Cross-platform information interaction method and device, equipment and medium

Abstract

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a cross-platform information interaction method, device and equipment and a medium, and the method comprises the steps: obtaining original information sent by a source platform, and analyzing the original information into a standardized data structure according to a preset data format; extracting multimedia content elements from the structure and performing format conversion to generate standardized multimedia content; packaging the structure and the content into a to-be-transmitted data packet; acquiring network delay and bandwidth parameters of the target platform, and selecting a transmission path based on the parameters; compressing the to-be-transmitted data packet and sending the compressed to-be-transmitted data packet to the target platform through the selected path; and the target platform receives and analyzes the compressed data packet and presents the original information content. Information format compatibility is achieved through a standardized structure and content packaging, transmission efficiency is improved through network parameter perception and path selection, complete presentation of information is guaranteed in combination with compression processing and terminal adaptation, and stability and consistency of cross-platform interaction are enhanced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a cross-platform information interaction method, apparatus, device, and storage medium. Background Technology

[0002] In existing cross-platform information exchange systems, there are significant differences between different platforms in terms of functional support, data processing capabilities, and message format compatibility, which seriously affects the accuracy and consistency of information transmission.

[0003] In the fintech business sector, business systems typically need to push financial notifications, transaction alerts, and review process information to users through various terminal devices (such as mobile apps, web pages, and SMS channels). However, differences between existing platforms in terms of message parsing formats, rich media support, and transmission performance can easily lead to inconsistencies in information display, missing data, or inability to be parsed properly during the transmission process. This directly affects users' understanding and response to business notifications and may even create compliance risks in financial risk control scenarios.

[0004] In the healthcare sector, medical institutions and health management platforms often rely on multiple platforms to deliver treatment reminders, test result notifications, and health reports to patients, especially requiring the simultaneous transmission of multimedia information and structured data. However, current platforms have varying capabilities in supporting multimedia content (such as medical images and video explanations), and their message format processing capabilities are inconsistent. This often leads to problems such as formatting errors, missing key content, or inability to open information sent by doctors at the receiving end. This not only affects the efficiency of doctor-patient communication but may also adversely impact the quality of diagnosis and treatment.

[0005] Furthermore, due to differences in network conditions, system capabilities, and data processing mechanisms across platforms, existing technologies lack effective dynamic transmission path optimization mechanisms when facing environments with limited network bandwidth or high latency. This results in problems such as unstable information transmission, high latency, and high failure rates during cross-platform information exchange. These performance inconsistencies are particularly prominent in scenarios requiring timely financial early warning information and emergency medical notifications.

[0006] More seriously, current information interaction technologies mostly employ static data structures and transmission paths when processing information encapsulation and transmission, lacking mechanisms for flexible adjustment based on the capabilities of the receiving platform. This results in significant differences in the presentation of the same information content across different platforms, making it difficult to guarantee the uniformity and controllability of cross-platform interaction. At the functional support level, some platforms lack the ability to synchronously encapsulate and interpret structured data and multimedia content, further exacerbating the problem of insufficient cross-platform information reconstruction capabilities. Summary of the Invention

[0007] The main objective of this invention is to provide a cross-platform information interaction method, apparatus, device, and storage medium, aiming to solve the technical problem that the existing technology lacks a cross-platform information interaction mechanism that can uniformly encapsulate structured data and multimedia content according to the capabilities of different platforms and adaptively select the optimal transmission path.

[0008] To achieve the above objectives, the present invention provides a cross-platform information interaction method, comprising:

[0009] Obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to a preset data format;

[0010] Multimedia content elements are extracted from the standardized data structure, and format conversion is performed on the multimedia content elements to generate standardized multimedia content.

[0011] The standardized data structure and the standardized multimedia content are encapsulated into a data packet to be transmitted.

[0012] Obtain the network latency and bandwidth parameters of the target platform, and select a transmission path based on the network latency and bandwidth parameters;

[0013] The data packet to be transmitted is compressed to generate a compressed data packet, and the compressed data packet is sent to the target platform through the transmission path;

[0014] The target platform receives and parses the compressed data packet to present the original information content.

[0015] Furthermore, to achieve the above objectives, the present invention provides a cross-platform information interaction device, comprising:

[0016] The source information parsing module is used to obtain the original information sent by the source platform and parse the original information into a standardized data structure according to a preset data format.

[0017] A multimedia format conversion module is used to extract multimedia content elements from the standardized data structure and perform format conversion on the multimedia content elements to generate standardized multimedia content.

[0018] A data encapsulation module is used to encapsulate the standardized data structure and the standardized multimedia content into a data packet to be transmitted.

[0019] The transmission path selection module is used to obtain the network latency and bandwidth parameters of the target platform, and select the transmission path according to the network latency and bandwidth parameters;

[0020] The data compression and transmission module is used to compress the data packet to be transmitted to generate a compressed data packet, and send the compressed data packet to the target platform through the transmission path;

[0021] The target platform parsing and presentation module is used to receive and parse the compressed data packet through the target platform and present the original information content.

[0022] Furthermore, to achieve the above objectives, the present invention also provides a computer device, the computer device including a memory, a processor, and a cross-platform information interaction program stored in the memory and executable on the processor, wherein the cross-platform information interaction program, when executed by the processor, implements the steps of the cross-platform information interaction method as described above.

[0023] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a cross-platform information interaction program, wherein the cross-platform information interaction program, when executed by a processor, implements the steps of the cross-platform information interaction method as described above.

[0024] Beneficial Effects: This invention relates to the field of data processing technology and can be applied to business scenarios such as fintech and healthcare. It discloses a cross-platform information interaction method, apparatus, device, and medium, comprising: acquiring raw information sent by a source platform and parsing it into a standardized data structure according to a preset data format; extracting multimedia content elements from the structure and performing format conversion to generate standardized multimedia content; encapsulating the structure and content into a data packet to be transmitted; acquiring the network latency and bandwidth parameters of the target platform and selecting a transmission path based on these parameters; compressing the data packet to be transmitted and sending it to the target platform through the selected path; and the target platform receiving and parsing the compressed data packet to present the original information content. This invention achieves information format compatibility between different platforms through the unified encapsulation of standardized data structures and multimedia content; improves data transmission efficiency and stability through network parameter awareness and path selection mechanisms; and ensures complete information restoration at the presentation end by combining compression processing and platform adaptation, thereby improving the reliability and consistency of cross-platform interaction without changing the original data content. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments. In the accompanying drawings:

[0026] Figure 1 This is a schematic diagram of an application environment for a cross-platform information interaction method according to an embodiment of the present invention;

[0027] Figure 2 This is a flowchart illustrating an embodiment of the cross-platform information interaction method of the present invention;

[0028] Figure 3 This is a schematic diagram of the functional modules of a preferred embodiment of the cross-platform information interaction device of the present invention;

[0029] Figure 4 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention;

[0030] Figure 5 This is another structural schematic diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0031] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0032] The cross-platform information interaction method provided in this invention can be applied to, for example... Figure 1 In this application environment, the user terminal communicates with the server via a network. The server can obtain the original information sent by the source platform from the user terminal, parse it into a standardized data structure according to a preset data format, extract multimedia content elements from the structure and perform format conversion to generate standardized multimedia content, encapsulate the structure and content into a data packet to be transmitted, obtain the network latency and bandwidth parameters of the target platform, select a transmission path based on these parameters, compress the data packet to be transmitted and send it to the target platform through the selected path, and the target platform receives and parses the compressed data packet to present the original information content. This invention achieves information format compatibility between different platforms through standardized data structures and unified encapsulation of multimedia content; improves data transmission efficiency and stability through network parameter awareness and path selection mechanisms; and ensures complete information restoration at the presentation end by combining compression processing and platform adaptation, thereby improving the reliability and consistency of cross-platform interaction without changing the original data content. The user terminal can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices. The server can be implemented using a standalone server or a server cluster consisting of multiple servers. The invention will be described in detail below through specific embodiments.

[0033] Please see Figure 2 , Figure 2 This is a flowchart illustrating an embodiment of the cross-platform information interaction method provided by the present invention. It should be noted that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0034] like Figure 2 As shown, the cross-platform information interaction method proposed in this invention includes the following steps:

[0035] S10, Obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to the preset data format;

[0036] In this embodiment, obtaining raw information from the source platform involves data interaction with different types of information sources. At the information acquisition level, it needs to support multiple platform protocols, such as HTTP / HTTPS, WebSocket, MQTT, and SMTP, to adapt to the data push patterns of heterogeneous platforms. The information acquisition operation is typically completed by the receiving end listening module. This module, by configuring the source platform address, port, authentication credentials, and message listening mechanism, immediately caches the received data in a specific format to the access cache. Raw information can include various types of information such as SMS content, push messages, notifications, and customer interaction records. Specific formats include structured fields (such as JSON or XML formats), unstructured content (such as free text, mixed text and image fragments, and coded attachment data), and complex data structures with multi-level nested relationships, capable of comprehensively supporting the data needs of different financial platforms in business transmission, user services, and compliance communication.

[0037] To effectively parse the raw information, a pre-defined data format containing field mappings, structure definitions, and data type specifications must first be constructed. This pre-defined format is typically generated through a unified inter-platform protocol interface definition (API schema) and modeled using an extensible data description language (such as XSD, Protobuf, or Avro) to ensure compatibility and scalability. Upon receiving the raw information, the parsing module automatically selects field parsing rules corresponding to the source platform identifier based on the pre-defined data format. For example, it may extract field values ​​using regular expressions, extract structure nodes using XPath, or combine semantic analysis to identify implicit fields in unstructured text.

[0038] The fields in the original information need to undergo format standardization, unit standardization, and name remapping. The process of generating a standardized data structure includes operations such as field renaming, data type conversion (e.g., string to timestamp, decimal to percentage), and structure rearrangement (e.g., reorganizing a flat structure into a nested tree hierarchy). The standardized result is represented by a data structure model, often using tree structures (e.g., DOM node trees, hierarchical JSON structures) to manage the hierarchical relationships and contextual positions between fields, supporting subsequent data extraction and rendering. Each node in the standardized data structure contains meta-attributes such as field name, field value, field type, context path, and associated tags. This information not only serves as the basis for visualizing the structure but also as the fundamental data source for subsequent content processing, compression transmission, and content restoration.

[0039] In one implementation, the receiving end deploys a listening program that connects to the source platform's REST API. After authentication via the OAuth 2.0 protocol, it receives data packets pushed via POST. The received data packets are in JSON format and contain fields such as user message, sending time, and attachment link. The system uses a predefined field mapping configuration file to match the original field names with standard field names, applies a unified ISO 8601 format conversion to the time field, uniformly retains two decimal places for the currency field, expands nested data into a flat structure, constructs a node relationship graph, and finally reconstructs it into a standardized data structure of JSON, where the nesting level is explicitly identified by the path field to indicate parent-child relationships.

[0040] In another implementation, the source platform sends the original email content using the SMTP protocol. The receiving module parses the email header to obtain sender and timestamp information, and then parses the XML structure contained in the body content. For this type of structure, the system uses an XSD template-based structure recognition engine to identify field hierarchy, constructs a node structure through tree navigation, and performs content cleaning operations on specific node values ​​(such as removing HTML tags and decoding Base64 content), thereby generating a standardized data structure with clear structure, consistent field types, and unified semantics.

[0041] It can also support continuously receiving raw information from asynchronous event streams via the WebSocket interface, mapping fields in the events to fields in the standard structure in real time according to rules, and continuously appending them to the structured record set, thereby realizing online structured reconstruction of the data stream.

[0042] Example Explanation: In the field of fintech business, the transaction notifications sent by the bank counter system and the account update messages pushed by the mobile terminal have significantly different structures. By using a unified preset data format model, fields such as transaction amount, transaction time, and account information can be integrated at the structural level to form a standardized data structure, providing a foundation for subsequent cross-platform display.

[0043] In the healthcare sector, the structured reports uploaded to electronic medical record systems by different hospitals often differ in format from the raw test results sent by laboratories, with inconsistent field naming and disorganized hierarchical structures. This processing mechanism can reconstruct patient basic information, test results, diagnostic opinions, and other content into a unified structure, providing standardized input for subsequent data analysis and content visualization.

[0044] This embodiment solves the problem of inconsistent multi-source heterogeneous data structures by unifying the format and reorganizing the structure of the raw information sent by the source platform. It also significantly improves the universality and operability of the parsed data. The unified, standardized data structure serves as an intermediate representation, providing a clear data source and structural reference for subsequent data extraction, content conversion, encapsulation, and transmission. By using a tree-like hierarchical structure to express the hierarchical dependencies of fields, it effectively supports unified parsing and rendering of multi-level data, avoiding information loss and formatting errors.

[0045] S20, extract multimedia content elements from the standardized data structure, and perform format conversion on the multimedia content elements to generate standardized multimedia content;

[0046] In this embodiment, the extraction of multimedia content elements is based on the field hierarchy and content tagging features of a standardized data structure. In the standardized data structure, each field node contains meta-attributes such as field name, field value, data type tag, and path information. The system identifies multimedia content elements by traversing field nodes and matching data type tags. Multimedia content elements include images, audio, video, and animations, and their identification logic relies on field value features, such as MIME type tags (e.g., image / jpeg, audio / mp3), file extensions (e.g., .png, .mp4), and data prefixes (e.g., binary fragments starting with base64). After identification, the system associates and stores the multimedia content with its position in the data structure based on the path information, ensuring contextual consistency.

[0047] Format conversion refers to converting multimedia content from different sources, with different encodings, or in different container formats into a preset target standard format to ensure the content's compatibility and controllability on the target platform. This operation may include container format conversion (e.g., MOV to MP4), encoding method re-encoding (e.g., H.265 to H.264), and adjustments to parameters such as frame rate, resolution, adaptive bitrate, and image compression ratio. Based on the configured conversion strategy, the system automatically processes the extracted multimedia content elements, calling multimedia processing engines (e.g., FFmpeg, OpenCV, SoX), and performing operations such as image compression and resampling, audio noise reduction and bitrate unification, and video keyframe reconstruction and container re-encoding according to the specific content type.

[0048] The generation of standardized multimedia content includes not only format uniformity but also synchronous updates to the metadata structure. The system constructs a set of descriptive fields that match the converted multimedia content, including parameters such as file size, media duration, encoding format, and resolution. These fields are then mapped and aligned with the original fields to ultimately form standardized multimedia content that is structurally complete, formatted uniformly, and semantically clear, serving as the input source for subsequent content encapsulation and transmission.

[0049] In one implementation, the system identifies a field labeled as type "image" in a standardized data structure, whose value is base64 encoded JPEG image data. The system decodes this field value into an image file and converts it to PNG format using an image processing module, standardizing the image resolution to 720×1280 and compressing the image size to within 500KB. The converted image file is saved to the multimedia content directory, and the mapping relationship between the corresponding path and the original field location is recorded.

[0050] In another implementation, the original field contains a URL link to an external video file. The system downloads the MP4 file via this link and detects its encoding format and resolution. If the video resolution exceeds 1080p, the system calls a transcoding engine to re-encode it into H.264 format, compress the resolution to 720p, limit the frame rate to 25fps, and extract the audio track information to generate a synchronized container. The final video file is saved in a standard container format and includes attribute information such as file duration, video bitrate, and containerization time.

[0051] It can also process HTML tag content embedded in text fields and extract the content. Label, <audio>Tags or <video>The tag's src attribute is used to download multimedia and reconstruct the format of these referenced links, generating data files supported by the standard structure, and completing any missing encapsulation information.

[0052] Example Explanation: In the healthcare field, patient records often include diagnostic imaging images, pathological slide photographs, or video examination data. Different hospitals may use different image encoding and encapsulation methods in the data structures they upload. By identifying and extracting image fields from standardized data structures and unifying image resolution and encoding formats, we can ensure clear images and strong compatibility when remotely viewing and diagnosing within healthcare information systems.

[0053] In the fintech sector, user identity verification information often includes selfie videos, ID photos, and voice signatures, uploaded via embedded fields or external video links. The system extracts this multimedia content and converts it into a standardized format, enabling the intelligent review engine to recognize content uploaded from different user platforms in a standardized manner. This improves review efficiency and avoids review failures caused by format incompatibility.

[0054] This embodiment extracts and transforms multimedia content elements from a standardized data structure, unifying heterogeneous media formats into a format compatible with the target platform while preserving the complete content structure and metadata information. The format conversion process combines structural reconstruction and meta-attribute mapping, ensuring high consistency and adaptability of the content in subsequent use, effectively improving the cross-platform transmission efficiency and display consistency of multimedia content.

[0055] S30, the standardized data structure and the standardized multimedia content are encapsulated into a data packet to be transmitted;

[0056] In this embodiment, the core of the encapsulation operation lies in merging standardized data structures and standardized multimedia content into a unified and standardized data representation format to construct a data packet adapted for cross-platform transmission. The standardized data structure is a collection of structured information obtained from the preceding operations, including field hierarchy, field names, data types, field values, and metadata descriptions, organized through key-value pair mappings or serialized objects. Standardized multimedia content refers to media resources that have undergone unified format conversion, including image files, audio streams, video segments, animation clips, etc., possessing predefined encapsulation formats and consistent encoding parameters.

[0057] The encapsulation operation first constructs the basic container structure for transmitting data packets, employing an encapsulation format that supports multi-field nesting and binary data storage, such as JSON encapsulation format embedding base64 data segments, ProtocolBuffer, MessagePack, or a custom TLV format. The system uses a field merging strategy to merge key-value fields in the standardized data structure with standardized multimedia content according to defined binding relationships, ensuring the integrity of the structural semantics. Standardized multimedia content is embedded in the encapsulation structure in the form of file references, binary embeddings, or streaming data pointers, retaining content indexes or unique identifiers such as UUIDs or MD5 hashes to achieve data consistency verification.

[0058] Meanwhile, to meet data integrity verification requirements, the system adds metadata fields to the encapsulated data packets, including encapsulation timestamp, content checksum, content identification information, and data packet sequence number, for subsequent verification and reconstruction at the transmission end. After encapsulation, the system outputs the encapsulated data packet object as the input source for subsequent compression processing and transmission operations.

[0059] In one implementation, the standardized data structure is stored in JSON format, containing fields such as "record_id", "patient_name", "diagnosis_text", and "image_id". The standardized multimedia content is an image file converted to JPEG format, named img12345.jpeg. The encapsulation operation writes the base64 encoded image file as a string into the "media_content" field of the JSON structure, and after forming a merged structure, it is serialized into a data packet object.

[0060] In another implementation, the system uses Protocol Buffer as the encapsulation format. When constructing the structure definition file, two message bodies are defined: "StandardData" and "MediaFile". The former includes text fields, numeric fields, and tag fields, while the latter contains file type, file length, binary content, and hash digest. During the encapsulation operation, the system packages both into a unified message body "TransferPayload" and outputs the serialized binary stream.

[0061] Alternatively, the TLV encapsulation format, namely the Tag-Length-Value structure, can be used. First, a type field of tag=0x01 is assigned to the standardized data structure, and a type field of tag=0x02 is assigned to the standardized multimedia content. The two are then serialized into value segments and length information is added before them. The two segments are then combined to form a complete encapsulated data stream, ensuring clear structure and efficient processing.

[0062] Example Description: In the healthcare business, patient follow-up record systems need to transmit structured health questionnaire information along with uploaded electrocardiogram images or diagnostic report images to a remote expert platform for analysis and confirmation. The system merges the completed questionnaire's structured fields and image resources into a data packet, ensuring that the remote system can parse the text content and corresponding images, and perform logical matching based on the location field.

[0063] In the fintech business, online credit review systems need to merge user-submitted application information forms with uploaded income certificates and identity images into a standardized data package before sending it to the risk control center. This packaging process organizes structured application data fields and multimedia supporting documents in a unified structure, improving the integrity of transmitted data and the automation of the review process, effectively avoiding document omissions or review failures due to format differences.

[0064] This embodiment integrates standardized data structures and standardized multimedia content into a single data packet through encapsulation. This achieves the joint transmission of structured information and unstructured resources without compromising the data structure hierarchy or the integrity of the media content. The encapsulation format supports multi-platform parsing capabilities, and the content verification identifiers and field mapping information included in the encapsulation facilitate efficient reconstruction and location of the target content at the receiving end. This improves the structural clarity, accuracy, and resilience of data transmission, providing a unified and structurally complete basic content carrier for subsequent compression, path selection, and network transmission.

[0065] S40, obtain the network latency and bandwidth parameters of the target platform, and select the transmission path according to the network latency and bandwidth parameters;

[0066] In this embodiment, acquiring the network latency and bandwidth parameters of the target platform is a core action in network status monitoring, used to establish a dynamic understanding of the target platform's communication channel performance before information transmission. Network latency refers to the round-trip time from sending a data packet to receiving a response, generally calculated using ICMP echo requests or based on the TCP three-way handshake time difference; bandwidth parameters represent the upper limit of the amount of data the target platform can receive per unit time, which can be estimated through UDP bandwidth testing, TCP window adjustment, or HTTP segment file transfer.

[0067] During the acquisition operation, the system can invoke the network diagnostic module to send several measurement data packets to the target platform, record their round-trip times, and calculate the minimum, average, and maximum values ​​over multiple measurement periods as latency evaluation metrics. Bandwidth evaluation can be performed by continuously sending data blocks of known size and monitoring the time it takes for the other end to acknowledge receipt, calculating the real-time data throughput as an estimate of the current link bandwidth.

[0068] After acquiring network latency and bandwidth parameters, the system selects a path based on these parameters. A transmission path is a set of multiple available communication links between the source system and the target platform, which may include direct links, CDN node forwarding paths, multi-hop VPNs, or heterogeneous network channels. The system quantifies and compares the network latency and bandwidth parameters corresponding to all currently available transmission paths and calculates weights according to a preset path evaluation strategy. This strategy can be based on a comprehensive scoring function that prioritizes minimum latency, maximum bandwidth, minimizes the bandwidth-latency product, or combines metrics such as link stability and packet loss rate.

[0069] Once the transmission path selection is completed, the system will select the path with the best score as the path to be used for subsequent transmission tasks, and record the network latency and bandwidth parameter values ​​used in this path selection decision for use in subsequent analysis or model training.

[0070] In one implementation, the source system uses the ping command to send five ICMP packets to the target platform via the operating system's underlying network interface, measures the round-trip time, removes extreme values, and takes the average as the network latency value. Next, the iperf tool is used to establish a TCP session between the source system and the target platform, sending a data stream for 20 seconds, recording the total amount received by the target platform, and calculating the bandwidth parameter value. Three available transmission paths are pre-registered in the path selection module. The system measures and evaluates each of the three paths sequentially, ultimately selecting the path with a latency of less than 50ms and a bandwidth of more than 10Mbps as the final transmission path.

[0071] In another implementation, in a bandwidth-constrained environment, the system introduces a minimum RTT threshold priority strategy. This involves selecting only the set of paths from all measured paths that meet the criteria of latency being less than a threshold and packet loss rate being zero, and then prioritizing the first path based on bandwidth. If the latency of all paths fails to meet the threshold, the system initiates a congestion avoidance mechanism, temporarily using paths with suboptimal latency but high stability, and continues to reassess the state in subsequent cycles.

[0072] AI inference can also be used for path selection. The currently acquired network latency and bandwidth parameters are input into the pre-trained model, which determines the optimal path index position and returns it, avoiding network burden caused by frequent field tests.

[0073] Example Description: In the healthcare field, a doctor platform needs to upload data packets containing standardized structural information and high-resolution medical images to a remote image analysis center. Before uploading, the system obtains the network latency and bandwidth parameters of the target image analysis center to avoid transmitting large amounts of data when network congestion or high-latency links exist, thereby ensuring the timeliness and accuracy of diagnostic tasks.

[0074] In the fintech business, remote approval systems need to receive structured application information and credential image files from clients, and transmission efficiency directly impacts business processes. By monitoring the current network latency and bandwidth of approval system nodes and selecting the optimal transmission path for user data packets, application response speed can be significantly improved, and the probability of content corruption or reception failure due to network jitter can be reduced, thus optimizing user experience and business stability.

[0075] This embodiment acquires the network latency and bandwidth parameters of the target platform and performs path selection based on these parameters, effectively improving the adaptability and intelligence of data transmission paths, making the subsequent transmission of compressed data packets more stable and efficient. Network latency monitoring allows the system to avoid data timeouts caused by long-duration links, while bandwidth parameter evaluation ensures the throughput capacity of the transmission process. When network conditions fluctuate significantly, the system can re-evaluate the path selection results in real time, maintaining overall transmission quality and effectively improving the reliability and response speed of cross-platform data distribution.

[0076] S50, the data packet to be transmitted is compressed to generate a compressed data packet, and the compressed data packet is sent to the target platform through the transmission path;

[0077] In this embodiment, compressing the data packets to be transmitted refers to encoding and compressing the structured text, multimedia content, and metadata in the data packets using a compression algorithm to reduce data size, thereby reducing network load and improving transmission efficiency. The compression process can include two types: lossy compression and lossless compression. Structured text and control fields are usually compressed using lossless compression to ensure data integrity, while standardized multimedia content such as images and audio can be encoded using lossy encoding formats with specific compression ratios.

[0078] The input to the compression process is the data packet to be transmitted, which encapsulates standardized data structures and standardized multimedia content, typically in the form of a uniform binary data stream or a multi-part MIME encapsulation format. The system first analyzes the components of the data packet to be transmitted and calls the appropriate compression module for different types of content. For standardized data structures, general compression algorithms such as DEFLATE, Zstandard, or LZMA can be used; for image content, encoding methods with higher compression ratios, such as WebP or JPEG2000, can be selected; and for audio content, AAC or Opus encoding can be used to reduce bandwidth consumption.

[0079] After compression, a compressed data packet is generated. The system adds necessary format identifiers, encoding type fields, and integrity verification information to the compressed data packet to ensure that the receiving end can correctly decode and restore the original content.

[0080] Subsequently, the system sends the compressed data packets to the target platform via the previously selected transmission path. The sending action is initiated by the network transmission module, which establishes a connection based on the selected path's transmission protocol (such as TCP, UDP, HTTP / 2, or QUIC) and executes necessary processes such as packet fragmentation, handshake, transmission confirmation, and retransmission requests. Congestion control and traffic shaping strategies can also be integrated into the sending operation to dynamically adjust the sending rate when transmission bandwidth is limited.

[0081] In one implementation, the data packet to be transmitted is encapsulated in a standardized data structure in JSON format, and an H.264 compressed image frame is embedded within it. The compression module first calls the Zstandard algorithm to compress the JSON structure, generating a structured compressed segment, and then calls the H.264 encoder to compress the image to a specified frame rate and bitrate. The two compressed segments are merged to generate a compressed data packet containing a content type tag and a data length field. Subsequently, the system sends this compressed data packet in frames via the selected HTTP / 2 channel, adding a checksum field to each frame.

[0082] In another implementation, for the data packet to be transmitted, which consists of voice data and structured information, the system uses Opus to encode and compress the voice data, and LZMA to compress the structured information. Separators are added to the compressed data packet to identify multi-channel audio streams and the start of JSON fields. The transmission phase uses the QUIC protocol for fast transmission, featuring low latency and transmission recovery capabilities. During transmission, the system continuously monitors the receiver's acknowledgment response and dynamically adjusts the congestion window to match changes in the target platform's bandwidth.

[0083] Processing efficiency can be further improved by integrating multi-threaded compression and asynchronous transmission mechanisms. The system can divide the data packets to be transmitted into sub-blocks according to content type, compress each sub-block in parallel in a multi-threaded environment, and merge them into a compressed data packet after compression. The transmission request is then submitted to the network scheduling module asynchronously to minimize processing latency.

[0084] Example Description: In the healthcare field, mobile diagnostic terminals need to transmit structured medical record data along with high-resolution medical images to a central server. By compressing the data packets containing this information, the bandwidth occupied by image data can be significantly reduced, enabling rapid data submission under 4G network conditions and meeting the real-time requirements of remote consultations.

[0085] In the fintech business, mobile customer identity verification often involves the joint uploading of structured form data and captured images. By compressing the form information and image content in the data packet to be transmitted separately before sending, and then sending them to the backend risk control platform based on path selection, the smoothness and real-time response capability of the identity verification process can be ensured even under fluctuations in terminal network quality.

[0086] This embodiment generates compressed data packets by compressing the data packets to be transmitted and then sends them to the target platform via the transmission path. This significantly reduces data size and transmission latency while ensuring content integrity, thereby improving overall system transmission efficiency and bandwidth utilization. Especially in high-concurrency or unstable network scenarios, compression effectively alleviates bandwidth bottlenecks and enhances the system's adaptability to different network environments. Furthermore, the addition of format identifiers and verification fields after compression ensures that the target platform has clear data boundaries and a foundation for data consistency during decompression and parsing, reducing decoding error rates and the risk of data recovery failures.

[0087] S60, the target platform receives and parses the compressed data packet to present the original information content.

[0088] In this embodiment, receiving compressed data packets through the target platform refers to the network communication module activating a listening or polling mechanism on the target platform, establishing a connection with the source platform based on the selected transmission path, and receiving compressed data packets sent by the source platform after the connection is stable. The receiving process requires complete data transmission control logic, including connection handshake, flow control, packet reordering, and packet loss retransmission. The system performs integrity verification on the received data packets, verifying whether the checksum, content length, and packet header identifier are consistent, ensuring a reliable input basis for subsequent parsing.

[0089] Parsing compressed data packets refers to performing decompression and format recognition operations on the received data content, restoring the compressed encoding to the original structured data and multimedia content. The system automatically calls the corresponding decompression module based on the preset compression format identifier in the compressed data packet. Structured data segments can be restored to their original key-value structure using decompression algorithms such as Zstandard and DEFLATE; image content can be restored to renderable image frames using decoders such as H.264 and WebP; and audio content can be restored to playable audio streams using audio decoders such as Opus and AAC.

[0090] After decompression and parsing, the system performs format unification and cross-validation on the restored structured data and multimedia content. The structured data is used for content organization, field mapping, and display logic control, while the multimedia content is used for visualization on the front-end page or terminal interface. The system combines local presentation templates or rendering components to perform style rendering, content layout, and interactive markup processing on the original information content, ultimately presenting the original information content on the terminal interface in the form of text, images, audio, or structured information forms.

[0091] In one implementation, the target platform receives compressed data packets via a TCP connection. After receiving the packets, it calls the Zstandard decompression module to decompress the JSON structure and then calls the H.264 hardware decoder to restore the image frames. The decompressed JSON content enters the field mapping module, where field names are mapped locally and loaded into the rendering template; the decoded image frames are directly written to the display buffer. The entire process is controlled using an event-driven model.

[0092] In another implementation, the system receives compressed data packets transmitted in frame fragments via the QUIC protocol. The receive buffer reassembles and verifies each frame, then calls the LZMA and Opus decoders to reconstruct the structured data and speech content, respectively. After data parsing, the system integrates text and speech into a unified user interface based on a local style template, enhancing content accessibility and interactivity through clickable interactive buttons or a voice controller.

[0093] A layered concurrent parsing strategy can also be adopted, where data packets are divided into structure segments and media segments, with each segment being parsed by a different thread. The restored structure segments are used to generate data-bound views; the restored media segments are used by the multimedia engine to schedule playback or display tasks, thereby improving the efficiency of concurrent processing of large data packets and the terminal response speed.

[0094] Example Description: In the healthcare field, a remote consultation platform receives a compressed data packet containing medical records and medical images from a mobile terminal. The platform decompresses and parses this compressed data packet, extracts structured diagnostic data and high-resolution images, and displays patient information, lesion area markers, and diagnostic conclusions through an interface module, enabling doctors to perform real-time and complete medical data evaluations from remote terminals.

[0095] In the fintech business, the back-end risk control platform receives compressed data packets uploaded from the front end, which contain structured forms and user photos. After parsing the data packet, the system reconstructs the original user information form and ID card image. Through automatic form parsing and image comparison algorithms, it completes risk verification and user identity confirmation, improving data review efficiency and security while ensuring smooth interaction.

[0096] This embodiment describes a processing flow where the target platform receives and parses compressed data packets to present the original information content. This process can completely restore the compressed, mixed content to its original, usable form on the target platform. This flow not only ensures structural consistency and multimedia usability of data after cross-platform, high-compression transmission, but also improves the user's visual perception and interaction quality across different terminals. The system supports automatic identification and adaptive decoding of multiple compression formats, enhancing the versatility and scalability of the parsing stage, thereby improving the platform's compatibility and stability in handling diverse input scenarios.

[0097] This invention relates to the field of data processing technology and can be applied to business scenarios such as fintech and healthcare. It discloses a cross-platform information interaction method, apparatus, device, and medium, comprising: acquiring raw information sent by a source platform and parsing it into a standardized data structure according to a preset data format; extracting multimedia content elements from the structure and performing format conversion to generate standardized multimedia content; encapsulating the structure and content into a data packet to be transmitted; acquiring the network latency and bandwidth parameters of the target platform and selecting a transmission path based on these parameters; compressing the data packet to be transmitted and sending it to the target platform through the selected path; and the target platform receiving and parsing the compressed data packet to present the original information content. This invention achieves information format compatibility between different platforms through standardized data structures and unified encapsulation of multimedia content; improves data transmission efficiency and stability through network parameter awareness and path selection mechanisms; and ensures complete information restoration at the presentation end by combining compression processing and platform adaptation, thereby improving the reliability and consistency of cross-platform interaction without changing the original data content.

[0098] In one embodiment, step S10 includes:

[0099] S101, Receive the raw information data stream transmitted from the source platform;

[0100] S102, Identify the source platform feature identifier in the original information data stream;

[0101] S103, Select the parsing strategy corresponding to the source platform feature identifier according to the preset data format;

[0102] S104, extract the text content and metadata information from the original information using the parsing strategy;

[0103] S105, Reorganize the text content and the metadata information into a tree-like hierarchical structure according to the preset data format;

[0104] S106, Generate a standardized data structure containing the tree-like hierarchical structure.

[0105] In this embodiment, the process of receiving the raw information data stream transmitted from the source platform is based on a network transmission channel established in a heterogeneous communication environment. The system is configured with a continuous monitoring mechanism at the receiving end, supporting compatible parsing of multiple protocols (such as HTTP, MQTT, WebSocket, QUIC, etc.) and adapting to different communication formats and transmission behaviors adopted by the source platform. The system initiates the data stream reception process through an event-driven or polling mechanism, writes the raw information data stream into a buffer queue, and maintains timing consistency to avoid data loss or out-of-order delivery. During this process, data is transmitted to the receiving buffer in the form of byte streams or frame structures. The system needs to construct a streaming data boundary detection mechanism to identify the logical boundaries between transmission units and achieve the integrity splicing and reconstruction of data frames.

[0106] Identifying the source platform feature identifier in the raw information data stream refers to performing a rapid preprocessing operation on the received raw information data stream to extract the platform identifier from the data stream header or embedded fields. This feature identifier can be a predefined string, encoding pattern, field structure, or a unique identifier assigned to the platform. The system extracts this feature identifier as an anchor point for identifying the data source by parsing the data frame header or matching field mapping relationships. This identification mechanism supports dynamic registration and expansion, allows for the configuration of independent identifier matching rules for newly connected platforms, and possesses multi-platform heterogeneous identification capabilities.

[0107] Selecting a parsing strategy based on the preset data format and the corresponding source platform feature identifier refers to the system retrieving a set of parsing rules bound to the identified source platform feature identifier from a pre-configured strategy library. Parsing strategies may include field mapping rules, data block decomposition order, nested structure expansion methods, character encoding schemes, and time format conversion methods. The strategy library is stored in key-value mapping format and has dynamic loading capabilities. The system supports automated management of strategy switching and achieves automatic adaptation and transformation capabilities for different source platform data structures through configuration drivers.

[0108] The system extracts text content and metadata from the original information using a parsing strategy. Following the selected strategy, it performs field extraction and structure mapping operations, parsing the core information embedded in the original data stream into separate text content units and a set of structured metadata. The text content includes the main text displayed to the user, such as notification text, message prompts, and service information; the metadata includes control fields such as timestamps, source markers, priority tags, sensitivity levels, and category tags. This extraction process supports unpacking and deconstructing complex structures such as nested fields, compressed fields, and variable-length arrays, ensuring the completeness of information extraction.

[0109] Reorganizing text content and metadata into a tree-like hierarchical structure according to a preset data format refers to mapping the extracted text and metadata content to a logical tree structure with clearly defined hierarchy and parent-child relationships. This structure can be represented in JSON, XML, or a custom object tree format, where text content serves as leaf nodes attached to semantic classification branches, and metadata information is assigned to corresponding attribute nodes or control nodes. The reorganization process supports optimization operations such as node merging, structure unification, and redundant field compression to ensure a unified semantic data representation model, facilitating subsequent content parsing, rendering, and interactive processing.

[0110] Generating a standardized data structure with a tree-like hierarchical structure involves encapsulating the constructed hierarchical structure into independently transmittable and processable data objects, thus forming a standardized data structure. This structure exposes a unified access interface, possessing field uniformity, self-descriptive structure, and cross-platform adaptability, facilitating processing using a unified standard during subsequent encapsulation, compression, format conversion, and terminal rendering. The system supports serializing this standardized data structure into binary streams, structured text, or other intermediate formats to meet the transmission and compatibility requirements in multi-terminal environments.

[0111] This embodiment, through receiving raw information data streams, platform identification, strategy matching, information extraction, and hierarchical structure construction, can accurately extract standardized text and metadata content from multi-source heterogeneous data and generate a standardized data structure in a unified hierarchical format. This process supports input adaptation across multiple platforms, formats, and communication protocols, significantly enhancing the system's compatibility and parsing stability with heterogeneous data sources. The separation, parsing, and hierarchical reorganization mechanism of text and metadata not only improves the clarity and logical structure integrity of data expression but also provides a standardized and semantically clear input foundation for subsequent data encapsulation, format conversion, and content display. By transforming diverse information streams into a standard structure, consistency, accuracy, and scalability are ensured in cross-platform data processing workflows.

[0112] In one embodiment, step S20 above includes:

[0113] S201, Traverse the node hierarchy of the standardized data structure and identify the multimedia content elements contained in the node hierarchy;

[0114] S202, determine the specific media type of the multimedia content element;

[0115] S203, query the format conversion strategy corresponding to the specific media type;

[0116] S204, Perform multimedia format conversion operation on the multimedia content elements using the format conversion strategy to generate converted content;

[0117] S205, Perform integrity verification on the converted content and generate an integrity verification value;

[0118] S206, combine the converted content with the integrity verification value to generate standardized multimedia content.

[0119] In this embodiment, traversing the node hierarchy of the standardized data structure involves systematically scanning each nested level and node entry within the structured data representation to identify its attributes and numerical content. This traversal process is typically implemented recursively or iteratively. In JSON, XML, or tree-like structures, child nodes are read level by level from the root node, visiting all branch paths one by one using a depth-first or breadth-first strategy. Node information during traversal includes field names, field types, field values, nested structures, and additional attributes. The system maintains the location information of node paths to pinpoint the logical context to which the node belongs. At the implementation level, a unified field access interface can be built using reflection mechanisms or a dynamic field parsing engine to support handling uncertainties in arbitrary structural depth and field naming.

[0120] Identifying multimedia content elements within a node hierarchy involves pattern matching of each node's field types, content formats, and tag attributes during traversal to determine whether the node represents multimedia data, such as images, audio, video, PDFs, vector graphics, animations, or embedded documents. Identification criteria may include keywords in field names (e.g., "img", "audio", "file", "media"), MIME type prefixes in field values, base64 encoding patterns, URL suffixes, and media format magic bytes. The identification mechanism supports three modes: regular expressions, hash indexes, and rule set matching. It can process identification results in tiers based on identification confidence and supports robust identification across heterogeneous structures.

[0121] Determining the specific media type of a multimedia content element refers to performing fine-grained type judgment on the multimedia field or file body after initial identification, clarifying its specific media subclass, such as JPEG, PNG, MP4, AAC, WAV, PDF, SVG, etc. Type judgment can be based on the file header bytes (magic number), content encoding format, resource path extension, or annotation information in metadata. For example, if the field is a base64 string, the system will decode the prefix to check if its binary header matches the signature at the beginning of a PNG file; if the field is a URL address, its suffix can be parsed and the resource header accessed to confirm the media MIME type. To improve compatibility, the type judgment module should embed a multimedia format registry and signature feature library to achieve automatic judgment and redundancy checks of mainstream media types.

[0122] Querying the format conversion strategy corresponding to a specific media type refers to retrieving the appropriate conversion method from a pre-defined strategy mapping table after identifying the media type. Format conversion strategies include control parameters such as target format, compression parameters, frame rate control, sampling rate adjustment, size normalization, encoding standards, transparency preservation, and subframe extraction. The strategy table is typically indexed based on media type, supporting dynamic loading and user customization. The system can dynamically adjust the target format according to the current target platform requirements (such as terminal supported formats, bandwidth limitations, and rendering capabilities), such as converting high-resolution PNG images to WebP format to save bandwidth. The system supports a strategy fallback mechanism, automatically switching to an alternative solution when a target format cannot be converted.

[0123] Multimedia format conversion operations on multimedia content elements through format conversion strategies involve substantive content transcoding, re-encoding, or format replacement. For image-based media, this may include color space transformation, size scaling, resolution compression, alpha channel preservation, and format encoding (e.g., PNG to WebP). Audio media conversion may include bitrate adjustment, sampling rate change, and encoder change (e.g., WAV to AAC). Video content may involve frame rate control, bitrate limiting, and encoding format replacement (e.g., MP4 to H.265). The format conversion engine can integrate multimedia processing libraries such as FFmpeg, libvips, and SoX, encapsulating them into a unified interface, allowing asynchronous batch conversions via a task scheduler. During processing, metadata mapping between the source and target media must be maintained to ensure that the content semantics and structure remain unchanged.

[0124] Performing integrity verification on the converted content refers to verifying the accuracy of the output of the format conversion operation to ensure that no data corruption, structural damage, or content truncation occurred during the conversion process. Integrity verification mechanisms can be based on hash verification (such as MD5, SHA256), media metadata comparison (such as resolution, frame rate, length), decoding feasibility detection (attempting to open and read), or feature value comparison (such as color histograms, audio spectrograms). The verification module should have cross-platform consistency capabilities, meaning that the verification rules should yield consistent results across different operating systems and runtime environments. For conversions performed using external tools, the system should internally verify their usability again to avoid dependency chain anomalies that could prevent content from being rendered.

[0125] Combining the converted content with integrity verification values ​​to generate standardized multimedia content refers to constructing an encapsulation object that uniformly represents the multimedia content and its verification information, forming a multimedia entity unit with integrity, security, and resolvability. The encapsulation structure typically includes fields such as content body, content type, original source, target format, conversion parameters, integrity hash value, and additional verification information. This structure supports serialization into JSON objects, Protocol Buffers, or independent binary blocks and has self-describing capabilities. This standardized structure supports interface with subsequent encapsulation modules, achieving unified encapsulation and network transmission, avoiding metadata loss or content confusion during cross-module operations.

[0126] This embodiment, through structured parsing and precise type judgment, can efficiently identify multimedia content elements within a standardized data structure and perform compatible conversions across various formats based on a strategic approach. This ensures content usability while achieving compression optimization and terminal adaptation. An integrity verification mechanism promptly captures potential anomalies after conversion, guaranteeing data security. The final packaged standardized multimedia content possesses cross-platform interpretability and content verification capabilities, establishing a unified content expression standard for subsequent unified packaging and cross-platform transmission, thereby improving the stability, controllability, and scalability of the data interaction process.

[0127] In one embodiment, step S30 above includes:

[0128] S301, Create a package template that includes format identifiers;

[0129] S302, serialize the standardized data structure into a first data unit, and serialize the standardized multimedia content into a second data unit;

[0130] S303, insert the first data unit and the second data unit into the corresponding positions of the encapsulation template;

[0131] S304, obtain the current system clock to generate the encapsulation timestamp, and determine the current data volume of the encapsulation template as the data packet size;

[0132] S305, Generate a packet header data unit containing the encapsulation timestamp and the data packet size;

[0133] S306, combine the header data unit with the encapsulation template to generate a combined data body;

[0134] S307, determine the overall check value based on the combined data body, and combine the overall check value with the combined data body to generate a data packet to be transmitted.

[0135] In this embodiment, creating a packaging template containing format identifiers is the foundational framework for organizing data structures and content fields. This template defines the arrangement, field order, and encoding of standardized data structures and standardized multimedia content within data packets. Format identifiers indicate the protocol specifications, data representation methods, and version numbers used throughout the packaging structure, ensuring that different systems can correctly parse the data based on this format information during unpacking. Format identifiers are typically fixed-length fields or flag bits, such as using ASCII strings like "PKGFMT1" or binary bits to identify the number of content segments, encoding methods (e.g., UTF-8, Base64, Protobuf), compression status, and media packaging mode. Field slots in the packaging template are logically allocated to content segments such as structure data, media data, timestamps, data volume identifiers, and checksums. This template is organized in memory as a byte array or mapping structure, allowing for rapid insertion, replacement, and concatenation, improving construction efficiency and simplifying packaging logic.

[0136] Serializing a standardized data structure into its first data unit involves converting a standardized object with a clear logical hierarchy and field structure into a transmittable linear byte stream. Serialization methods can include JSON, XML, MessagePack, Protocol Buffers, etc. This process preserves the nesting relationships between fields, the structure of key-value pairs, and field type definitions to ensure the receiver can accurately deserialize and recover the original data structure. Similarly, serializing standardized multimedia content into its second data unit involves encoding and encapsulating the formatted and integrity-verified media data object to ensure it has self-describing capabilities and integrity at the binary level. Multimedia content serialization typically includes the media body byte stream, media type identifier, size information, frame rate or sampling rate parameters, checksum fields, etc. This serialization process supports block-based segmentation encoding, facilitating data segmentation during subsequent compression and transmission.

[0137] Inserting the first and second data units into their corresponding positions in the encapsulation template is the data filling operation. In the encapsulation template, slots for structured data segments and multimedia data segments are pre-defined, and the corresponding serialized data is inserted in a fixed order or at index positions. The insertion process must ensure field alignment and prevent data overlap; padding bytes or structure boundary markers can be added to the insertion boundaries if necessary. Simultaneously, to ensure the integrity of the inserted data, the system performs a quick length check and format verification on the inserted segment after insertion. This process also allows for data compression or encryption before insertion, enabling coordinated optimization of encapsulation and content preprocessing.

[0138] Obtaining the current system clock to generate the encapsulation timestamp involves calling a high-precision time acquisition module to record the absolute time of the current encapsulation action. This timestamp is used for packet lifecycle tracking, latency analysis, sequence control, and synchronization verification. The timestamp format can be UNIX timestamp, ISO 8601 time format, or a system-defined format, with precision typically in milliseconds or microseconds. Determining the current data volume of the encapsulation template as the packet size involves performing byte statistics on the encapsulation template after data insertion and calculating its total length as the transmission unit size parameter. This packet size parameter plays a crucial role in subsequent network optimization, segmentation, window control, and bandwidth estimation during transmission.

[0139] Generating a header data unit containing the encapsulation timestamp and data packet size is the construction of a control field structure containing time and length metadata. The header data unit identifies the basic parameters of the data body, ensuring that the receiving system can quickly read and process the corresponding data content according to the header instructions. The header structure can be a fixed-length structure or a variable-length set of metadata fields, including timestamp, data length, format checksum, number of content segments, and flag bits. The header data is usually placed at the beginning of the data body and generated in a fixed structured order and byte alignment during packet packaging. This header data unit serves as an index, format constraint, and content location within the entire data packet.

[0140] Combining the header data unit with the encapsulation template to generate the composite data body involves appending the header generated in the previous step as a prefix to the encapsulation template into which the data content has already been inserted, forming a complete content container. The composite data body is an intermediate encapsulation product with complete format identifiers, structural data, multimedia content, and metadata fields. Its internal field relationships are fixed, enabling direct compression, signing, or encryption before transmission. The combination process must ensure no offset or misalignment between structural fields and perform a format consistency check on the combination result.

[0141] Determining the overall checksum based on the combined data body involves performing an integrity hash calculation on the entire combined data body to generate a hash value used for verification and tamper prevention. The overall checksum can be calculated using hash functions such as SHA256, CRC32, and BLAKE3. This checksum not only verifies the consistency between the structured data and the media content but also covers the encapsulation structure, timestamp, and length information, thus achieving integrity constraints on the entire encapsulation unit. Combining the overall checksum with the combined data body to generate the data packet to be transmitted involves appending the checksum field to the end of the combined data body or encapsulating it in a separate checksum field, forming a final transmission data unit with transmission compliance, unpacking capabilities, and security control capabilities. This data packet has a clear structure, closed-loop verification, and aligned fields, and can support compression optimization, encrypted encapsulation, and multi-path distribution in subsequent network channels.

[0142] This embodiment serializes and formats the structured data and multimedia content respectively, forming a combined data body with structural consistency and parsing universality. It introduces encapsulation templates and format identifiers to achieve consistent data encapsulation across platforms, and enhances timing control and network adaptability by combining timestamps and data volume parameters. The generation of checksums ensures that the data content is not tampered with or damaged during encapsulation, compression and transmission, thereby significantly improving the parsability, verifiability and transmission robustness of multimodal data in cross-platform environments.

[0143] In one embodiment, step S40 above includes:

[0144] S401, send a network probe data packet to the target platform and receive a response data packet returned by the target platform;

[0145] S402, determine the network latency time difference based on the timestamp of sending the network probe data packet and the timestamp of receiving the response data packet;

[0146] S403, Measure the effective payload transmission rate of the response data packet as a bandwidth parameter;

[0147] S404, Obtain the preset set of available transmission paths;

[0148] S405, Analyze the performance indicators of each transmission path in the set of available transmission paths based on the network latency time difference and bandwidth parameters;

[0149] S406, Select the transmission path with the best performance index from the set of available transmission paths as the target transmission path.

[0150] In this embodiment, sending network probe packets to the target platform and receiving response packets from the target platform involves constructing a test data unit with timestamp and identifier fields, performing a round-trip communication under an application layer or transport layer protocol, to dynamically perceive the network status of the target platform. This probe packet can be constructed using UDP, ICMP, or a custom TCP handshake message. The data structure embeds a precise timestamp generated by the local system clock and a unique session identifier, enabling a one-to-one backtracking confirmation upon receiving the response packet. Upon receiving the probe packet, the target platform immediately returns along the same path or constructs a response, ensuring the response path matches the probe path, thus accurately reflecting the physical and protocol latency of the current network link.

[0151] The network latency difference is determined by comparing the timestamps of the sent probe packets and the received response packets using a high-precision system clock to calculate the round-trip time (RTT). This calculation does not rely on the target platform's clock synchronization; it only uses the time difference between local packet sending and receiving as the network latency estimate. Timestamp accuracy is typically in the millisecond or microsecond range, supports floating-point time encoding, and stability can be optimized through time synchronization mechanisms such as NTP or kernel clock calibration modules. If multiple probes are used, the system can use a weighted average or sliding window algorithm to smooth the latency value to reduce short-term jitter interference.

[0152] Measuring the effective payload transmission rate of response data packets as a bandwidth parameter involves recording packet size and transmission time during data reception to estimate the actual available bandwidth. This process requires specifying the payload size in bytes, excluding TCP / IP header overhead and non-business fields, and retaining only the actual transmitted data body. The bandwidth calculation formula is: Bandwidth = Number of effective data bytes / Transmission time, with the result typically in bps, Kbps, or Mbps. To improve the accuracy of bandwidth estimation, the system can embed padding fields of a specific size into probe packets to simulate the packet length of actual business data. Furthermore, it supports summarizing calculations from multiple responses to construct bandwidth trend charts for subsequent path strategy prediction.

[0153] Obtaining a preset set of available transmission paths involves retrieving currently selectable link paths from the network management module, transmission policy table, or historical connection records. Transmission paths can be abstracted based on multi-layered topology information, including direct links, proxy channels, multi-segment relays, or CDN node links. Each path structure contains metadata such as start and end nodes, hop count, stability rating, historical congestion rate, and security policy configuration. The path set data structure is generally a graph structure or an indexed array, supporting quick location or filtering of path subsets under specific conditions based on path labels. Path sources can be static configuration, dynamic discovery, or obtained through system adaptive learning, possessing a certain degree of scalability and scheduling flexibility.

[0154] The performance metrics analysis based on network latency difference and bandwidth parameters for each transmission path in the available transmission path set combines previously measured dynamic parameters with the performance baseline of each path in the path set, and constructs a path performance scoring model to comprehensively evaluate each path. This model can employ a weighted scoring method, where latency difference measures the link's real-time response capability, and bandwidth measures the amount of data that can be supported per unit time. The combination reflects the available throughput of the end-to-end link. The scoring logic of the performance metrics can dynamically adjust the weights, for example, increasing the latency proportion in high real-time tasks and increasing the bandwidth proportion in large data transmission tasks. The system can also incorporate additional metrics such as path congestion, packet loss rate, and historical retransmission counts to expand the scoring dimensions.

[0155] The target transmission path is selected from the available transmission path set based on its optimal performance. This is done by choosing the path with the highest score calculated in the scoring model as the preferred path and setting it as the transmission channel for subsequent data packets. If multiple paths have similar scores or fall within dynamic fluctuation boundaries, the system can use polling, multi-path switching, path merging, or a primary / backup mechanism for dynamic scheduling. The target path setting result is synchronized to the data sending module, encryption and compression module, and data transmission controller to ensure the consistency, timing continuity, and network condition adaptability of data packet transmission.

[0156] This embodiment actively sends probe data to the target platform and combines it with timestamp backtracking to obtain network latency and bandwidth status in real time without additional overhead. Then, it dynamically selects the optimal transmission path based on the currently measured performance parameters, thereby avoiding the performance bottleneck caused by fixed channels. Combined with the dual evaluation mechanism of latency and bandwidth parameters, the decision logic of the transmission path takes into account both latency control and data throughput capability. It is particularly suitable for multimedia and interactive data scenarios that require both low latency and high bandwidth, effectively improving the stability, response efficiency and adaptability of cross-platform data transmission.

[0157] In one embodiment, step S50 above includes:

[0158] S501, parse the content structure features of the data packet to be transmitted;

[0159] S502, set high-priority content identifiers and ordinary-priority content identifiers based on the content structure features;

[0160] S503, Select a compression module that matches the content structure features;

[0161] S504, the compression module performs lossless compression on the data block marked with high priority content identifier and lossy compression on the data block marked with ordinary priority content identifier, generating lossless compressed data block and lossy compressed data block respectively;

[0162] S505, the lossless compressed data block and the lossy compressed data block are recombined to generate a compressed data packet;

[0163] S506, Establish a transmission session connection with the target platform through the transmission path;

[0164] S507, the compressed data packet is divided into sequentially arranged transmission data units;

[0165] S508, the transmission data units are sent sequentially through the transmission session connection;

[0166] S509, Receive the transmission confirmation response returned by the target platform.

[0167] In this embodiment, parsing the content structure features of the data packet to be transmitted involves performing a deep structural analysis of the encapsulated data content within the data packet before data compression. This process typically includes identifying data type tags, parsing hierarchical structures, extracting field attributes, and recognizing field semantics. Based on the serialization descriptions or specific tagging rules of the fields in the encapsulation template, the system establishes a structure mapping table for the original content according to logical hierarchy and content density, and further forms a set of data groups for classification processing. The construction of structural features may include, but is not limited to, information such as data block length, access frequency, update frequency, and field function categories (e.g., main business fields, media resource fields, redundancy check fields).

[0168] Setting high-priority and ordinary-priority content identifiers based on content structure characteristics involves assigning priority tags to each data block after structure parsing, according to preset priority evaluation rules. High-priority content typically corresponds to content units with high information sensitivity, strong user awareness, or high parsing dependency, such as text descriptions, metadata fields, index headers, or keyframes. Ordinary-priority content typically corresponds to redundant content, auxiliary content, or resource units for which quality degradation is acceptable, such as background images, compressed audio segments, or auxiliary annotation information. The priority setting mechanism supports both static rule-driven and dynamic evaluation methods based on historical transmission performance or the target platform's decoding capabilities. Identification methods can be embedded in the compression module using inline tags, external mapping tables, or compression control fields.

[0169] The selection of a compression module that matches the content structure features involves choosing the most suitable compression algorithm component and configuring it based on the structure analysis results and priority allocation from the previous stage. The compression module includes implementations of several compression encoding methods, including but not limited to Huffman coding, LZ77, LZMA, JPEG compression, AAC encoding, WebP image encoding, and H.265 inter-frame compression. Each type of compression module has different input interfaces and suitable scenarios. Based on the data type recorded in the content structure, target platform compatibility, and compression efficiency requirements, the system selects the combination with the highest suitability from the module library. Multi-module joint compression scheduling can be performed at the field, block, or logical region granularity.

[0170] The core operation of the compression phase involves performing lossless compression on data blocks marked with high-priority content identifiers and lossy compression on data blocks marked with ordinary-priority content identifiers. Lossless compression ensures byte-level consistency between the decompressed and uncompressed data, suitable for structured text, control information, metadata, or fields with high precision requirements; common algorithms include Deflate, Zstd, or Brotli. Lossy compression, on the other hand, allows for tolerable loss of precision in the compressed data, suitable for media content blocks such as images, videos, and audio; common algorithms include JPEG, MP3, and H.264. During execution, the system configures corresponding parameters for each compression method, such as compression ratio, precision level to be preserved, and inter-frame reference interval, to balance compression efficiency and recovery quality. The compression result generates two types of data blocks, and the compression method and corresponding decompression parameters are recorded in the metadata.

[0171] Reassembling lossless and lossy compressed data blocks into a compressed data packet involves reorganizing the output order and structure of the data blocks after compression to form a unified encapsulation. This operation may involve processing actions such as compressed block splicing, offset relocation, compression method marker embedding, and overall checksum updating. The reassembly order can retain the original structure or be optimized according to the decoding efficiency of the target platform to improve decoding concurrency after transmission. The internal structure of the reassembled compressed data packet must conform to the system-defined encapsulation protocol format so that the decompression module can correctly identify and restore each data block.

[0172] Establishing a transmission session connection with the target platform via the transmission path involves initiating a connection handshake process to the selected network channel after compressed data preparation, thus constructing a reusable transmission session environment. This session connection is established based on TCP, QUIC, HTTP / 3, or a dedicated transmission protocol. Connection parameters such as MTU, sliding window size, flow control mechanisms, and encryption methods are configured, and state synchronization and connection cache management are performed. The session connection supports multiplexing, connection migration, and retransmission mechanisms to ensure data continuity and security under network fluctuations.

[0173] Dividing compressed data packets into sequentially arranged transmission data units involves splitting large packets into several sequentially numbered sub-data units according to network link characteristics and protocol MTU limitations, generating a set of data segments that meet the load capacity of the transmission channel. Each data unit includes a segment number, total length, compressed block offset information, checksum field, etc., and supports packet loss recovery, out-of-order reordering, and error identification mechanisms.

[0174] Sending data units sequentially via a transmission session connection involves pushing these data units to the receiving end of the target platform in an orderly or concurrent manner through the session connection. During transmission, the system continuously monitors the link status and performs rate adjustment, congestion control, and error retransmission operations to ensure that data arrives in order and is transmitted completely. The transmission mechanism can integrate flow control algorithms such as Reno, Cubic, and BBR to optimize bandwidth utilization.

[0175] The transmission acknowledgment response returned by the target platform is sent after each batch of data units is successfully delivered. The target platform returns an ACK response or a success status receipt according to the protocol to confirm that the data has been completely received and can proceed to the decompression process. The response mechanism supports window acknowledgment, cumulative acknowledgment, or block acknowledgment modes, improving transmission stability and security fault tolerance.

[0176] This embodiment, through in-depth analysis and priority identification of the data packet structure to be transmitted, enables the system to select the most appropriate compression method based on the importance of the data content during the compression processing stage, maximizing both compression efficiency and data fidelity. While maintaining lossless restoration of critical information, high-ratio lossy compression is performed on data blocks with tolerable errors, significantly reducing the overall size of the data packet. Combined with optimal path selection and data unit segmentation mechanisms, the system achieves fast, efficient, and secure data transmission under various network conditions, making it particularly suitable for cross-platform, large-scale, multimedia data transmission tasks, improving network resource utilization efficiency and user content reception experience.

[0177] In one embodiment, step S60 above includes:

[0178] S601, Obtain compressed data packets through the data receiving interface of the target platform;

[0179] S602, verify whether the overall checksum in the compressed data packet is valid;

[0180] S603, decompress the verified compressed data packet and restore it to a combined data body;

[0181] S604, Separate the header data unit and the encapsulation template from the combined data body;

[0182] S605, extract the first data unit and the second data unit from the encapsulation template;

[0183] S606, Perform deserialization processing on the first data unit to reconstruct a standardized data structure;

[0184] S607, Perform deserialization processing on the second data unit to reconstruct standardized multimedia content;

[0185] S608, Adapt the standardized data structure according to the rendering capabilities of the target platform to obtain the adapted data structure;

[0186] S609, adapt the standardized multimedia content according to the playback capabilities of the target platform to obtain the adapted multimedia content;

[0187] S610, combine the adapted data structure and the adapted multimedia content to present the original information content.

[0188] In this embodiment, obtaining compressed data packets through the target platform's data receiving interface refers to the network communication component in the target platform listening to a designated transmission session port and receiving the compressed data stream transmitted from the source platform. The receiving interface must support the segmentation and reassembly of transmitted data units, including receive buffer management, order correction, fragment splicing, and status marking. This interface typically supports multi-protocol access (such as HTTP / 3, QUIC, WebSocket, etc.) and has fault-tolerant mechanisms for connection interruptions, retransmissions, or data unit rearrangements to ensure the continuity and integrity of the receiving process.

[0189] Verifying the validity of the overall checksum in the compressed data packet involves comparing the checksum field within the compressed packet with the locally recalculated checksum to confirm whether the data packet has been tampered with or lost critical content during network transmission. The overall checksum can be generated using hash functions (such as SHA-256), CRC checksums, MAC verification tags, etc., providing high collision resistance. This step is crucial for ensuring data trustworthiness and security. If verification fails, subsequent decompression and content reconstruction processes are halted, and an error feedback mechanism or retransmission request is triggered.

[0190] The verified compressed data packets are decompressed to restore the combined data body. This involves reverse decoding using the compression format corresponding to the source platform to reconstruct the original multi-layer encapsulation structure. This process requires selecting an appropriate decompression module based on the compression identifier and processing according to the compression algorithm type (lossless or lossy). The combined data body restored by the decompression module should retain the original packet header information and serialization format to ensure the usability and accuracy of subsequent data parsing operations.

[0191] Separating the header data unit and the encapsulation template from the combined data body is the action of separating the structured metadata from the business data in the compressed data body. The header data unit typically contains system-level information such as timestamps, packet length, checksum method, and transmission identifier; the encapsulation template contains standardized data structures and the serialization mapping structure of multimedia content. The separation operation requires the accurate location of the boundary between the two using structure pointers or offset indices.

[0192] Extracting the first and second data units from the encapsulation template involves distinguishing and extracting the encapsulated text information portion (first data unit) and the media content portion (second data unit) based on the data field mapping relationships, content sequence markers, and structural partition pointers recorded in the encapsulation template. This operation requires the encapsulation template to define clear field distribution rules during design and support an extensible data unit identification mechanism.

[0193] Deserialization of the first data unit reconstructs a standardized data structure by reloading the compressed and transmitted standardized text content into a platform-recognizable data structure in a tree hierarchy or key-value pair format. The deserialization process requires format recognition, data type conversion, and hierarchical binding operations on the structure fields to restore the logical relationships and attribute definitions between the original fields.

[0194] Deserialization of the second data unit reconstructs standardized multimedia content based on the media format specifications supported by the target platform, restoring media data blocks into playable or renderable object forms. This process includes format decoding, data reconstruction, integrity checksum comparison, and cache preloading. For image content, its resolution and color space must be restored; for audio or video, bitrate, duration, and inter-frame structure must be restored.

[0195] Adapting standardized data structures to the target platform's rendering capabilities involves analyzing environmental conditions such as the target terminal's display resolution, processing power, and UI layout specifications. This process then performs operations on the standardized data structure, including content reorganization, field reduction, font size adjustment, and node collapsing / expanding, to achieve the best front-end display effect. This process may call the local rendering engine's capability adaptation interface, supporting the automatic generation of responsive data structures for different terminal types (such as mobile devices, web pages, and embedded terminals).

[0196] Adapting standardized multimedia content to the target platform's playback capabilities involves determining the platform's supported media formats, buffer size, decoding rate, and other limitations. This is followed by processing the media content through transcoding, downsampling, frame rate compression, or multi-bitrate version selection to achieve fast and smooth playback. This process can also reference the target platform's historical playback performance and network condition prediction models to intelligently select the optimal adaptation version.

[0197] The combination of the adapted data structure and the adapted multimedia content is achieved by defining rendering layout logic or interface template rules, integrating the reconstructed text content and media resources into a unified display entity. This combination is typically accomplished through the Document Object Model (DOM), component binding mechanisms, or front-end rendering frameworks, ensuring the coherence of information expression and the consistency of user interaction.

[0198] The final presentation of the original information content involves the target platform displaying the restored text and multimedia information to the user through a visual interface, creating a content experience consistent with the source platform. This presentation includes not only the reproduction of static content but may also include the reconstruction of dynamic interactive behaviors, such as text-image linkage, video playback control, and content switching functions.

[0199] Example description: In the healthcare field, it can be applied to the structured transmission and cross-platform display of medical information across different platforms, ensuring the integrity, accuracy, and transmission efficiency of diagnostic and treatment content.

[0200] First, the system receives raw information data streams transmitted from a source platform (such as a hospital's electronic medical record system), which can include text and multimedia content such as patient complaints, examination records, prescription data, image links, and diagnostic suggestions. The system identifies the source platform's characteristic identifiers in the data stream and, based on a preset data format, selects a parsing strategy corresponding to that platform to extract the text content and metadata information (such as timestamps, patient identifiers, and medical service types). This content is then reconstructed into a standardized data structure with a hierarchical structure according to logical relationships, facilitating subsequent unified processing and transmission.

[0201] Next, the system traverses the hierarchical nodes of the standardized data structure, identifying and extracting multimedia content elements such as MRI images, electrocardiogram images, or doctor's voice annotations. For different types of media content, the system queries matching format conversion strategies, such as converting DICOM format medical images to standard image formats (JPEG 2000 or PNG) and uniformly converting audio recordings to AAC encoding, and performs the corresponding format conversion operations. Subsequently, the converted content undergoes integrity verification, generating an integrity check value to ensure it has not been damaged or tampered with.

[0202] Subsequently, the system creates a packaging template with format identifiers, serializes the standardized data structure into a first data unit, and serializes the standardized multimedia content into a second data unit, inserting them into their corresponding positions within the packaging template. The system then uses the current system clock to generate a packaging timestamp and calculates the size of the packaged data to form the data packet size, thereby generating a header data unit containing the timestamp and data size. The system combines the header data unit with the packaging template to form a combined data body, calculates the overall checksum based on this combined body, and embeds it, ultimately constructing a complete data packet to be transmitted.

[0203] Before network transmission, the system proactively sends probe data packets to the target platform (such as a remote diagnosis and treatment platform), calculates network latency based on round-trip time, and measures the current network bandwidth by combining the payload of the response data packets. The system then performs performance analysis and comparison of these parameters with multiple preset available transmission paths, selecting the path with the lowest latency and optimal bandwidth as the transmission path to improve the stability and timeliness of subsequent data transmission.

[0204] During the data transmission phase, the system parses the content structure characteristics of the data packets to be transmitted, assigning priorities to structured data and unstructured media content respectively, and classifying them into high-priority and ordinary-priority data blocks based on content density and usage frequency. Lossless compression (e.g., using LZMA) is performed on high-priority data to preserve data integrity, while lossy compression (e.g., JPEG compression of images, low-bitrate encoded audio) is performed on ordinary-priority data. The compressed data packets are then reassembled to generate compressed data packets. A transmission session connection is established through the target transmission path, and the compressed data packets are divided into multiple sequential transmission units, sent in order, and a transmission acknowledgment response is received to ensure reliability.

[0205] After receiving the compressed data packet, the target platform obtains all content through its data receiving interface and verifies the overall checksum to ensure the data is not corrupted. Next, it decompresses the data packet, reconstructing it into a combined data body and separating the packet header and encapsulation template. It then extracts the serialized standardized data structure and standardized multimedia content. The system deserializes both into structured representations and visualized media information, and performs dimensionality reduction or graphical transformation on the structured data based on the target platform's rendering capabilities (e.g., the rendering limit of mobile devices). Simultaneously, it adapts the multimedia content based on playback capabilities (e.g., video frame rate, audio encoding compatibility).

[0206] Finally, the system combines the matched structured data and multimedia content to present complete medical information to end users. This process is suitable for doctors to remotely view patients' complete medical records and examination results, and also for patients to independently view their diagnosis and treatment information through smart devices. It improves the efficiency and consistency of medical data interaction across institutions and devices, and supports remote consultations, intelligent decision support, and the collection of data throughout the entire disease process.

[0207] In fintech platforms, institutions frequently need to send various SMS notifications to customer terminals, including repayment reminders, transaction confirmations, risk warnings, account changes, and other types of messages. This raw information may be generated by different business systems in JSON, XML, or text formats, and may contain embedded fields such as business number, customer identifier, sending channel, priority, and content body. The system first receives the raw SMS data stream pushed by the source platform and identifies platform characteristic identifiers in the data stream, such as business module tags or data protocol headers. Then, based on the identification results, it selects the corresponding parsing strategy to extract metadata information such as SMS text content, business type, and sending time from the raw data, and reorganizes it into a standardized data structure with hierarchical relationships according to a preset structure to adapt to the input requirements of downstream processing modules.

[0208] After standardizing the data structure, the system traverses each node within the structure, identifying the multimedia content elements it contains, such as QR code images, authentication icons, or multilingual audio clips embedded in the SMS text. The system further determines the media type and queries the corresponding format conversion strategy. Through the conversion module, it standardizes parameters such as encoding format and resolution; for example, it converts PNG images to JPEG compression or audio clips to a unified AAC format. Each converted piece of content is accompanied by an integrity check value to ensure consistency during transmission, ultimately generating structured multimedia content.

[0209] Next, the system encapsulates standardized data structures and standardized multimedia content into a data packet to be transmitted. This process involves creating an encapsulation template and embedding format identifiers, serializing the text and media content into independent data units, and inserting them into predefined positions within the template. Simultaneously, an encapsulation timestamp and data packet size information are generated, and a packet header data unit is constructed. These are then combined to form a complete data body. The system performs a hash calculation on this data body to generate an overall checksum, which is then combined into the data packet to be transmitted.

[0210] Before transmission, the system sends network probe packets to the target financial terminal (such as a customer SMS platform or mobile application), receives responses, calculates network latency and bandwidth parameters, assesses the current network status, and selects the transmission path with the minimum latency and optimal bandwidth from multiple candidate paths to ensure transmission efficiency and stability. Subsequently, based on the characteristics of the data packet content, it distinguishes between high-priority (such as verification codes and risk control warning SMS messages) and ordinary-priority (such as marketing SMS messages) content, and executes differentiated compression strategies. High-priority content uses lossless compression to preserve complete semantics, while ordinary-priority content uses lightweight lossy compression to improve transmission efficiency. After reassembling the compressed data blocks, the system establishes a session connection with the target platform, sends the compressed data packets in fragments, and receives and confirms successful transmission.

[0211] After the target financial terminal receives the compressed data packet, the system first verifies the packet's integrity to ensure the data has not been tampered with or lost. It then decompresses and restores the combined data, separating the text content and multimedia components, and performs deserialization operations to reconstruct the original structure. Finally, it adapts the content to the terminal device's rendering and playback capabilities; for example, it adjusts audio segments to locally supported playback formats or adapts structural fields for rendering. Ultimately, it combines and presents the SMS content and its accompanying text, image, or audio elements, ensuring the client receives a complete, accurate, and usable information display.

[0212] This embodiment performs structured decompression, data verification, and content decoding on the target platform, and adapts the content by combining rendering and playback capabilities. This allows different types of platforms to efficiently and accurately restore and display the original information content even in resource-constrained or heterogeneous terminal environments. Through modular decompression, deserialization, format conversion, and capability adaptation processes, end-to-end recoverable transmission of information from the source to the target is achieved, avoiding content distortion, information loss, or display abnormalities caused by platform differences. This solution has extremely high portability and adaptability, and can be widely applied to scenarios such as multi-terminal information synchronization, intelligent cross-platform content distribution, and remote multimedia collaborative presentation, improving the stability, adaptability, and user experience consistency of the content transmission system.

[0213] In one embodiment, a cross-platform information interaction device is provided, which corresponds one-to-one with the cross-platform information interaction method described in the above embodiments. (Refer to...) Figure 3 , Figure 3 This is a schematic diagram of the functional modules of a preferred embodiment of the cross-platform information interaction device of the present invention. The modules include a source information parsing module 10, a multimedia format conversion module 20, a data encapsulation module 30, a transmission path selection module 40, a data compression and transmission module 50, and a target platform parsing and presentation module 60. Detailed descriptions of each functional module are as follows:

[0214] The source information parsing module 10 is used to obtain the original information sent by the source platform and parse the original information into a standardized data structure according to a preset data format.

[0215] The multimedia format conversion module 20 is used to extract multimedia content elements from the standardized data structure and perform format conversion on the multimedia content elements to generate standardized multimedia content.

[0216] Data encapsulation module 30 is used to encapsulate the standardized data structure and the standardized multimedia content into a data packet to be transmitted;

[0217] The transmission path selection module 40 is used to obtain the network latency and bandwidth parameters of the target platform, and select the transmission path according to the network latency and bandwidth parameters;

[0218] The data compression and transmission module 50 is used to compress the data packet to be transmitted to generate a compressed data packet, and send the compressed data packet to the target platform through the transmission path;

[0219] The target platform parsing and presentation module 60 is used to receive and parse the compressed data packet through the target platform and present the original information content.

[0220] In one embodiment, the source information parsing module 10 is specifically used for:

[0221] Receive the raw information data stream transmitted from the source platform;

[0222] Identify the source platform feature identifier in the original information data stream;

[0223] Select the parsing strategy corresponding to the source platform feature identifier according to the preset data format;

[0224] The text content and metadata information in the original information are extracted using the parsing strategy.

[0225] The text content and metadata information are reorganized into a tree-like hierarchical structure according to the preset data format;

[0226] Generate a standardized data structure that includes the tree-like hierarchical structure.

[0227] In one embodiment, the multimedia format conversion module 20 is specifically used for:

[0228] Traverse the node hierarchy of the standardized data structure and identify the multimedia content elements contained in the node hierarchy;

[0229] Determine the specific media type of the multimedia content element;

[0230] Query the format conversion strategy corresponding to the specific media type;

[0231] The multimedia content elements are subjected to multimedia format conversion operations using the aforementioned format conversion strategy to generate converted content.

[0232] Perform integrity verification on the converted content and generate an integrity verification value;

[0233] The converted content is combined with the integrity verification value to generate standardized multimedia content.

[0234] In one embodiment, the data encapsulation module 30 is specifically used for:

[0235] Create a wrapper template that includes format identifiers;

[0236] The standardized data structure is serialized into a first data unit, and the standardized multimedia content is serialized into a second data unit;

[0237] Insert the first data unit and the second data unit into the corresponding positions of the encapsulation template;

[0238] Obtain the current system clock to generate an encapsulation timestamp, and determine the current data volume of the encapsulation template as the data packet size;

[0239] Generate a header data unit that includes the encapsulation timestamp and the data packet size;

[0240] The header data unit is combined with the encapsulation template to generate a combined data body;

[0241] The overall check value is determined based on the combined data body, and the overall check value is combined with the combined data body to generate a data packet to be transmitted.

[0242] In one embodiment, the transmission path selection module 40 is specifically used for:

[0243] Send network probe data packets to the target platform and receive response data packets returned by the target platform;

[0244] The network latency time difference is determined based on the timestamp of sending the network probe data packet and the timestamp of receiving the response data packet;

[0245] The effective payload transmission rate of the response data packet is measured as a bandwidth parameter.

[0246] Obtain the preset set of available transmission paths;

[0247] Based on the network latency difference and bandwidth parameters, analyze the performance indicators of each transmission path in the set of available transmission paths;

[0248] Select the transmission path with the best performance index from the set of available transmission paths as the target transmission path.

[0249] In one embodiment, the data compression and transmission module 50 is specifically used for:

[0250] Analyze the content structure features of the data packet to be transmitted;

[0251] Based on the aforementioned content structure characteristics, high-priority content identifiers and ordinary-priority content identifiers are set;

[0252] Select a compression module that matches the content structure features;

[0253] The compression module performs lossless compression on data blocks marked with high-priority content identifiers and lossy compression on data blocks marked with ordinary-priority content identifiers, generating lossless compressed data blocks and lossy compressed data blocks respectively.

[0254] The lossless compressed data block and the lossy compressed data block are recombined to generate a compressed data packet;

[0255] A transmission session connection is established with the target platform through the transmission path;

[0256] The compressed data packet is divided into sequentially arranged transmission data units;

[0257] The transmission data units are sent sequentially through the transmission session connection;

[0258] Receive the transmission confirmation response returned by the target platform.

[0259] In one embodiment, the target platform parsing and rendering module 60 is specifically used for:

[0260] The compressed data packet is obtained through the data receiving interface of the target platform;

[0261] Verify whether the overall checksum in the compressed data packet is valid;

[0262] Perform decompression processing on the verified compressed data packets to restore them to a combined data body;

[0263] Separate the header data unit and the encapsulation template from the combined data body;

[0264] Extract the first data unit and the second data unit from the encapsulation template;

[0265] Perform deserialization on the first data unit to reconstruct a standardized data structure;

[0266] Perform deserialization on the second data unit to reconstruct standardized multimedia content;

[0267] The standardized data structure is adapted according to the rendering capabilities of the target platform to obtain the adapted data structure;

[0268] The standardized multimedia content is adapted according to the playback capabilities of the target platform to obtain adapted multimedia content.

[0269] The adapted data structure and the adapted multimedia content are combined to present the original information content.

[0270] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides determination and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used for communication with external user terminals via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a cross-platform information interaction method on the server side.

[0271] In one embodiment, a computer device is provided, which may be a user terminal, and its internal structure diagram may be as follows: Figure 5 As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides determination and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a cross-platform information interaction method on the user side.

[0272] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0273] Obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to a preset data format;

[0274] Multimedia content elements are extracted from the standardized data structure, and format conversion is performed on the multimedia content elements to generate standardized multimedia content.

[0275] The standardized data structure and the standardized multimedia content are encapsulated into a data packet to be transmitted.

[0276] Obtain the network latency and bandwidth parameters of the target platform, and select a transmission path based on the network latency and bandwidth parameters;

[0277] The data packet to be transmitted is compressed to generate a compressed data packet, and the compressed data packet is sent to the target platform through the transmission path;

[0278] The target platform receives and parses the compressed data packet to present the original information content.

[0279] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0280] Obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to a preset data format;

[0281] Multimedia content elements are extracted from the standardized data structure, and format conversion is performed on the multimedia content elements to generate standardized multimedia content.

[0282] The standardized data structure and the standardized multimedia content are encapsulated into a data packet to be transmitted.

[0283] Obtain the network latency and bandwidth parameters of the target platform, and select a transmission path based on the network latency and bandwidth parameters;

[0284] The data packet to be transmitted is compressed to generate a compressed data packet, and the compressed data packet is sent to the target platform through the transmission path;

[0285] The target platform receives and parses the compressed data packet to present the original information content.

[0286] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side and user side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0287] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0288] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0289] It should be noted that if any software tools or components not belonging to this company appear in the embodiments of this application, they are merely illustrative examples and do not represent actual use. The embodiments described above are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.< / video> < / audio>

Claims

1. A cross-platform information interaction method, characterized in that, Includes the following steps: Obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to a preset data format; Multimedia content elements are extracted from the standardized data structure, and format conversion is performed on the multimedia content elements to generate standardized multimedia content. The standardized data structure and the standardized multimedia content are encapsulated into a data packet to be transmitted. Obtain the network latency and bandwidth parameters of the target platform, and select a transmission path based on the network latency and bandwidth parameters; The data packet to be transmitted is compressed to generate a compressed data packet, and the compressed data packet is sent to the target platform through the transmission path; The target platform receives and parses the compressed data packet to present the original information content.

2. The cross-platform information interaction method as described in claim 1, characterized in that, Receive text content, obtain the original information sent by the source platform, and parse the original information into a standardized data structure according to a preset data format, including: Receive the raw information data stream transmitted from the source platform; Identify the source platform feature identifier in the original information data stream; Select the parsing strategy corresponding to the source platform feature identifier according to the preset data format; The text content and metadata information in the original information are extracted using the parsing strategy. The text content and metadata information are reorganized into a tree-like hierarchical structure according to the preset data format; Generate a standardized data structure that includes the tree-like hierarchical structure.

3. The cross-platform information interaction method as described in claim 1, characterized in that, Extracting multimedia content elements from the standardized data structure and performing format conversion on the multimedia content elements to generate standardized multimedia content includes: Traverse the node hierarchy of the standardized data structure and identify the multimedia content elements contained in the node hierarchy; Determine the specific media type of the multimedia content element; Query the format conversion strategy corresponding to the specific media type; The multimedia content elements are subjected to multimedia format conversion operations using the aforementioned format conversion strategy to generate converted content. Perform integrity verification on the converted content and generate an integrity verification value; The converted content is combined with the integrity verification value to generate standardized multimedia content.

4. The cross-platform information interaction method as described in claim 1, characterized in that, Encapsulating the standardized data structure and the standardized multimedia content into a data packet to be transmitted includes: Create a wrapper template that includes format identifiers; The standardized data structure is serialized into a first data unit, and the standardized multimedia content is serialized into a second data unit; Insert the first data unit and the second data unit into the corresponding positions of the encapsulation template; Obtain the current system clock to generate an encapsulation timestamp, and determine the current data volume of the encapsulation template as the data packet size; Generate a header data unit that includes the encapsulation timestamp and the data packet size; The header data unit is combined with the encapsulation template to generate a combined data body; The overall check value is determined based on the combined data body, and the overall check value is combined with the combined data body to generate a data packet to be transmitted.

5. The cross-platform information interaction method as described in claim 1, characterized in that, Obtaining the network latency and bandwidth parameters of the target platform, and selecting a transmission path based on the network latency and bandwidth parameters, including: Send network probe data packets to the target platform and receive response data packets returned by the target platform; The network latency time difference is determined based on the timestamp of sending the network probe data packet and the timestamp of receiving the response data packet; The effective payload transmission rate of the response data packet is measured as a bandwidth parameter. Obtain the preset set of available transmission paths; Based on the network latency difference and bandwidth parameters, analyze the performance indicators of each transmission path in the set of available transmission paths; Select the transmission path with the best performance index from the set of available transmission paths as the target transmission path.

6. The cross-platform information interaction method as described in claim 1, characterized in that, The process of compressing the data packet to be transmitted to generate a compressed data packet, and then sending the compressed data packet to the target platform through the transmission path, includes: Analyze the content structure features of the data packet to be transmitted; Based on the aforementioned content structure characteristics, high-priority content identifiers and ordinary-priority content identifiers are set; Select a compression module that matches the content structure features; The compression module performs lossless compression on data blocks marked with high-priority content identifiers and lossy compression on data blocks marked with ordinary-priority content identifiers, generating lossless compressed data blocks and lossy compressed data blocks respectively. The lossless compressed data block and the lossy compressed data block are recombined to generate a compressed data packet; A transmission session connection is established with the target platform through the transmission path; The compressed data packet is divided into sequentially arranged transmission data units; The transmission data units are sent sequentially through the transmission session connection; Receive the transmission confirmation response returned by the target platform.

7. The cross-platform information interaction method as described in claim 1, characterized in that, The target platform receives and parses the compressed data packet to present the original information content, including: The compressed data packet is obtained through the data receiving interface of the target platform; Verify whether the overall checksum in the compressed data packet is valid; Perform decompression processing on the verified compressed data packets to restore them to a combined data body; Separate the header data unit and the encapsulation template from the combined data body; Extract the first data unit and the second data unit from the encapsulation template; Perform deserialization on the first data unit to reconstruct a standardized data structure; Perform deserialization on the second data unit to reconstruct standardized multimedia content; The standardized data structure is adapted according to the rendering capabilities of the target platform to obtain the adapted data structure; The standardized multimedia content is adapted according to the playback capabilities of the target platform to obtain adapted multimedia content. The adapted data structure and the adapted multimedia content are combined to present the original information content.

8. A cross-platform information interaction device, characterized in that, The cross-platform information interaction device includes: The source information parsing module is used to obtain the original information sent by the source platform and parse the original information into a standardized data structure according to a preset data format. A multimedia format conversion module is used to extract multimedia content elements from the standardized data structure and perform format conversion on the multimedia content elements to generate standardized multimedia content. A data encapsulation module is used to encapsulate the standardized data structure and the standardized multimedia content into a data packet to be transmitted. The transmission path selection module is used to obtain the network latency and bandwidth parameters of the target platform, and select the transmission path according to the network latency and bandwidth parameters; The data compression and transmission module is used to compress the data packet to be transmitted to generate a compressed data packet, and send the compressed data packet to the target platform through the transmission path; The target platform parsing and presentation module is used to receive and parse the compressed data packet through the target platform and present the original information content.

9. A computer device, characterized in that, The computer device includes a memory, a processor, and a cross-platform information interaction program stored in the memory and executable on the processor. When executed by the processor, the cross-platform information interaction program implements the steps of the cross-platform information interaction method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a cross-platform information interaction program, which, when executed by a processor, implements the steps of the cross-platform information interaction method as described in any one of claims 1-7.

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