Demand propagation referral chain tracking method, apparatus, device and medium

Abstract

The application relates to a demand propagation referral chain tracking method, device, equipment and medium. The method comprises the following steps: in response to a demand publishing instruction triggered by a demand initiator, a target demand data packet is generated; a response request sent by any response node is received, wherein the referral data of a complete propagation path from the demand initiator to the response node is contained in the response request, the referral data contains the identity of each level node on the complete propagation path, a timestamp and a corresponding hash value sequence; along the complete propagation path, the hash values of the nodes are recalculated in the direction from the demand initiator to the response node, and the hash values are compared with the hash value sequence; if all the hash values are consistent, it is confirmed that the complete propagation path is a valid referral chain, and a final referral chain hash value used for evidence preservation is generated, so that the referral chain tracking of demand propagation is completed. The application ensures the authenticity and integrity of the referral chain, and provides irrefutable evidence support for the distribution incentive.

Description

Technical Field

[0001] This application relates to the field of social networks, and more particularly to a method for tracking referral chains of demand propagation, a corresponding device, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In social networks, alumni associations, industry associations and other communities, users often post various requests for help, such as looking for jobs, suppliers, or expert recommendations, hoping to reach potential helpers through a multi-level dissemination model of "friends of friends".

[0003] However, existing demand dissemination methods based on general social tools have the following technical shortcomings:

[0004] First, the dissemination path is untraceable, and referral behavior cannot be attributed. When demand information is forwarded through social tools at each level, the original publisher cannot track how many intermediate nodes the information has passed through or who ultimately responded. This makes it impossible to identify key disseminators in the referral path, let alone incentivize effective referral behavior.

[0005] Secondly, the referral chain lacks integrity protection and is easily tampered with. Existing solutions mostly rely on manual recording of referral relationships, and the recording process is prone to omissions or malicious tampering, making the referral chain unreliable as a basis for attribution.

[0006] Third, there is insufficient privacy protection during the dissemination process. Demand information and the identity information of intermediaries lack effective protection during dissemination, making it easy for the identities of intermediaries to be leaked, thus posing a risk to user privacy and security.

[0007] Fourth, centralized storage presents single points of failure and data trust risks. If referral chain data is stored centrally by the platform, the referral records will lose their credibility if the platform is attacked or maliciously tampered with by internal personnel, and there is a risk of data loss due to single points of failure.

[0008] In summary, given that existing technologies rely on manual forwarding of demand information in social networks, resulting in untraceable propagation paths, a lack of tamper-proof protection and reliable evidence storage in referral chains, easy leakage of identity information, and the risk of single point of failure in centralized storage, the applicant has made corresponding explorations to address these issues. Summary of the Invention

[0009] The purpose of this application is to solve the above-mentioned problems by providing a demand propagation referral chain tracing method, corresponding device, electronic device and computer-readable storage medium.

[0010] To achieve the various objectives of this application, the following technical solution is adopted:

[0011] A method for tracing a referral chain for demand propagation, proposed to meet one of the purposes of this application, includes:

[0012] In response to a demand release instruction triggered by a demand initiator, a target demand data packet is generated, wherein the target demand data packet includes at least a demand identifier, an identity identifier of the demand initiator, and a root hash value generated based on the demand identifier and the identity identifier;

[0013] The system invokes a preset social relationship graph and, starting from the node corresponding to the request initiator, pushes the target request data packet outwards level by level along the node hierarchy of the social relationship graph. Each time the target request data packet is pushed through a node hierarchy, the current node, upon receiving the data packet from the previous level, appends the current node's identity identifier and timestamp to the previous level data packet and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet. The pushing stops when a preset propagation depth threshold is reached or a response node is found.

[0014] Receive a response request sent by any response node, wherein the response request contains referral data of the complete propagation path from the request initiator to the response node, and the referral data contains the identity identifier, timestamp and corresponding hash value sequence of each level node in the complete propagation path;

[0015] Along the complete propagation path, from the request initiator to the response node, the hash value of each node is recalculated sequentially and compared with the hash value sequence. If they all match, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

[0016] Optional steps for constructing a social relationship graph include:

[0017] Create a node table for storing user nodes and an edge table for storing relationship edges. The user nodes include user identity identifiers and user types, and the relationship edges include relationship starter identifiers, relationship end identifiers, relationship types, relationship weights, and relationship sources.

[0018] Configure the relationship types, which include direct friend relationships, same organization relationships, following relationships, mutual assistance relationships, and interest group relationships; direct friend relationships require confirmation from both parties, while same organization relationships and following relationships are one-way relationships;

[0019] Different initial weights are assigned to relation edges of different relation types, and relation edges with weights not lower than a first preset threshold are marked as strong relation edges, while relation edges with weights lower than the first preset threshold are marked as weak relation edges; wherein, the target demand data packet is preferentially pushed along the strong relation edges;

[0020] When any relationship edge does not interact for more than a preset time threshold, the weight of the relationship edge is gradually reduced according to a preset decay rate, and the relationship edge is automatically removed when the weight is lower than a second preset threshold, so as to construct a social relationship graph.

[0021] A full consistency check is performed on the social relationship graph every preset period to repair abnormal edges.

[0022] Optionally, the step of performing a hash operation on the hash value of the previous-level data packet and the appended data to generate the hash value of the current-level data packet includes:

[0023] Get the hash value of the previous level data packet, the identity of the current node, and the current timestamp;

[0024] The hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp are concatenated in sequence to construct a data block;

[0025] A preset encryption algorithm is used to perform a hash operation on the data block to generate the hash value of the current level data packet;

[0026] The hash value of the current-level data packet is associated with the identity identifier of the current node and the current timestamp and stored in the propagation record.

[0027] Optionally, the step of generating referral data that shows the complete propagation path from the demand initiator to the response node includes:

[0028] Using the identity identifier of the request initiator, the request identifier, and the request publication timestamp as input, a root hash value is generated, and the root hash value is associated with the identity identifier of the request initiator and the request identifier and stored as the starting record of the propagation path;

[0029] When any intermediate node forwards the target demand data packet along the social relationship graph, it obtains the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp. It then concatenates the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp into a data block in sequence, performs a hash operation on the data block to generate the hash value of the current level data packet, and associates the hash value of the current level data packet with the identity identifier of the current node and the current timestamp to store it as the current level propagation record.

[0030] The process iterates sequentially until the target requirement data packet is pushed to the response node, whereby the response node receives a complete hash value sequence containing the root hash value, the hash values ​​of data packets at each level, and the identity identifiers and timestamps of nodes at each level.

[0031] Optionally, the step of recalculating the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and comparing it with the hash value sequence, and confirming that the complete propagation path is a valid referral chain if all match, includes:

[0032] Using the root hash value as the current hash value, along the complete propagation path, according to the node order from the request initiator to the response node, the identity identifier and corresponding storage timestamp of each node are read sequentially, and the current hash value, the identity identifier of the current node and the storage timestamp of the current node are concatenated in order and then hashed to calculate the expected hash value of the node.

[0033] The expected hash value is compared with the hash value corresponding to the node in the referral data. If the expected hash value matches the hash value corresponding to the node in the referral data, the expected hash value is used as the current hash value of the next node, and the verification of the next node continues. If they do not match, the verification is deemed to have failed.

[0034] If the expected hash value of all nodes on the complete propagation path is consistent with the corresponding hash value in the referral data, and the final hash value obtained is equal to the final hash value submitted by the response node, then the complete propagation path is confirmed as a valid referral chain.

[0035] Optionally, the step of receiving a response request sent by any response node includes:

[0036] When multiple complete propagation paths point to the same response node, the response time corresponding to each complete propagation path is obtained respectively.

[0037] Calculate the response time difference between each complete propagation path. If the response time difference is greater than the preset time threshold, the complete propagation path with the earliest response time is automatically selected.

[0038] If the response time difference is less than or equal to the preset time threshold, a candidate list of multiple complete propagation paths is pushed to the request initiator, and the request initiator selects a complete propagation path from the candidate list.

[0039] Optionally, following the complete propagation path from the request initiator to the response node, the hash value of each node is recalculated sequentially and compared with the hash value sequence. If all match, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated. After this step, the process includes:

[0040] The referral level of each node is determined based on the propagation distance between each node in the effective referral chain (excluding the request initiator and the response node) and the response node.

[0041] According to the preset number of profit distribution levels, starting from the response node, contribution weights are distributed level by level along the effective referral chain towards the demand initiator; wherein, the closer the propagation distance, the higher the contribution weight is obtained, and the contribution weights of all nodes at the same referral level are the same.

[0042] The profit distribution ratio for each node is generated based on the contribution weight of each node.

[0043] A demand propagation referral chain tracking device provided for another purpose of this application includes:

[0044] The requirement initiation module is configured to generate a target requirement data package in response to a requirement release instruction triggered by the requirement initiator. The target requirement data package includes at least a requirement identifier, the identity identifier of the requirement initiator, and a root hash value generated based on the requirement identifier and the identity identifier.

[0045] The propagation control module is configured to invoke a preset social relationship graph, starting from the node corresponding to the request initiator, and push the target request data packet outward level by level along the node hierarchy in the social relationship graph. Each time the target request data packet is pushed through a node hierarchy, the current node, after receiving the data packet from the previous level, appends the identity identifier and timestamp of the current node to the data packet from the previous level, and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet, until the preset propagation depth threshold is reached or a response node is found and then the pushing stops.

[0046] The response collection module is configured to receive response requests sent by any response node. The response request contains referral data of the complete propagation path from the request initiator to the response node. The referral data includes the identity identifier, timestamp, and corresponding hash value sequence of each node at each level of the complete propagation path.

[0047] The verification and evidence storage module is configured to recalculate the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and compare it with the hash value sequence. If all are consistent, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

[0048] An electronic device provided for another purpose of this application includes a central processing unit and a memory, the central processing unit being configured to invoke and run a computer program stored in the memory to perform the steps of the demand propagation referral chain tracing method described in this application.

[0049] A computer-readable storage medium is provided for another purpose of this application, which stores, in the form of computer-readable instructions, a computer program implementing a referral chain tracing method according to the stated requirements, which, when invoked by a computer, executes the steps included in the corresponding method.

[0050] Compared to existing technologies, this application addresses the problems of demand information relying on manual forwarding in social networks, which leads to untraceable propagation paths, lack of tamper-proof protection and trusted evidence storage in referral chains, easy leakage of identity information, and single point of failure risks in centralized storage. This application includes, but is not limited to, the following beneficial effects:

[0051] Firstly, this application effectively solves the technical problems of untraceable paths, easily tampered referral chains, and unverifiable attributions in existing social communication methods. During demand propagation, each forwarding node appends its own identity identifier and timestamp to the data packet of the previous level, and performs a hash operation on the hash value of the previous level's data packet and the appended data to generate the hash value of the current level's data packet, forming a complete chain structure from the root hash to the final hash. During subsequent verification, the system recalculates the hash values ​​of each node sequentially along the complete propagation path and compares them with the hash value sequence. Tampering with the data of any level node will cause all subsequent hash values ​​to change, and the expected hash value recalculated during verification will be inconsistent with the stored record, thus accurately locating the tampering behavior. Through the cryptographic binding of chain hashes and the generation of evidence hashes, the authenticity and integrity of the referral chain are ensured, providing irrefutable evidence support for profit-sharing incentives.

[0052] Secondly, this application records the complete propagation path of a request from the initiator to the responding node by attaching its own identity and timestamp to each node during forwarding and generating hash values ​​level by level. The referral data included in the response request carries the identity, timestamp, and corresponding hash value sequence of each level of node, enabling the original publisher to accurately track each intermediate node and the final responder through which the information passes, and to identify and attribute all key propagators in the referral path.

[0053] Third, during the propagation process, the identity information of intermediate nodes is encapsulated in data packets and propagated along the path, visible only to necessary parties (such as the request initiator or platform administrator). It supports the desensitization display of the identity information of non-essential nodes, ensuring the traceability of the propagation path while meeting the requirements of personal information protection regulations.

[0054] Fourth, after verification, this application generates a final referral chain hash value for evidence storage. This hash value can be written into the blockchain or tamper-proof storage medium, avoiding the single point of failure and the risk of malicious tampering of centralized storage, and ensuring that the referral record has long-term credibility and legal evidentiary effect.

[0055] Furthermore, by recording and verifying the complete propagation path, this application can accurately determine the propagation distance between each intermediate node and the response node, thereby objectively quantifying the referral contribution of each node, avoiding a "one-size-fits-all" allocation, and providing a reasonable basis for fair profit sharing. Attached Figure Description

[0056] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0057] Figure 1 This is a flowchart illustrating the demand propagation referral chain tracing method in the embodiments of this application;

[0058] Figure 2 This is a schematic diagram illustrating the process of constructing a social relationship graph in an embodiment of this application;

[0059] Figure 3 A schematic diagram illustrating the storage structure definition of the social relationship graph in this application embodiment;

[0060] Figure 4 This is a schematic diagram illustrating the process of generating the hash value of the current-level data packet in an embodiment of this application;

[0061] Figure 5 This is a flowchart illustrating the selection of a complete propagation path in an embodiment of this application;

[0062] Figure 6 This is a schematic diagram illustrating the process of generating referral data for a complete propagation path from the request initiator to the response node in this embodiment of the application.

[0063] Figure 7 This is a flowchart illustrating the process of confirming that the complete propagation path is a valid referral chain in the embodiments of this application;

[0064] Figure 8 This is a schematic diagram of the process for generating the profit distribution ratio of each node in an embodiment of this application;

[0065] Figure 9This is a schematic diagram of the demand propagation referral chain tracing device in the embodiments of this application;

[0066] Figure 10 This is a schematic diagram of the structure of the computer device in the embodiments of this application. Detailed Implementation

[0067] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0068] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0069] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0070] Those skilled in the art will understand that the terms "client," "terminal," and "terminal device" as used herein include both devices that receive wireless signals, devices that only possess wireless signal receiver capabilities without transmission capabilities, and devices with receiving and transmitting hardware, devices that have receiving and transmitting hardware capable of bidirectional communication over a bidirectional communication link. Such devices may include: cellular or other communication devices such as personal computers or tablets, having single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service) that can combine voice, data processing, fax, and / or data communication capabilities; PDAs (Personal Digital Assistants) that may include radio frequency receivers, pagers, internet / intranet access, web browsers, notebooks, calendars, and / or GPS (Global Positioning System) receivers; and conventional laptops and / or handheld computers or other devices that have and / or include radio frequency receivers. As used herein, "client," "terminal," and "terminal device" can be portable, transportable, installed in a means of transportation (air, sea, and / or land), or suitable and / or configured to operate locally and / or in a distributed manner, operating in any other location on Earth and / or in space. "Client," "terminal," and "terminal device" as used herein can also be a communication terminal, an internet access terminal, or a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or a smart TV, set-top box, etc.

[0071] The hardware referred to by the names "server," "client," and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann architecture, such as a central processing unit (including an arithmetic logic unit and a control unit), memory, input devices, and output devices. The computer program is stored in its memory, and the central processing unit loads the program stored in the secondary storage into the main memory to run it, execute the instructions in the program, and interact with the input and output devices to complete specific functions.

[0072] It should be noted that the concept of "server" used in this application can also be extended to the case of server clusters. Based on the network deployment principles understood by those skilled in the art, the servers should be logically divided. Physically, these servers can be independent of each other but accessible through interfaces, or they can be integrated into a single physical computer or a computer cluster. Those skilled in the art should understand this flexibility and should not use it to constrain the implementation of the network deployment method in this application.

[0073] One or more of the technical features of this application, unless explicitly specified herein, can be deployed on a server and accessed by a client remotely calling the online service interface provided by the server, or can be directly deployed and run on a client for access.

[0074] Unless otherwise specified, the neural network models referenced or potentially referenced in this application may be deployed on a remote server and invoked remotely on the client, or deployed on a client with the capability to invoke directly. In some embodiments, when running on the client, the corresponding intelligence may be acquired through transfer learning in order to reduce the requirements on the client's hardware resources and avoid excessive consumption of the client's hardware resources.

[0075] Unless otherwise specified, all data involved in this application may be stored remotely on a server or on a local terminal device, as long as it is suitable for use by the technical solution of this application.

[0076] Those skilled in the art will understand that although the various methods in this application are described based on the same concept and thus present commonality among them, they can be performed independently unless otherwise specified. Similarly, the various embodiments disclosed in this application are all based on the same inventive concept; therefore, concepts expressed in the same way, as well as concepts that are appropriately changed for convenience but are expressed differently, should be understood equivalently.

[0077] Unless otherwise expressly stated, the various embodiments disclosed in this application can be combined in a cross-cutting manner to flexibly construct new embodiments, as long as such combination does not depart from the inventive spirit of this application and can meet the needs of the prior art or solve a certain deficiency in the prior art. Those skilled in the art should be aware of such modifications.

[0078] Please see Figure 1 The demand propagation referral chain tracing method of this application, in one embodiment, includes:

[0079] Step S10: In response to the demand release instruction triggered by the demand initiator, generate a target demand data packet, wherein the target demand data packet includes at least a demand identifier, the identity identifier of the demand initiator, and a root hash value generated based on the demand identifier and the identity identifier;

[0080] The demand propagation referral chain tracking system in the terminal device can respond to the demand release instruction triggered by the demand initiator to generate a target demand data packet, wherein the target demand data packet includes at least a demand identifier, the identity identifier of the demand initiator, and a root hash value generated based on the demand identifier and the identity identifier.

[0081] Specifically, the request initiator submits the request content to the server through the client and selects to enable the "social dissemination" function to trigger the request release instruction; after receiving the request release instruction, the server starts the process of generating the target request data package.

[0082] The target requirement data packet contains at least a requirement identifier. The identity identifier of the request initiator and the root hash value generated based on the demand identifier and the identity identifier. The requirement identifier is used to uniquely identify the requirement in the system, ensuring that each subsequent stage of dissemination, response, and verification can be associated with the correct requirement; the identity identifier of the requirement initiator is used to record the identity of the initiator, ensuring that the starting point of the dissemination path is clear and cannot be forged; the root hash value is generated by cryptographic hashing based on the requirement identifier, identity identifier, and system timestamp at the time of requirement publication.

[0083] In some embodiments, the root hash value is generated as follows: the identity identifier of the request initiator, the request identifier, and the request publication timestamp are sequentially concatenated into a data block, and the data block is transformed unidirectionally using the SHA256 cryptographic hash function to generate a fixed-length root hash value. This root hash value serves as the trust anchor point of the entire propagation chain, cryptographically binding the request and the initiator's identity into an inseparable whole; in subsequent propagation processes, the hash value of each node extends sequentially from this root hash value, making the starting point of the entire propagation path verifiable and unforgeable.

[0084] In a specific embodiment, the root hash value The expression is:

[0085]

[0086] in, Indicates the demand identifier; This indicates the identity of the party initiating the request; Indicates the timestamp of the request posting; Represents a cryptographic hash function. This represents the root hash value generated based on the identity of the request initiator, the request identifier, and the request publication timestamp.

[0087] Step S20: Invoke the preset social relationship graph, starting from the node corresponding to the request initiator, push the target request data packet outward level by level along the node hierarchy in the social relationship graph. Each time the target request data packet is pushed through a node hierarchy, the current node, after receiving the data packet from the previous level, appends the identity identifier and timestamp of the current node to the data packet from the previous level, and performs a hash operation on the hash value of the data packet from the previous level and the appended data to generate the hash value of the current level data packet, until the preset propagation depth threshold is reached or a response node is found and the push stops.

[0088] In response to a demand release command triggered by the demand initiator, after generating a target demand data packet, a preset social relationship graph is invoked. Starting from the node corresponding to the demand initiator, the target demand data packet is pushed outward level by level along the node hierarchy of the social relationship graph. Each time the target demand data packet is pushed through a node hierarchy, the current node, upon receiving the data packet from the previous level, appends the current node's identity identifier and timestamp to the previous level data packet, and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet. Pushing stops when a preset propagation depth threshold is reached or a response node is found.

[0089] In some embodiments, please refer to Figure 2 The steps to construct a social relationship graph include:

[0090] Step S201: Create a node table for storing user nodes and an edge table for storing relationship edges, wherein the user node includes a user identity identifier and a user type, and the relationship edge includes a relationship starter identifier, a relationship end identifier, a relationship type, a relationship weight, and a relationship source;

[0091] Please see Figure 3The node table stores all user nodes in the social graph. Each user node contains at least a user identifier (user_id) and a user type (node_type), and can be expanded to store auxiliary information such as user attribute tags (attributes) and creation time (created_at). The edge table stores social relationship edges between users. Each relationship edge contains at least a relationship starter identifier (from_user_id), a relationship end identifier (to_user_id), a relationship type (relation_type), a relationship weight (weight), and a relationship source (source).

[0092] Step S202: Configure the relationship type, wherein the relationship type includes direct friend relationship, same organization relationship, follow relationship, mutual assistance relationship and interest group relationship; the direct friend relationship requires confirmation from both parties, and the same organization relationship and the follow relationship are one-way relationships;

[0093] The direct friend relationship refers to a friend relationship actively added and established by the user; the same organization relationship refers to a relationship automatically established after the user joins the same organization (such as an alumni association or chamber of commerce); the following relationship refers to a relationship formed by the user unilaterally following other users; the mutual assistance relationship refers to a relationship automatically established by the system when users have had a record of responding to requests or collaborating; the interest group relationship refers to a relationship automatically established by the system between users who have repeatedly posted or responded to similar requests.

[0094] The direct friend relationship requires confirmation from both parties to be effective, ensuring the mutual consent required to establish the relationship. The same organization relationship and the following relationship are one-way relationships. The same organization relationship is automatically established based on the shared organizational membership, and the following relationship is formed based on the user's following behavior. Both are one-way relationships and do not require confirmation from the other party.

[0095] Step S203: Assign different initial weights to relation edges of different relation types, and mark relation edges with weights not lower than a first preset threshold as strong relation edges, and mark relation edges with weights lower than the first preset threshold as weak relation edges; wherein, the target demand data packet is pushed preferentially along the strong relation edges;

[0096] Different types of social relationships have varying degrees of trust and influence on the dissemination of needs. The system assigns different initial weights to each relationship type. Direct friend relationships are given the highest weight due to their high level of trust, followed by organizational relationships, while follower relationships have relatively lower weights. For example, the initial weight for direct friend relationships can be 0.8, for organizational relationships 0.6, for follower relationships 0.3, for mutual assistance relationships 0.7, and for interest group relationships 0.4.

[0097] The system marks relationship edges with a weight not lower than a first preset threshold as strong relationship edges, and relationship edges with a weight lower than the first preset threshold as weak relationship edges. This hierarchical mechanism is used to guide the push priority in the subsequent propagation process. During demand propagation, the system prioritizes pushing target demand data packets along strong relationship edges to improve the probability of the demand being effectively responded to and the propagation efficiency. The first preset threshold can be 0.6, etc., and those skilled in the art can determine the first preset threshold as needed according to the actual application scenario; no limitations are made here.

[0098] Specifically, in traditional social needs-posting scenarios (such as alumni groups and chamber of commerce groups), after a user posts a request for help, the information is often pushed to everyone in a "broadcast" manner. The drawback of this approach is obvious: most people in the group are not familiar with the poster, neither care about nor have the ability to respond to the request, and the information is quickly buried, while those who truly have resources and willingness may miss it. Step S203 solves this problem by layering based on strong and weak relationships. For example, in the scenario of cross-border e-commerce expert referrals, the system will prioritize pushing the request to the requester's direct friends and people with a history of mutual assistance, then to members of the same organization, and finally to followers. This means that the requester's old friends and those who have helped them in the past will receive the request first. These people have a higher level of trust with the requester and are more willing to lend a hand, greatly increasing the probability that the request will be effectively responded to. At the same time, users with low relevance will not be overly disturbed, avoiding the information harassment and decreased user experience of the traditional mass messaging model.

[0099] Step S204: When any relationship edge does not interact for more than a preset time threshold, the weight of the relationship edge is gradually reduced according to a preset decay rate, and the relationship edge is automatically removed when the weight is lower than a second preset threshold, so as to construct a social relationship graph.

[0100] Social relationships are time-sensitive; weak relationships with no interaction over a long period gradually lose value for disseminating needs. Therefore, the system implements a dynamic decay mechanism. The system monitors the last interaction time of each relationship edge, and triggers the decay process when any relationship edge has not interacted for more than a preset time threshold (e.g., 90 days or 180 days). This preset time threshold can be configured according to the platform's operational strategy.

[0101] After the decay is triggered, the weight of the relationship edge decreases gradually according to a preset decay rate (e.g., 5% or 7%), progressively reducing its priority and influence in the propagation. When the weight of the relationship edge continues to decay to below a second preset threshold (e.g., 0.1 or 0.2), the system automatically removes the relationship edge from the social relationship graph. At this point, the relationship is considered invalid and will no longer participate in subsequent propagation.

[0102] Specifically, a person's social relationships are dynamic and constantly changing. Friends met five years ago in a chamber of commerce may no longer be in contact; former classmates may have changed industries after graduation. If the social graph retains a large number of such outdated relationships, the system will include these ineffective relationships in its push notifications, leading to wasted resources and decreased efficiency. Suppose a client posts a request in May 2026. The system will automatically check the interaction history of each relationship edge over the past 180 days. If a relationship edge has had no interaction for more than 180 days (no chatting, no mutual assistance, no joint activities, etc.), its weight will decrease by 5% monthly, and it will be automatically removed when it falls below 0.1. This means the system will automatically remove ineffective social relationships, retaining only currently active and effective relationships for request dissemination. For example, if you met Mr. Zhang in a chamber of commerce five years ago and haven't interacted with him in the past two years, the relationship will be automatically downgraded or even removed. The system will no longer push requests to Mr. Zhang based on this relationship, avoiding the awkwardness and inefficiency of pushing requests to "strangers."

[0103] Step S205: Perform a full consistency check on the social relationship graph every preset period to repair abnormal edges.

[0104] The system scans the entire social relationship graph at preset intervals (e.g., every 24 or 48 hours) to check for abnormal edges. Abnormal edges include, but are not limited to: edges that still exist even after a user has logged out, duplicate edges, isolated edges pointing to non-existent nodes, and edges with abnormal types or weight values. Upon detecting abnormal edges, the system automatically performs repair operations, such as removing invalid edges, merging duplicate edges, and updating inconsistent edge attributes, to ensure the data quality and logical consistency of the graph.

[0105] As described in steps S201 to S205 above, the relationship type and strength of each node are determined through a social relationship graph. Then, the contribution weight is calculated based on the propagation distance. Referrers closer to the solution provider receive a higher contribution weight, while those farther away receive a lower weight. In real-world scenarios, due to the different types of social relationships between different referrers and the requester, their trust levels also differ. The trust level between direct friends is usually higher than that between members of the same organization or ordinary followers. The system can further fine-tune the weight allocation based on propagation distance by adding a relationship strength coefficient, ensuring that profit sharing considers both propagation distance and social trust. This allows for a more refined and fair quantitative assessment of the contribution of each node in the referral chain.

[0106] For further embodiments, please refer to Figure 4 The step of performing a hash operation on the hash value of the previous-level data packet and the appended data to generate the hash value of the current-level data packet includes:

[0107] Step S2001: Obtain the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp;

[0108] The hash value of the previous-level data packet represents the latest hash value carried in the data packet received by the current node. For a first-degree friend that received the initial data packet, this latest hash value is the root hash value. For nodes at subsequent levels, this latest hash value is the hash value generated by the node at the previous level. As the input basis for chained hashing, it ensures that the current level hash value establishes a cryptographic dependency with the previous level hash value.

[0109] The identity identifier of the current node It serves as a unique identifier for the current forwarding node, used to record "who performed this forwarding" in the propagation path, enabling the referral chain to trace the specific propagator at each level.

[0110] The current timestamp This represents the system time when the current node performs the forwarding operation. It is used to record "when this forwarding was performed" to ensure that the time sequence of each node in the propagation path can be verified.

[0111] Step S2002: Concatenate the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp in sequence to construct a data block;

[0112] The hash value of the previous level data packet The identity identifier of the current node and current timestamp By concatenating them in sequence, it is ensured that the same nodes can generate the same hash value when forwarding at the same time, which facilitates subsequent verification.

[0113] Step S2003: Perform a hash operation on the data block using a preset encryption algorithm to generate the hash value of the current level data packet;

[0114] The encryption algorithm can be SHA256, etc. The system uses a preset SHA256 algorithm to perform a one-way hash operation on the data block to generate the hash value of the current-level data packet. The hash value of the current level data packet The latest hash value in the data packet continues to propagate to lower-level nodes as the data packet continues to propagate. The SHA256 algorithm transforms input data of arbitrary length into a fixed-length hash value output and satisfies the following cryptographic properties: any small change in the input data will cause an unpredictable large change in the output hash value, and it is impossible to deduce the original input data from the hash value.

[0115] Step S2004: Associate the hash value of the current level data packet with the identity identifier of the current node and the current timestamp and store it in the propagation record.

[0116] The system will use the hash value of the current level data packet The identity identifier of the current node and current timestamp The three are stored together as a single propagation record.

[0117] During propagation, the server records the forwarding behavior of each node, but to avoid over-pushing, the same request is only pushed to the same user once. When the propagation depth reaches a propagation depth threshold... At this time, forwarding will stop, but users can still respond.

[0118] Step S30: Receive a response request sent by any response node, wherein the response request contains referral data of the complete propagation path from the request initiator to the response node, and the referral data contains the identity identifier, timestamp and corresponding hash value sequence of each level node in the complete propagation path;

[0119] The system invokes a preset social relationship graph, starting from the node corresponding to the request initiator, and pushes the target request data packet outwards level by level along the node hierarchy of the social relationship graph. Each time the target request data packet passes through a node level, the current node, upon receiving the data packet from the previous level, appends its own identity identifier and timestamp to the previous level data packet, and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet. Pushing stops when a preset propagation depth threshold is reached or a response node is found. Then, the system receives a response request from any response node. The response request contains referral data for the complete propagation path from the request initiator to the response node. This referral data includes the identity identifier, timestamp, and corresponding hash value sequence of each node at each level of the complete propagation path. The response node represents the request solver.

[0120] In some embodiments, please refer to Figure 5 The steps for receiving a response request from any response node include:

[0121] Step S301: When multiple complete propagation paths point to the same response node, obtain the response time corresponding to each complete propagation path;

[0122] The system detected multiple complete propagation paths with the same end-response node. This conflict scenario indicates that the same solution is simultaneously recommended to the request initiator through multiple different referral paths, and the system needs to select one as the final valid referral chain. The system obtains the response time corresponding to each candidate path. The response time refers to the timestamp of the end-response node of the candidate path sending a "able to resolve the request" signal to the server, used to measure the order of responses from each path. Each propagation path contains a complete node sequence from the request initiator to the response node and a corresponding hash value sequence. The system distinguishes different candidate paths by path identifiers (such as path ID or final response time).

[0123] Step S302: Calculate the response time difference between each complete propagation path. If the response time difference is greater than the preset time threshold, the complete propagation path with the earliest response time is automatically selected.

[0124] The preset time threshold can be 5 minutes or 10 minutes, etc.; the system calculates the response time difference between each candidate path. This response time difference reflects the time interval between different referral chains recommending the same solution to the request initiator. The system compares the calculated response time difference with a preset time threshold. If the response time difference... If the time exceeds 5 minutes, the complete propagation path with the earliest response time will be automatically selected.

[0125] Step S303: If the response time difference is less than or equal to the preset time threshold, then push a candidate list of multiple complete propagation paths to the request initiator, and receive a complete propagation path selected by the request initiator from the candidate list.

[0126] If the response time difference If the time interval is less than or equal to 5 minutes, a candidate list of multiple complete propagation paths will be pushed to the request initiator, and the request initiator will receive one complete propagation path selected from the candidate list. The request initiator can view the complete referral chain information of each candidate path on the client and select one path based on their own judgment.

[0127] In some embodiments, please refer to Figure 6 The steps for generating referral data that forms the complete propagation path from the request initiator to the response node include:

[0128] Step S3001: Using the identity identifier of the request initiator, the request identifier, and the request publication timestamp as input, generate a root hash value, and associate the root hash value with the identity identifier of the request initiator and the request identifier and store it as the starting record of the propagation path;

[0129] The system obtains the identity identifier of the request initiator. Demand Identifier and the timestamp of the request posting The three data points are concatenated in sequence into a data block, and the SHA256 algorithm is used to perform a hash operation to generate the root hash value. .

[0130] The system will use the root hash value Identification of the request initiator Demand Identifier The associated storage serves as the starting record for the propagation path. This starting record acts as the trust anchor for the entire referral chain. The hash value of each subsequent node extends from this root hash value level by level. Any tampering with the identity of the request initiator or the content of the request will cause the root hash value to change, thus making the starting point of the entire propagation chain verifiable and unforgeable.

[0131] Step S3002: When any intermediate node forwards the target demand data packet along the social relationship graph, it obtains the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp. It concatenates the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp into a data block in sequence, performs a hash operation on the data block to generate the hash value of the current level data packet, and associates the hash value of the current level data packet with the identity identifier of the current node and the current timestamp to store it as the current level propagation record.

[0132] When any intermediate node chooses to continue forwarding data packets along the social relationship graph, it obtains the hash value of the data packet from the previous level. The identity identifier of the current node and current timestamp The hash value of the previous level data packet The identity identifier of the current node and current timestamp The data is sequentially concatenated into data blocks, and a hash operation is performed on the data blocks to generate the hash value of the current-level data packet. The hash value of the current level data packet Identity identifier of the current node and current timestamp The associated storage is the current level propagation record.

[0133] Step S3003: Iterate sequentially until the target requirement data packet is pushed to the response node, and the response node receives a complete hash value sequence containing the root hash value, the hash values ​​of data packets at each level, and the identity identifiers and timestamps of nodes at each level.

[0134] The process is repeated at each node along the propagation path, with a hash value generated after execution at the first-level node. Second-level nodes are based on hash values A hash value is generated after execution. And so on, forming a root hash value. To the final hash value The complete chain structure. Iteration continues until the data packet is pushed along the propagation path to the response node or reaches the preset propagation depth threshold. When the data packet is pushed to the response node, the data packet received by the response node contains the root hash value. To the final hash value And the complete hash sequence of the identity identifiers and timestamps of each level of nodes.

[0135] Step S40: Along the complete propagation path, from the request initiator to the response node, recalculate the hash value of each node in sequence and compare it with the hash value sequence. If they are all consistent, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

[0136] After receiving a response request from any response node, along the complete propagation path from the request initiator to the response node, the hash value of each node is recalculated sequentially and compared with the hash value sequence. If all are consistent, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

[0137] In some embodiments, please refer to Figure 7 The step of recalculating the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and comparing it with the hash value sequence, and confirming that the complete propagation path is a valid referral chain if all hash values ​​match, includes:

[0138] Step S401: Using the root hash value as the current hash value, along the complete propagation path, according to the node order from the request initiator to the response node, read the identity identifier and corresponding storage timestamp of each node in sequence, and perform a hash operation on the current hash value, the identity identifier of the current node and the storage timestamp of the current node in sequence to calculate the expected hash value of the node.

[0139] Step S402: Compare the expected hash value with the hash value corresponding to the node in the referral data. If the expected hash value is consistent with the hash value corresponding to the node in the referral data, then use the expected hash value as the current hash value of the next node and continue to verify the next node; if they are inconsistent, then the verification is determined to be unsuccessful.

[0140] Step S403: If the expected hash values ​​of all nodes on the complete propagation path are consistent with the corresponding hash values ​​in the referral data, and the final hash value obtained is equal to the final hash value submitted by the response node, then the complete propagation path is confirmed as a valid referral chain.

[0141] As can be seen from steps S401 to S403 above, by independently recalculating the expected hash value and comparing the recorded value for each node in the propagation path, any tampering behavior targeting the node's identity, timestamp, or hash data is precisely locked based on the chain dependency relationship. Combined with the dual verification conditions of node-by-node consistency and the equality of the final hash value, a complete verification closed loop is formed from the root hash starting point to the response node ending point, ensuring that only the propagation path that has not been tampered with, has a continuous chain, and has a correct ending point can pass the verification. At the same time, the verification process adopts the same hash operation rules as the forward propagation, making the forward generation and reverse verification cryptographic inverse processes, ensuring the objectivity and reproducibility of the verification results, and providing an unforgeable and independently verifiable credible data foundation for subsequent contribution quantification and profit-sharing incentives.

[0142] For further embodiments, please refer to Figure 8 Following the steps of recalculating the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and comparing it with the hash value sequence, and confirming that the complete propagation path is a valid referral chain after generating the final referral chain hash value for evidence storage, the process includes:

[0143] Step S4001: Determine the referral level of each node based on the propagation distance between each node in the effective referral chain (excluding the request initiator and the response node) and the response node.

[0144] The system excludes request initiators and response nodes from the valid referral chain, retaining only intermediate propagation nodes as revenue-sharing recipients. Request initiators are those who propose the request and do not participate in referral revenue sharing; response nodes are those who provide solutions and receive service compensation, not referral rewards.

[0145] The system calculates the propagation distance (i.e., hop count) between each intermediate node and the response node. The propagation distance refers to the number of edges required to reach the response node from the intermediate node along the referral chain. The node that is 1 hop away from the response node is the first-level referrer who directly recommends the solution, the node that is 2 hops away is the second-level referrer who recommended the referrer, and so on.

[0146] The system assigns each intermediate node to its corresponding referral level based on the propagation distance. Nodes with a propagation distance of 1 hop are identified as first-level referrers, nodes with a propagation distance of 2 hops are identified as second-level referrers, and nodes with a propagation distance of 3 hops or more are identified as third-level or higher referrers.

[0147] Step S4002: According to the preset number of profit distribution levels, starting from the response node, the contribution weight is distributed level by level along the effective referral chain towards the demand initiator; wherein, the closer the propagation distance, the higher the contribution weight is obtained, and the contribution weight of each node in the same referral level is the same.

[0148] Starting from the response node, the system allocates contribution weights level by level along the effective referral chain towards the request initiator. This allocation direction is consistent with the hierarchical determination direction in step S4001; counting backwards from the response node, the closer the node is to the response node, the higher its level and the greater its weight.

[0149] The system allocates weights based on the core principle that nodes closer to the propagation distance receive higher contribution weights. First-level referrers (1 hop from the response node) receive the highest weight W1, second-level referrers (2 hops from the response node) receive the second highest weight W2, and third-level and above referrers receive lower weights W3. This allocation principle aligns with the propagation logic of the referral chain; nodes closer to the solution provider contribute more directly to the solution and should receive a higher share of the profits.

[0150] The system allocates profits according to a preset number of levels. There can be up to three levels, with W1=70%, W2=30%, and W3=0%. The number of levels can also be expanded to four, five, or even six. When expanding the number of levels, the system automatically redistributes the weights of each level in a decreasing sequence (e.g., four-level profit sharing: 60%, 25%, 10%, 5%). The propagation depth and the number of profit sharing levels are decoupled; regardless of how deep the propagation path extends, the profit sharing range can be configured independently without affecting each other.

[0151] Nodes at the same referral level have the same contribution weight. In a single-chain scenario, each level has only one node, which exclusively enjoys the full weight of that level. In a multi-branch scenario, if there are multiple nodes at the same level, each node is assigned the same weight coefficient.

[0152] Step S4003: Generate the profit distribution ratio for each node based on the contribution weight of each node.

[0153] The system calculates the actual profit-sharing ratio for each intermediate node based on the contribution weights of each node determined in step S4002. The node allocation ratio is equal to the contribution weight of its level, or equal to the contribution weight of its level divided by the total number of nodes at that level; the sum of the contribution weights of all levels is 100%, ensuring that the profit-sharing pool is fully allocated to each referring node, without over-allocation or surplus. The system outputs the profit-sharing ratio for each node, serving as the basis for distributing referral rewards to each referring node during the profit-sharing execution phase.

[0154] Please see Figure 9 This application provides a demand propagation referral chain tracking device, comprising a demand initiation module 1100, a propagation control module 1200, a response collection module 1300, and a verification and storage module 1400. The demand initiation module 1100 is configured to generate a target demand data packet in response to a demand release command triggered by a demand initiator. The target demand data packet includes at least a demand identifier, the identity identifier of the demand initiator, and a root hash value generated based on the demand identifier and the identity identifier. The propagation control module 1200 is configured to invoke a preset social relationship graph, starting from the node corresponding to the demand initiator, and push the target demand data packet outwards level by level along the node hierarchy of the social relationship graph. Each time the target demand data packet is pushed through a node hierarchy, the current node, upon receiving the data packet from the previous level, appends its own identity identifier and timestamp to the previous level data packet, and performs a hash operation on the hash value of the previous level data packet and the appended data. The system generates a hash value for the current level data packet until a preset propagation depth threshold is reached or a response node is found, at which point the push stops. A response collection module 1300 is configured to receive a response request sent by any response node. The response request contains referral data for the complete propagation path from the request initiator to the response node. The referral data includes the identity identifier, timestamp, and corresponding hash value sequence of each node at each level along the complete propagation path. A verification and evidence storage module 1400 is configured to recalculate the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and compare it with the hash value sequence. If all hash values ​​match, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value is generated for evidence storage, thus completing the referral chain tracing for request propagation.

[0155] As can be seen from the above embodiments, compared with the prior art, this application addresses the problems of demand information relying on manual forwarding in social networks, resulting in untraceable propagation paths, lack of anti-tampering protection and trusted evidence storage in referral chains, easy leakage of identity information, and single point of failure risk in centralized storage. This application has, but is not limited to, the following beneficial effects:

[0156] Firstly, this application effectively solves the technical problems of untraceable paths, easily tampered referral chains, and unverifiable attributions in existing social communication methods. During demand propagation, each forwarding node appends its own identity identifier and timestamp to the data packet of the previous level, and performs a hash operation on the hash value of the previous level's data packet and the appended data to generate the hash value of the current level's data packet, forming a complete chain structure from the root hash to the final hash. During subsequent verification, the system recalculates the hash values ​​of each node sequentially along the complete propagation path and compares them with the hash value sequence. Tampering with the data of any level node will cause all subsequent hash values ​​to change, and the expected hash value recalculated during verification will be inconsistent with the stored record, thus accurately locating the tampering behavior. Through the cryptographic binding of chain hashes and the generation of evidence hashes, the authenticity and integrity of the referral chain are ensured, providing irrefutable evidence support for profit-sharing incentives.

[0157] Secondly, this application records the complete propagation path of a request from the initiator to the responding node by attaching its own identity and timestamp to each node during forwarding and generating hash values ​​level by level. The referral data included in the response request carries the identity, timestamp, and corresponding hash value sequence of each level of node, enabling the original publisher to accurately track each intermediate node and the final responder through which the information passes, and to identify and attribute all key propagators in the referral path.

[0158] Third, during the propagation process, the identity information of intermediate nodes is encapsulated in data packets and propagated along the path, visible only to necessary parties (such as the request initiator or platform administrator). It supports the desensitization display of the identity information of non-essential nodes, ensuring the traceability of the propagation path while meeting the requirements of personal information protection regulations.

[0159] Fourth, after verification, this application generates a final referral chain hash value for evidence storage. This hash value can be written into the blockchain or tamper-proof storage medium, avoiding the single point of failure and the risk of malicious tampering of centralized storage, and ensuring that the referral record has long-term credibility and legal evidentiary effect.

[0160] Furthermore, by recording and verifying the complete propagation path, this application can accurately determine the propagation distance between each intermediate node and the response node, thereby objectively quantifying the referral contribution of each node, avoiding a "one-size-fits-all" allocation, and providing a reasonable basis for fair profit sharing.

[0161] Based on any embodiment of this application, please refer to Figure 10 Another embodiment of this application also provides an electronic device, which can be implemented by a computer device, such as... Figure 10 The diagram shows the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. The computer-readable storage medium stores an operating system, a database, and computer-readable instructions. The database may store control information sequences. When the computer-readable instructions are executed by the processor, the processor can implement a demand propagation referral chain tracing method. The processor of the computer device provides computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the demand propagation referral chain tracing method of this application. The network interface of the computer device is used for communication with a terminal. Those skilled in the art will understand that… Figure 10 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0162] In this embodiment, the processor is used to execute... Figure 9 The specific functions of each module are defined within the memory, which stores the program code and various data required to execute these modules. The network interface is used for data transmission between the user terminal and the server. In this embodiment, the memory stores the program code and data required to execute all modules in the requirement propagation referral chain tracing device of this application. The server can call the server's program code and data to execute the functions of all modules.

[0163] This application also provides a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the steps of the demand propagation referral chain tracing method described in any embodiment of this application.

[0164] This application also provides a computer program product, including a computer program / instructions that, when executed by one or more processors, implement the steps of the demand propagation referral chain tracing method described in any embodiment of this application.

[0165] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. This computer program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0166] The above description is only a partial embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for tracing a referral chain in demand propagation, characterized in that, include: In response to a demand release instruction triggered by a demand initiator, a target demand data packet is generated, wherein the target demand data packet includes at least a demand identifier, an identity identifier of the demand initiator, and a root hash value generated based on the demand identifier and the identity identifier; The system invokes a preset social relationship graph and, starting from the node corresponding to the request initiator, pushes the target request data packet outwards level by level along the node hierarchy of the social relationship graph. Each time the target request data packet is pushed through a node hierarchy, the current node, upon receiving the data packet from the previous level, appends the current node's identity identifier and timestamp to the previous level data packet and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet. The pushing stops when a preset propagation depth threshold is reached or a response node is found. Receive a response request sent by any response node, wherein the response request contains referral data of the complete propagation path from the request initiator to the response node, and the referral data contains the identity identifier, timestamp and corresponding hash value sequence of each level node in the complete propagation path; Along the complete propagation path, from the request initiator to the response node, the hash value of each node is recalculated sequentially and compared with the hash value sequence. If they all match, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

2. The demand propagation referral chain tracing method according to claim 1, characterized in that, The steps to construct a social relationship graph include: Create a node table for storing user nodes and an edge table for storing relationship edges. The user nodes include user identity identifiers and user types, and the relationship edges include relationship starter identifiers, relationship end identifiers, relationship types, relationship weights, and relationship sources. Configure the relationship types, which include direct friend relationships, same organization relationships, following relationships, mutual assistance relationships, and interest group relationships; direct friend relationships require confirmation from both parties, while same organization relationships and following relationships are one-way relationships; Different initial weights are assigned to relation edges of different relation types, and relation edges with weights not lower than a first preset threshold are marked as strong relation edges, while relation edges with weights lower than the first preset threshold are marked as weak relation edges; wherein, the target demand data packet is preferentially pushed along the strong relation edges; When any relationship edge does not interact for more than a preset time threshold, the weight of the relationship edge is gradually reduced according to a preset decay rate, and the relationship edge is automatically removed when the weight is lower than a second preset threshold, so as to construct a social relationship graph. A full consistency check is performed on the social relationship graph every preset period to repair abnormal edges.

3. The demand propagation referral chain tracing method according to claim 1, characterized in that, The step of performing a hash operation on the hash value of the previous-level data packet and the appended data to generate the hash value of the current-level data packet includes: Get the hash value of the previous level data packet, the identity of the current node, and the current timestamp; The hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp are concatenated in sequence to construct a data block; A preset encryption algorithm is used to perform a hash operation on the data block to generate the hash value of the current level data packet; The hash value of the current-level data packet is associated with the identity identifier of the current node and the current timestamp and stored in the propagation record.

4. The demand propagation referral chain tracing method according to claim 1, characterized in that, The steps for generating referral data for the complete propagation path from the request initiator to the response node include: Using the identity identifier of the request initiator, the request identifier, and the request publication timestamp as input, a root hash value is generated, and the root hash value is associated with the identity identifier of the request initiator and the request identifier and stored as the starting record of the propagation path; When any intermediate node forwards the target demand data packet along the social relationship graph, it obtains the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp. It then concatenates the hash value of the previous level data packet, the identity identifier of the current node, and the current timestamp into a data block in sequence, performs a hash operation on the data block to generate the hash value of the current level data packet, and associates the hash value of the current level data packet with the identity identifier of the current node and the current timestamp to store it as the current level propagation record. The process iterates sequentially until the target requirement data packet is pushed to the response node, whereby the response node receives a complete hash value sequence containing the root hash value, the hash values ​​of data packets at each level, and the identity identifiers and timestamps of nodes at each level.

5. The demand propagation referral chain tracing method according to claim 1, characterized in that, The step of recalculating the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and comparing it with the hash value sequence, and confirming that the complete propagation path is a valid referral chain if all hash values ​​match, includes: Using the root hash value as the current hash value, along the complete propagation path, according to the node order from the request initiator to the response node, the identity identifier and corresponding storage timestamp of each node are read sequentially, and the current hash value, the identity identifier of the current node and the storage timestamp of the current node are concatenated in order and then hashed to calculate the expected hash value of the node. The expected hash value is compared with the hash value corresponding to the node in the referral data. If the expected hash value matches the hash value corresponding to the node in the referral data, the expected hash value is used as the current hash value of the next node, and the verification of the next node continues. If they do not match, the verification is deemed to have failed. If the expected hash value of all nodes on the complete propagation path is consistent with the corresponding hash value in the referral data, and the final hash value obtained is equal to the final hash value submitted by the response node, then the complete propagation path is confirmed as a valid referral chain.

6. The demand propagation referral chain tracing method according to claim 1, characterized in that, The steps for receiving a response request from any response node include: When multiple complete propagation paths point to the same response node, the response time corresponding to each complete propagation path is obtained respectively. Calculate the response time difference between each complete propagation path. If the response time difference is greater than the preset time threshold, the complete propagation path with the earliest response time is automatically selected. If the response time difference is less than or equal to the preset time threshold, a candidate list of multiple complete propagation paths is pushed to the request initiator, and the request initiator selects a complete propagation path from the candidate list.

7. The demand propagation referral chain tracing method according to claim 6, characterized in that, Following the steps of recalculating the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and comparing it with the hash value sequence, and confirming that the complete propagation path is a valid referral chain after generating the final referral chain hash value for evidence storage, the process includes: The referral level of each node is determined based on the propagation distance between each node in the effective referral chain (excluding the request initiator and the response node) and the response node. According to the preset number of profit distribution levels, starting from the response node, contribution weights are distributed level by level along the effective referral chain towards the demand initiator; wherein, the closer the propagation distance, the higher the contribution weight is obtained, and the contribution weights of all nodes at the same referral level are the same. The profit distribution ratio for each node is generated based on the contribution weight of each node.

8. A demand propagation referral chain tracking device, characterized in that, include: The requirement initiation module is configured to generate a target requirement data package in response to a requirement release instruction triggered by the requirement initiator. The target requirement data package includes at least a requirement identifier, the identity identifier of the requirement initiator, and a root hash value generated based on the requirement identifier and the identity identifier. The propagation control module is configured to invoke a preset social relationship graph, starting from the node corresponding to the request initiator, and push the target request data packet outward level by level along the node hierarchy in the social relationship graph. Each time the target request data packet is pushed through a node hierarchy, the current node, after receiving the data packet from the previous level, appends the identity identifier and timestamp of the current node to the data packet from the previous level, and performs a hash operation on the hash value of the previous level data packet and the appended data to generate the hash value of the current level data packet, until the preset propagation depth threshold is reached or a response node is found and then the pushing stops. The response collection module is configured to receive response requests sent by any response node. The response request contains referral data of the complete propagation path from the request initiator to the response node. The referral data includes the identity identifier, timestamp, and corresponding hash value sequence of each node at each level of the complete propagation path. The verification and evidence storage module is configured to recalculate the hash value of each node sequentially along the complete propagation path from the request initiator to the response node and compare it with the hash value sequence. If all are consistent, the complete propagation path is confirmed as a valid referral chain, and a final referral chain hash value for evidence storage is generated to complete the referral chain tracing of the request propagation.

9. An electronic device comprising a central processing unit and a memory, characterized in that, The central processing unit is used to invoke and run a computer program stored in the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores, in the form of computer-readable instructions, a computer program implemented according to any one of claims 1 to 7, which, when invoked by a computer, executes the steps included in the corresponding method.

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