A multi-platform data sharing monitoring and statistics method and system

Abstract

The application discloses a kind of multi-platform data sharing monitoring statistics methods, comprising: sharing user triggers sharing request, calls the sharing identification generation interface of cross-platform server;Cross-platform server generates tracking parameter;Cross-platform server generates sharing link according to tracking parameter and URL;Cross-platform server generates reading identification and shared user identification in response to target platform loading sharing link, and store the user behavior basic information of shared user;Cross-platform server constructs the association of reading identification, shared user identification, sharing behavior identification and user behavior basic information;Cross-platform server generates target statistical data according to association.This application makes the sharing data of different platforms directly associated through standardized fields, forms a full-link tracking mechanism, which can significantly improve the accuracy, efficiency and traceability of social sharing behavior tracking.The application also discloses a system, an electronic device and a storage medium for implementing the above method.

Description

A Multi-Platform Data Sharing Monitoring and Statistical Method and System Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to a multi-platform data sharing, monitoring, and statistical method and system. Background Technology

[0002] With the rapid development of mobile internet and social media platforms, content sharing across multiple platforms (such as WeChat, QQ, Weibo, Douyin, and Xiaohongshu) has become a mainstream method of information dissemination. To evaluate sharing effectiveness and optimize dissemination strategies, businesses, content creators, and platform operators generally need to track and statistically analyze users' cross-platform social sharing behavior, including but not limited to: User behavior tracking: recording who (user ID), when (timestamp), through which platform (e.g., WeChat / Douyin), what content (content ID), and subsequent clicks, conversions, and other derivative behaviors; Cross-platform statistics: integrating sharing data from different social platforms (e.g., WeChat Mini Programs, QQ Space, Weibo) to analyze each platform's traffic contribution, user preferences, and conversion efficiency; Dissemination chain reconstruction: tracing the complete dissemination path of shared content (e.g., user A → user B → user C), identifying key dissemination nodes (e.g., "super spreaders"), and optimizing viral growth strategies.

[0003] However, different social media platforms (such as WeChat, Douyin, and Weibo) have independent account systems, API interfaces, and data specifications. Furthermore, due to platform policies, sharing behavior data is typically scattered across the servers of each platform or the backend systems of third-party applications. Data is not interoperable between platforms, and within the same platform, multiple scenarios are fragmented. Content shared within the same application through different social scenarios such as "private chats with friends," "group chats," and "Moments / Features" may be categorized and stored in different database tables, lacking a unified identifier and association. While existing third-party statistical tools support multi-platform access, they rely on limited publicly available interfaces from each platform (e.g., only able to obtain share counts and clicks), failing to deeply track user identity (e.g., real UID), sharing paths, and other key information. This results in a single data dimension, making it difficult for operators to quickly obtain accurate sharing statistics. Moreover, due to platform data isolation, it is difficult to statistically analyze complete dissemination chain information across platforms. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, this invention provides a multi-platform data sharing, monitoring, and statistical method and system. The technical problem to be solved by this invention is achieved through the following technical solution:

[0005] The first aspect of this invention provides a multi-platform data sharing monitoring and statistics method, comprising the following steps:

[0006] In response to a sharing request triggered by a user, the sharing platform calls the sharing identifier generation interface of the cross-platform server.

[0007] The sharing identifier generation interface generates identifier parameters and a sharing URL based on the sharing request; wherein, the identifier parameters include: sharing user identifier, sharing timestamp, target platform identifier, and sharing behavior identifier; the sharing user identifier, the target platform identifier, the sharing timestamp, and the random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier;

[0008] The cross-platform server encodes the identifier parameter in Unicode and generates a digital signature using a signature algorithm, and then generates tracking parameters based on the identifier parameter and the digital signature;

[0009] The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL;

[0010] After obtaining the sharing link, the sharing platform sends it to the target platform. After loading the sharing link, the target platform encodes the device attribute information, loading timestamp, and random string into Unicode and uses a signature algorithm to generate the identifier of the user being shared with.

[0011] The cross-platform server responds to the target platform loading the sharing link by encoding the sharing behavior identifier, loading timestamp, loading page identifier and the shared user identifier into Unicode, generating a reading identifier using a signature algorithm, and storing the basic user behavior information of the shared user.

[0012] The cross-platform server establishes the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and basic user behavior information;

[0013] The cross-platform server generates target statistical data based on the association relationship.

[0014] In one embodiment of the present invention, the share link indicates a business form;

[0015] The cross-platform server constructs the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and basic user behavior information, including:

[0016] The cross-platform server constructs and stores the association between the reading identifier, the shared user identifier, and the sharing behavior identifier;

[0017] The cross-platform server responds to the target platform's call to the form submission interface by constructing and storing the association relationship between the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information indication form data, and storing the data result indicating the submission of the business form.

[0018] In one embodiment of the present invention, the structure of the tracking parameters is as follows:

[0019] uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature.

[0020] In one embodiment of the present invention, the cross-platform server generates a sharing link based on the tracking parameters and the sharing URL of the target platform, including:

[0021] The cross-platform server adds the tracking parameters to the end of the target platform's sharing URL to generate a sharing link.

[0022] A second aspect of this invention provides a multi-platform data sharing monitoring and statistics system, characterized in that it includes:

[0023] The sharing platform is used to respond to sharing requests triggered by sharing users by calling the sharing identifier generation interface of the cross-platform server.

[0024] The sharing identifier generation interface is used to generate identifier parameters and a sharing URL based on the sharing request; wherein, the identifier parameters include: sharing user identifier, sharing timestamp, target platform identifier, and sharing behavior identifier; the sharing user identifier, the target platform identifier, the sharing timestamp, and the random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier;

[0025] The cross-platform server is used to encode the identifier parameter in Unicode and generate a digital signature using a signature algorithm, and then generate tracking parameters based on the identifier parameter and the digital signature;

[0026] The cross-platform server is also used to generate a sharing link based on the tracking parameters and the sharing URL;

[0027] The sharing platform is also used to obtain the sharing link and send it to the target platform. After loading the sharing link, the target platform encodes the device attribute information, loading timestamp and random string into Unicode and uses a signature algorithm to generate the identifier of the user being shared with.

[0028] The cross-platform server is also configured to, in response to the target platform loading the sharing link, encode the sharing behavior identifier, loading timestamp, loading page identifier and the shared user identifier in Unicode and generate a reading identifier using a signature algorithm, and store the basic user behavior information of the shared user;

[0029] The cross-platform server is also used to construct the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information;

[0030] The cross-platform server is also used to generate target statistical data based on the association relationship.

[0031] In one embodiment of the present invention, the share link indicates a business form;

[0032] The process of constructing the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information includes:

[0033] Construct and store the association relationship between the reading identifier, the shared user identifier, and the sharing behavior identifier;

[0034] In response to the target platform calling the form submission interface, the association relationship of the form data indicating the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information is constructed and stored, and the data result indicating the submission of the business form is stored.

[0035] In one embodiment of the present invention, the structure of the tracking parameters is as follows:

[0036] uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature.

[0037] In one embodiment of the present invention, the cross-platform server generates a sharing link based on the tracking parameters and the sharing URL of the target platform, including:

[0038] The cross-platform server adds the tracking parameters to the end of the target platform's sharing URL to generate a sharing link.

[0039] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a multi-platform data sharing monitoring and statistics method provided in the first aspect of the present invention.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a multi-platform data sharing monitoring and statistics method provided in the first aspect of the present invention.

[0041] The beneficial effects of this invention are:

[0042] This invention uses cross-platform universal parameter fields and Unicode encoding to uniformly encode these fields (such as user ID, platform identifier, timestamp, etc.), enabling direct association of shared data across different platforms through standardized fields. It generates multiple identifiers for each sharing behavior, forming a full-link tracking mechanism that significantly improves the accuracy, efficiency, and traceability of social sharing behavior tracking. Each sharing behavior generates a multi-dimensional unique identifier (including cross-platform tracking parameters based on Unicode encoding, share ID, read ID, and tracking user ID). Standardized parameter transmission rules adapt to different platform URL parsing logic, ensuring that shared links can be transmitted normally on any social platform and that data is transmitted completely. The signature (such as HMAC-SHA256) generated based on standardized parameters (such as UTF-8 encoding) effectively prevents attackers from modifying key parameters such as user ID and timestamp to impersonate others and ensures the authenticity of statistical data.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0044] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0046] Figure 1 is a flowchart illustrating a multi-platform data sharing monitoring and statistical method provided in an embodiment of the present invention;

[0047] Figure 2 is a block diagram of a multi-platform data sharing monitoring and statistics system provided in an embodiment of the present invention. Detailed Implementation

[0048] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0049] As shown in Figure 1, a first aspect of the present invention provides a multi-platform data sharing monitoring and statistics method, comprising the following steps:

[0050] Step 11: In response to the sharing request triggered by the sharing user, the sharing platform calls the sharing identifier generation interface of the cross-platform server.

[0051] Step 12: The share identifier generation interface generates identifier parameters and share URL based on the share request.

[0052] The identification parameters include: sharing user identifier, sharing timestamp, target platform identifier, and sharing behavior identifier; the sharing user identifier, target platform identifier, sharing timestamp, and random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier.

[0053] Step 13: The cross-platform server encodes the identifier parameters in Unicode and generates a digital signature using a signature algorithm. Then, it generates tracking parameters based on the identifier parameters and the digital signature.

[0054] Step 14: The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL.

[0055] Step 15: After obtaining the sharing link, the sharing platform sends it to the target platform. After loading the sharing link, the target platform encodes the device attribute information, loading timestamp, and random string into Unicode and uses a signature algorithm to generate the identifier of the user being shared with.

[0056] Step 16: The cross-platform server responds to the target platform by loading the sharing link, encoding the sharing behavior identifier, loading timestamp, loading page identifier, and shared user identifier into Unicode, generating a reading identifier using a signature algorithm, and storing the basic user behavior information of the shared user.

[0057] Step 17: The cross-platform server establishes the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information.

[0058] Step 18: The cross-platform server generates target statistics based on the association relationship.

[0059] This invention uses cross-platform universal parameter fields and Unicode encoding to uniformly encode these fields (such as user ID, platform identifier, timestamp, etc.), enabling direct association of shared data across different platforms through standardized fields. It generates multiple identifiers for each sharing behavior, forming a full-link tracking mechanism that significantly improves the accuracy, efficiency, and traceability of social sharing behavior tracking. Each sharing behavior generates a multi-dimensional unique identifier (including cross-platform tracking parameters based on Unicode encoding, share ID, read ID, and tracking user ID). Standardized parameter transmission rules adapt to different platform URL parsing logic, ensuring that shared links can be transmitted normally on any social platform and that data is transmitted completely. The signature (such as HMAC-SHA256) generated based on standardized parameters (such as UTF-8 encoding) effectively prevents attackers from modifying key parameters such as user ID and timestamp to impersonate others and ensures the authenticity of statistical data.

[0060] Based on the first aspect of the present invention, the second aspect of the present invention provides a more detailed description of a multi-platform data sharing monitoring and statistical method, which includes the following steps:

[0061] Step 21: In response to the sharing request triggered by the sharing user, the sharing platform calls the sharing identifier generation interface of the cross-platform server.

[0062] In this step, the sharing user is the user who sends the information they want to share to the recipient user. The sharing platform is the platform the sharing user is currently using. The sharing user can share information from the sharing platform with the recipient user on the target platform. There can be one or more target platforms. The sharing platform can be, for example, an application such as WeChat, QQ, or Weibo, or an H5 page or web page, and the target platform can be, for example, an application such as WeChat, QQ, or Weibo, or an H5 page or web page.

[0063] When a user needs to share information while using the sharing platform, the user clicks the share button and sends a share request to the sharing platform. The sharing platform then calls the cross-platform server's share identifier generation interface.

[0064] Step 22: The sharing identifier generation interface generates identifier parameters and a sharing URL adapted to the target platform based on the sharing request.

[0065] The identification parameters include: sharing user identifier, sharing timestamp, target platform identifier, and sharing behavior identifier; the sharing user identifier, target platform identifier, sharing timestamp, and random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier.

[0066] In this step, the sharing identifier generation interface generates identifier parameters based on the information carried in the sharing request. Specifically, the information carried in the sharing request generates a 32-bit string sharing user identifier using the UUID v4 algorithm. The sharing user identifier (UID) is a unique identifier for the sharing user on the cross-platform server. Different identity identifiers of the same sharing user on different sharing platforms will only have a unique sharing user identifier on the cross-platform server (logged-in users are associated with an account ID, and unlogged-in users generate a temporary UID). The sharing timestamp is the millisecond-level time of the sharing, obtained from the information carried in the sharing request. The target platform identifier (PlatformID) is obtained from the information carried in the sharing request, such as "qq" for QQ, "weibo" for Weibo, and "dingtalk" for DingTalk. The sharing behavior identifier (ShareID) is calculated by encoding "UID + PlatformID + Timestamp + 3-digit RandomSalt" into Unicode (UTF-8) and then using the hash-based message authentication code HMAC-SHA256.

[0067] Example of a ShareID generation algorithm:

[0068] ShareID = HMAC-SHA256(UTF8(UID + Timestamp + PlatformID +RandomSalt), SECRET_KEY)

[0069] SECRET_KEY: Server-side key, used only for signature generation and not involved in transmission. HMAC-SHA256: Ensures the uniqueness and unpredictability of ShareID. UTF8() indicates that the concatenated string will be converted to a UTF-8 byte stream.

[0070] Encoding rules: Convert the binary result to a hexadecimal string (e.g., 8a7b6c5d4e3f2a1b0c9d). The length is fixed (e.g., 32 bits) for easy storage and transmission.

[0071] ShareID can uniquely identify a sharing behavior (such as user A sharing an article to WeChat Moments), and serves as a core parameter for cross-platform tracking, connecting subsequent reading and conversion behaviors.

[0072] The share identifier generation interface generates a share URL that is compatible with the target platform based on the share request.

[0073] Step 23: The cross-platform server encodes the identifier parameter into Unicode to obtain the encoded identifier parameter, calculates the digital signature based on the encoded identifier parameter, and then generates the tracking parameter based on the identifier parameter and the digital signature.

[0074] Use the HMAC-SHA256 algorithm to generate a digital signature. The formula is as follows:

[0075] HMAC-SHA256(UTF8(UID + Timestamp + PlatformID + ShareID), SECRET_KEY)

[0076] Here, SECRET_KEY represents a pre-configured key on the server, used only for signature generation and verification, and not involved in transmission. UTF8() converts the concatenated string into a UTF-8 byte stream.

[0077] Preferably, the raw binary result of HMAC-SHA256 is Base64 encoded to generate Base64_Signature. The Base64 encoded string is then URL-safely converted (+ is replaced with -, / is replaced with _, and = is padded at the end) to ensure that the signature parameters do not conflict with special characters in the URL.

[0078] The converted signature is assigned to the `sig` parameter, which, together with other tracing parameters (such as `uid`, `ts`, `pid`, and `sid`), forms the tracing parameters (`trace_param`). The structure of the tracing parameters is as follows:

[0079] uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature, that is, uid=UID&ts=Timestamp&pid=PlatformID&sid=ShareID&sig=Unicode_Signature.

[0080] Step 24: The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL.

[0081] In this step, the tracking parameters mentioned above are appended to the end of the sharing URL to obtain the sharing link.

[0082] For example, a cross-platform server generates UID=123e4567-e89b-12d3-a456-426614174000, Timestamp=1695500000000, PlatformID=wechat, and ShareID=8a7b6c5d4e3f2a1b0c9d. After concatenating the string "123e4567-e89b-12d3-a456-4266141740001695500000000wechat8a7b6c5d4e3f2a1b0c9d", a signature is generated using HMAC-SHA256 and a key, and then processed via Base64_URL. After conversion, we get sig=7f8d9c0a1b2e3f4g5h6j. The final sharing link format is as follows: https: / / example.com / share?trace_param=uid=123e4567-e89b-12d3-a456-426614174000&ts=1695500000000&pid=wechat&sid=8a7b6c5d4e3f2a1b0c9d&sig=7f8d9c0a1b2e3f4g5h6j.

[0083] Step 25: After obtaining the sharing link, the sharing platform sends it to the target platform.

[0084] After obtaining the sharing link, the sharing platform adapts the link according to the target platform's rules (such as domain verification for WeChat) so that users can share it on their social networks (friends / group chats / moments). The sharing platform then sends the sharing link to the target platform.

[0085] When shared links spread within social networks, clients on various platforms (such as the built-in browser of QQ and the Weibo app) automatically retain the multi-dimensional ID in trace_param (no fields are lost due to Unicode encoding adaptation).

[0086] Step 26: After the target platform loads the sharing link, it encodes the device attribute information, loading timestamp, and random character (RandomToken) string of the target platform into Unicode and uses a signature algorithm to generate the identifier of the user being shared.

[0087] In this step, when the user being shared with clicks the share link, the target platform will load the page and content indicated by the share link. At the same time, the target platform or its server will generate a share user identifier (TrackUID), indicating that the user being shared with has read the shared content.

[0088] The target platform or its server can obtain the device's attribute information and current loading timestamp. Device attribute information includes ClientIP (the real IP address of the user being shared with) and UserAgent (user agent information, such as operating system, browser type, device fingerprint, etc.). For example, ClientIP + UserAgent + Timestamp + RandomToken are encoded in UTF-8, and then HMAC-SHA256 is used to calculate TrackUID. The specific algorithm is as follows:

[0089] TrackUID = HMAC-SHA256(UTF8(ClientIP + UserAgent + Timestamp +RandomToken), SECRET_KEY)

[0090] Encoding rules: Convert the binary result to Base64_URL encoding (e.g., dGhpcyIsInRlc3QiLCJhYmMiXQ) to adapt to URL transmission. The length is variable (typically 32-64 characters), balancing uniqueness and transmission efficiency.

[0091] TrackUID identifies a single reading action (e.g., user B reading an article via a shared link), anonymizing the reading behavior of users who are not logged in and preventing privacy leaks. TrackUID is related to device attributes. If the user clicking the shared link for the first time, a TrackUID (formatted like UID) is generated and bound to the device; if the user is an existing user, it is directly associated with their historical TrackUID.

[0092] Step 27: The cross-platform server responds to the target platform by loading the sharing link, encodes the sharing behavior identifier, loading timestamp, loading page identifier, and shared user identifier in Unicode, generates a reading identifier using a signature algorithm, and stores the basic user behavior information of the shared user.

[0093] In this step, after the user being shared with clicks the shared link on the target platform, the target platform loads the shared link. While the target platform loads the shared link, the cross-platform server extracts the UID and ShareID from the trace_param, verifies the validity of the Unicode_Signature, and filters out tampering requests. The cross-platform server can obtain basic user behavior information, the page identifier of the page indicated by the shared link, and the TrackUID. After successful verification, a ReadID is generated and stored, representing the basic user behavior information of the user being shared with.

[0094] For example, the ShareID + TrackUID + PageID + Timestamp are encoded in UTF-8, and then the ReadID is calculated using HMAC-SHA256. The specific algorithm is as follows:

[0095] ReadID = HMAC-SHA256(UTF8(ShareID + TrackUID + PageID + Timestamp),SECRET_KEY)

[0096] SECRET_KEY: Server-side key, separate from the keys for ShareID and TrackUID. HMAC-SHA256: Ensures strong association and tamper-proof nature of ReadID.

[0097] Encoding rules: Convert the binary result to a hexadecimal string (e.g., a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6). The length is fixed (e.g., 32 bits) for easy database indexing.

[0098] ReadID uniquely identifies a complete reading action (e.g., user B reading an article shared by user A). It acts as a bridge between "sharing" and "reading," supporting subsequent conversion analysis.

[0099] Basic user behavior information includes device attribute information (User-Agent and the operating system, browser type, IP address and the region obtained through IP address resolution) and behavior information (link opening time, page dwell time, whether scrolling to the bottom, and form data filled in by the user).

[0100] The sharing scenario in this embodiment typically consists of two stages: first, a reading stage, and then a form submission stage. The forms include, but are not limited to, registration forms, order forms, and questionnaires, which are the two stages of reading and submitting forms.

[0101] Step 28: The cross-platform server establishes the association between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information.

[0102] Step 28 includes steps 281-282:

[0103] Step 281: The cross-platform server builds and stores the association between the reading identifier, the shared user identifier, and the sharing behavior identifier.

[0104] This step corresponds to the reading stage. After generating ReadID and TrackUID, the association relationship of "ShareID → TrackUID → ReadID" is built and stored. UID, ShareID, TrackUID, ReadID, and basic user behavior information are written into the data warehouse of the cross-platform server to form a single share-read association record.

[0105] Step 282: The cross-platform server responds to the target platform's call to the form submission interface, constructs and stores the association relationship between the form data indicating the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information, and stores the data result and form ID indicating the submission of the business form.

[0106] This step corresponds to the form submission stage. The target platform's page has built-in customized forms corresponding to the business (such as registration forms, order forms, and questionnaires). The form submission interface carries ReadID and TrackUID. After the shared user reads the form, they fill it out and submit it. When submitting the form, the cross-platform server receives the form data, establishes and stores the association relationship of "form data → TrackUID → ReadID → ShareID → UID", and stores the form submission result (such as registration success / failure, order amount, questionnaire options, etc.). The direct source (TrackUID) and indirect source (UID) of the form data can also be stored on the cross-platform server.

[0107] The association logic of the above example relationship is as follows:

[0108] ShareID ↔ ReadID: Link sharing and reading behaviors using ShareID.

[0109] TrackUID ↔ ReadID: Anonymously identifies the user who reads the content using TrackUID.

[0110] TrackUID ↔ User Behavior: Records the device, region, and behavioral patterns of the user who reads the content (such as the duration of page stay).

[0111] In this embodiment, an example of the overall data flow for the reading step and the form submission step is provided:

[0112] User (UID=A) shares an article → ShareID=123456 is generated → User B clicks the link → The target platform generates TrackUID=abcdef → The server generates ReadID=xyz789 → Reading behavior is recorded → User B submits a form → The form carries ReadID=xyz789 → Conversion data is associated.

[0113] In this embodiment, global uniqueness is ensured through HMAC-SHA256 and a random salt (RandomSalt / RandomToken). The key (SECRET_KEY) is held only by the server, preventing forgery and ensuring security. Base64_URL encoding adapts to different platform URL rules. Dynamically adding parameters (such as PageID, CampaignID) is supported to meet refined analysis needs, providing scalability. Unlogged-in users are anonymized and tracked using TrackUID, avoiding exposure of personal identity information and ensuring privacy protection.

[0114] Step 29: The cross-platform server generates target statistical data based on the association relationship.

[0115] ShareID is used to uniquely identify a single sharing behavior and trace the source of dissemination; ReadID is used to identify a single shared link opening behavior; and TrackUID is used to associate multiple reading and form submission behaviors of the same user.

[0116] Multidimensional data analysis based on multi-ID associated data can generate corresponding visual statistical reports or relationship network diagrams, such as the sharing volume of each platform is statistically analyzed based on ShareID (ShareID carries PlatformID).

[0117] Before generating statistical data, the stored data can be cleaned and further correlated:

[0118] First, duplicate read records are deduplicated based on ShareID+ReadID, and duplicate submission records are deduplicated based on TrackUID+Form ID;

[0119] Secondly, missing region and device type records are filled in using the IP database and User-Agent database; missing ShareID reading records are matched in reverse using URL history snapshots.

[0120] Finally, based on the "ShareID" association, sharing and reading behaviors are linked; based on the "TrackUID" association, multiple reading / form submission behaviors of the same user are linked; and based on the "UID" association, multiple sharing behaviors of the same user are linked, generating a unified cross-platform data view (including three-layer association of sharing-reading-conversion).

[0121] For example, various data statistics and analyses can be performed:

[0122] It can analyze the propagation chain: with ShareID as the node and ReadID as the connection line, a relationship network diagram of "sharing user → reading user → secondary sharing user" can be formed, showing the multi-level propagation path and the number of users covered.

[0123] Platform-level analysis is possible: sharing volume (statistics by ShareID), reading conversion rate (read volume / sharing volume, calculated by ReadID / ShareID), and form submission conversion rate (submission volume / read volume, calculated by Form ID / ReadID).

[0124] User-level analysis is possible, including: distribution of reading users' devices (iOS / Android / PC, via TrackUID User-Agent), geographic distribution (via IP), and the average number of reading users per ShareID (propagation efficiency, via ShareID and ReadID).

[0125] It can perform conversion funnel analysis: the entire conversion rate from sharing (ShareID generation) → reading (ReadID generation) → form opening (TrackUID association) → form submission (form data association) → final conversion (registration / order placement success), and locate the drop-off nodes in the funnel.

[0126] It can also combine TrackUID's IP and User-Agent information to analyze the geographic distribution and device preferences of reading users, as well as to count the number of reads, form submissions, and registration success rates generated by a particular share (ShareID=123456).

[0127] In one feasible implementation, the trace_param containing multi-dimensional IDs can be dynamically encoded and adjusted to accommodate URL parameter limitations on different platforms.

[0128] If the platform limits the total URL length (e.g., QQ Space ≤ 2048 characters), use Base64 encoding (UTF-8 byte encoding) to compress non-critical fields such as Timestamp and TrackUID, which will reduce the length by 30%~40% after compression.

[0129] If the platform filters symbols such as "&" and "=" (e.g., on Weibo), the parameter separator will be replaced with Unicode-compatible characters ("&" → "\u0026", "=" → "\u003D"), and the server will automatically reverse the mapping during parsing.

[0130] This invention, through Unicode-encoded standardized parameter design and a full-link tracking mechanism, introduces refined identifiers such as share ID, read ID, and tracking user ID to achieve unified collection, multi-level association, and visual analysis of shared data across multiple platforms, solving the problems of data silos, inefficient statistics, and unclear propagation link tracing. This invention generates a multi-dimensional unique identifier for each sharing behavior (including cross-platform tracking parameters based on Unicode encoding, share ID, read ID, and tracking user ID), adapts to different platform URL parsing logic through standardized parameter transmission rules, and combines server-side log collection, ID association engine, and data aggregation module to achieve refined full-link tracking and statistics of cross-platform sharing behavior from initiation, propagation, to conversion.

[0131] As shown in Figure 2, a third aspect of the present invention provides a multi-platform data sharing monitoring and statistics system, comprising:

[0132] The sharing platform 31 is used to respond to sharing requests triggered by sharing users by calling the sharing identifier generation interface of the cross-platform server;

[0133] The sharing identifier generation interface is used to generate identifier parameters and a sharing URL based on the sharing request. The identifier parameters include: sharing user identifier, sharing timestamp, target platform identifier, and sharing behavior identifier. The sharing user identifier, target platform identifier, sharing timestamp, and random verification code are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier.

[0134] Cross-platform server 32 is used to encode the identifier parameters in Unicode and generate a digital signature using a signature algorithm, and then generate tracking parameters based on the identifier parameters and the digital signature;

[0135] The cross-platform server 32 is also used to generate sharing links based on tracking parameters and sharing URLs;

[0136] The sharing platform 31 is also used to obtain the sharing link and send it to the target platform 33. After loading the sharing link, the target platform encodes the device attribute information, loading timestamp and random string into Unicode and uses a signature algorithm to generate the identifier of the user being shared.

[0137] The cross-platform server 32 is also used to respond to the target platform loading the shared link by encoding the sharing behavior identifier, loading timestamp, loading page identifier and the shared user identifier in Unicode and generating a reading identifier using a signature algorithm, and storing the basic user behavior information of the shared user;

[0138] The cross-platform server 32 is also used to build the association between reading identifiers, shared user identifiers, sharing behavior identifiers, and basic user behavior information;

[0139] The cross-platform server 32 is also used to generate target statistics based on relationships.

[0140] In one embodiment of the present invention, the share link indicates a business form;

[0141] Establish the association between reading identifiers, shared user identifiers, sharing behavior identifiers, and basic user behavior information, including:

[0142] Build and store the association between reading identifier, shared user identifier, and sharing behavior identifier;

[0143] In response to the target platform's call to the form submission interface, construct and store the association relationships of form data indicating the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information, and store the data results indicating the submission of the business form.

[0144] In one embodiment of the present invention, the structure of the tracking parameters is as follows:

[0145] uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature.

[0146] In one embodiment of the present invention, the cross-platform server generates a sharing link based on tracking parameters and the sharing URL of the target platform, including:

[0147] The cross-platform server adds tracking parameters to the end of the sharing URL on the target platform to generate a sharing link.

[0148] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the multi-platform data sharing monitoring and statistics method provided by the present invention described above.

[0149] A fifth aspect of the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the multi-platform data sharing monitoring and statistics method provided in the present invention described above.

[0150] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage system located remotely from the aforementioned processor.

[0151] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware systems.

[0152] The method provided in this invention can be applied to electronic devices. Specifically, the electronic device can be a desktop computer, a portable computer, a smart mobile terminal, a server, etc. No limitation is made herein; any electronic device that can implement this invention falls within the protection scope of this invention.

[0153] For system / electronic device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be found in the description of the method embodiments.

[0154] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0155] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0156] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0157] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A multi-platform data sharing, monitoring, and statistical method, characterized in that, Includes the following steps: In response to a sharing request triggered by a user, the sharing platform calls the sharing identifier generation interface of the cross-platform server. The sharing identifier generation interface generates identifier parameters and a sharing URL based on the sharing request. The identifier parameters include: a sharing user identifier, a sharing timestamp, a target platform identifier, and a sharing behavior identifier. The sharing user identifier, the target platform identifier, the sharing timestamp, and a random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier. The cross-platform server Unicode-encodes the identifier parameters and generates a digital signature using a signature algorithm, then generates tracking parameters based on the identifier parameters and the digital signature. The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL. After obtaining the sharing link, the sharing platform sends it to the target platform. After loading the sharing link, the target platform Unicode-encodes the device attribute information, the loading timestamp, and a random string and generates a shared user identifier using a signature algorithm. In response to the target platform loading the sharing link, the cross-platform server Unicode-encodes the sharing behavior identifier, the loading timestamp, the loading page identifier, and the shared user identifier. The system encodes and uses a signature algorithm to generate a reading identifier, and stores the basic user behavior information of the user whose information is being shared; the cross-platform server constructs the association relationship between the reading identifier, the user identifier whose information is being shared, the sharing behavior identifier, and the basic user behavior information; the cross-platform server generates target statistical data based on the association relationship.

2. The method as described in claim 1, characterized in that, The sharing link indicates a business form; the cross-platform server constructs the association relationship between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information, including: the cross-platform server constructs and stores the association relationship between the reading identifier, the shared user identifier, and the sharing behavior identifier; the cross-platform server responds to the target platform's call to the form submission interface, constructs and stores the association relationship between the form data indicated by the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information, and stores the data result indicating the submission of the business form.

3. The method as described in claim 1, characterized in that, The structure of the tracking parameters is as follows: uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature.

4. The method as described in claim 1, characterized in that, The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL of the target platform, including: the cross-platform server adding the tracking parameters to the end of the sharing URL of the target platform to generate a sharing link.

5. A multi-platform data sharing, monitoring, and statistical system, characterized in that, include: The sharing platform is used to respond to sharing requests triggered by sharing users by calling the sharing identifier generation interface of the cross-platform server. The sharing identifier generation interface is used to generate identifier parameters and a sharing URL based on the sharing request. The identifier parameters include: a sharing user identifier, a sharing timestamp, a target platform identifier, and a sharing behavior identifier. The sharing user identifier, the target platform identifier, the sharing timestamp, and a random checksum are Unicode encoded and a signature algorithm is used to generate the sharing behavior identifier. The cross-platform server is used to Unicode encode the identifier parameters and generate a digital signature using a signature algorithm, and then generate tracking parameters based on the identifier parameters and the digital signature. The cross-platform server is also used to generate a sharing link based on the tracking parameters and the sharing URL. The sharing platform is also used to obtain the sharing link and send it to the target platform. After loading the sharing link, the target platform Unicode-encodes the device attribute information, the loading timestamp, and a random string and uses a signature algorithm to generate a shared user identifier. The cross-platform server is also used to, in response to the target platform loading the sharing link, Unicode-encode the sharing behavior identifier, the loading timestamp, the loading page identifier, and the shared user identifier. The cross-platform server is used to encode and generate a reading identifier using a signature algorithm, and to store basic user behavior information of the user whose information is being shared; the cross-platform server is also used to construct the association relationship between the reading identifier, the user identifier whose information is being shared, the sharing behavior identifier, and the basic user behavior information; the cross-platform server is also used to generate target statistical data based on the association relationship.

6. The system as described in claim 5, characterized in that, The sharing link indicates a business form; the construction of the association relationship between the reading identifier, the shared user identifier, the sharing behavior identifier, and the user behavior basic information includes: constructing and storing the association relationship between the reading identifier, the shared user identifier, and the sharing behavior identifier; responding to the target platform's call to the form submission interface, constructing and storing the association relationship between the form data indicated by the reading identifier, the shared user identifier, the sharing behavior identifier, the sharing user identifier, and the user behavior basic information, and storing the data result indicating the submission of the business form.

7. The system as described in claim 5, characterized in that, The structure of the tracking parameters is as follows: uid=sharing user identifier&ts=sharing timestamp&pid=target platform identifier&sid=sharing behavior identifier&sig=digital signature.

8. The system as described in claim 5, characterized in that, The cross-platform server generates a sharing link based on the tracking parameters and the sharing URL of the target platform, including: the cross-platform server adding the tracking parameters to the end of the sharing URL of the target platform to generate a sharing link.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-platform data sharing monitoring and statistics method as described in any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-platform data sharing monitoring and statistical method as described in any one of claims 1 to 4.

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