[0177] Example 5

[0178] A readable storage medium stores a computer program as described in Example 4, and when the computer program is executed, the steps of the cross-platform content distribution intelligent adaptation method as described in Example 1 or 2 are implemented.

[0179] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A cross-platform content distribution intelligent adaptation method, characterized in that: The following steps are involved: Get multimodal raw input content; Extract features from the multimodal original input content to obtain a content feature group; Obtain the platform characteristic parameters of the target distribution platform; Obtaining the feature matching degree between the content feature group and the platform feature parameters; Determine the transformation dimensions and parameters based on the platform's characteristic parameters and the degree of feature matching, and generate a content transformation plan based on the transformation dimensions and parameters; According to the content transformation plan, the multimodal original input content is transformed and adjusted to obtain the transformed content.

2. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The multimodal original input content includes: text, pictures, videos and audio.

3. The cross-platform content distribution intelligent adaptation method according to claim 2, characterized in that: The feature extraction of the multimodal original input content includes: Perform word segmentation and part-of-speech tagging on text, extract keywords and topics, analyze sentiment, and identify text style; Identify subjects in images or videos, extract composition, color, and texture features, analyze scenes and object categories, and calculate image quality scores; Extract waveform features from audio, analyze volume and rhythm, identify background music type, and evaluate audio quality.

4. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The cross-platform content distribution intelligent adaptation method further includes: The content feature group is standardized according to preset rules.

5. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The cross-platform content distribution intelligent adaptation method further includes: Collect content rules from various platforms, analyze user preference characteristics, extract popular content features, establish a feature index system, and build a platform feature library; Dynamically update the platform feature library.

6. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The obtaining of the feature matching degree between the content feature group and the platform feature parameters includes: Based on the deep learning model, a feature extractor is constructed to process the content feature group and platform feature parameters to obtain the feature vectors corresponding to the content feature group and platform feature parameters; Calculate the feature matching degree of the feature vectors corresponding to the content feature group and the platform feature parameters.

7. The cross-platform content distribution intelligent adaptation method according to claim 6, characterized in that: The obtaining of the feature matching degree of the content feature group and the platform feature parameters further includes: Integrate multiple feature extractors and use the Boosting method to improve matching accuracy.

8. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The cross-platform content distribution intelligent adaptation method further includes: Conduct quality assessment on the transformation content and obtain quality assessment indicators; According to the quality assessment indicators, the quality of the transformation content is optimized to obtain the optimized transformation content.

9. The cross-platform content distribution intelligent adaptation method according to claim 1, characterized in that: The cross-platform content distribution intelligent adaptation method further includes: Develop a release strategy and publish the transformed content.

10. A cross-platform content distribution intelligent adaptation system, characterized in that: The cross-platform content distribution intelligent adaptation method according to any one of claims 1 to 9 comprises: The original content acquisition module is used to obtain multimodal original input content; A feature extraction module is used to extract features from the multimodal original input content to obtain a content feature group; The platform parameter acquisition module is used to obtain the platform characteristic parameters of the target distribution platform; A matching degree acquisition module is used to obtain the feature matching degree of the content feature group and the platform feature parameters; A transformation plan acquisition module is used to determine the transformation dimensions and transformation parameters based on the platform characteristic parameters and the characteristic matching degree, and to generate a content transformation plan based on the transformation dimensions and transformation parameters; The transformation module is used to transform and adjust the multimodal original input content according to the content transformation plan to obtain the transformed content.

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