Service matching system and method based on multi-level user structure and dynamic connection

Abstract

The application discloses a service matching system and method based on a multi-level user structure and dynamic connection, wherein the system comprises: a user role structure management module for managing structured information of user roles; a release interaction module for supporting users to select structured descriptions of service supply or service demand according to types and completing early-stage user interaction; a connection identification and matching module for completing identification of B-B, B-C and C-C connections according to service demand released by users, calling a matching engine to calculate a matching degree based on the connection type, and pushing a result list sorted according to the matching degree to the users; and a matching connection module for providing a communication tool between users after matching is completed and providing a transaction promotion tool. The application solves the problem that an existing platform is difficult to support efficient satisfaction of cross-role and multi-dimensional service demand, and improves the accuracy, efficiency and compliance of service matching.

Description

Technical Field

[0001] This invention relates to the field of computer network information processing technology, specifically to a service matching system and method based on a multi-level user structure and dynamic connection. Background Technology

[0002] Current service platforms (such as e-commerce, crowdsourcing, and social networking) primarily focus on connecting specific user groups. However, many service demands—especially those involving comprehensive industries—often require collaborative resources and capabilities across multiple levels of roles. This involves not only individual service providers (C-end) but also professional organizations or businesses (B-end), and even requires the platform to operate under the guidance of policy compliance in specific sectors. Existing platform architectures lack efficient identification, structured management, and precise matching mechanisms for such complex, multi-layered (BB, BC, CC) dynamic connection demands, leading to the following problems:

[0003] 1. There is a disconnect between service demand and service capabilities, resulting in low matching accuracy and inefficiency.

[0004] 2. Difficulty in multi-role collaboration and inability to effectively integrate and allocate resources.

[0005] 3. For service sectors that rely on policy guidance or are subject to industry regulations, there is a lack of compliance guidance mechanisms.

[0006] 4. The lack of a unified platform to support complex and diverse connection relationships means that users have to switch between multiple platforms or channels.

[0007] 5. Current matching often only considers the matching of needs and abilities, without taking into account the compatibility of personalities between people.

[0008] Therefore, there is an urgent need for a service matching system and method based on a multi-level user structure and dynamic connections to solve the above problems. Summary of the Invention

[0009] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0010] A service matching system based on a multi-level user structure and dynamic connections includes:

[0011] The user role structure management module is used to manage the structured information of user roles;

[0012] The publishing interaction module allows users to select and publish structured descriptions of service supply or service demand by type, and complete the initial user interaction.

[0013] The connection identification and matching module is used to identify BB, BC and CC types of connections based on the service requests published by users, and to call the matching engine to calculate the matching degree based on the connection type, and push a list of results ranked by matching degree to the user.

[0014] The matching and connection module provides communication tools between users after a match is made, and also provides transaction facilitation tools.

[0015] Furthermore, the user role structure management module specifically includes:

[0016] The platform side is used by the platform provider to set relevant rules.

[0017] The server-side is used by industry service providers to store their structured information.

[0018] The regular user client is used by regular users and stores their structured information.

[0019] The adaptation layer integrates a database of relevant policies, industry standards, and / or compliance requirements to constrain and guide system operation.

[0020] Furthermore, on both the server and the user side, the corresponding users can complete identity authentication, establish a multi-dimensional tag library based on service capabilities, and call the publishing interaction module and connection module to publish service supply or service demand, respond to relevant business, and make evaluations.

[0021] The difference between the server-side and the regular client-side is:

[0022] On the server side, industry service providers need to complete industry qualification certification, publish service supply or service demand as B-end users, and respond to BB and BC businesses.

[0023] On the ordinary user side, ordinary users, as C-end users, publish service supply or service demand and respond to BC and CC business.

[0024] Furthermore, when users publish service offerings or service demands through the publishing interaction module, they can select their connection type preferences.

[0025] Furthermore, for the service requests published by users, the connection identification and matching module first determines whether the user has selected a connection type preference. If so, it processes the request according to the connection type selected by the user; otherwise, it automatically determines the connection type based on the published content and the user's identity.

[0026] After determining the connection type, the matching engine performs feature vectorization based on the structured information of the demand side and the multi-dimensional tag library of the service provider. Then, based on the feature vectors of the demand side and the service provider, it calculates the matching degree that reflects the adaptability between the demand and the service capability.

[0027] Furthermore, the matching engine automatically determines the importance of the current service requirement and, based on the different levels of importance, incorporates different levels of compliance rules into the adaptation layer; before calculating the matching degree, the matching engine removes matching results that do not meet the compliance rules.

[0028] Furthermore, for high-priority service requests, a regulatory electronic contract is generated before matching between users, and service performance is tracked. If there is a dispute during service performance or regarding the service acceptance results, an external institution will be involved in arbitration based on the rules preset in the adaptation layer.

[0029] Furthermore, for CC type connections, the matching engine not only calculates the matching degree reflecting the compatibility between demand and service capabilities, but also calculates the personality matching degree between users, and performs a weighted sum of the two matching degrees, with the resulting value serving as the final matching degree for the CC connection type.

[0030] Furthermore, after the service is completed, the system guides both parties to provide feedback and collects evaluation data, including feedback on the degree of matching.

[0031] Another aspect of the present invention provides a service matching method based on a multi-level user structure and dynamic connections, employing the aforementioned service matching system based on a multi-level user structure and dynamic connections, and including the following steps:

[0032] S1. Users publish content on the platform that includes service supply or service demand;

[0033] S2. For service requests published by demand-side users, identify the connection type;

[0034] S3. Based on the connection type, call the matching engine and calculate the matching degree;

[0035] S4. Push a list of results sorted by matching degree to users with demand;

[0036] S5. The demand-side user selects a service provider from the results list, and the matching is completed after both parties agree.

[0037] S6. After the service is completed, the system guides both parties to provide feedback.

[0038] Compared with existing technologies, the service matching system and method based on multi-level user structure and dynamic connection provided by this invention solves the problem that existing platforms are unable to efficiently meet cross-role and multi-dimensional service needs, and improves the accuracy, efficiency and compliance of service matching. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating a service matching method based on a multi-level user structure and dynamic connections provided by the present invention. Detailed Implementation

[0040] To make the technical means, creative features, objectives and effects of this invention easier to understand, the following description, in conjunction with the accompanying drawings and specific embodiments, further explains how this invention is implemented.

[0041] This invention provides a service matching system based on a multi-level user structure and dynamic connections, comprising: a user role structure management module for managing structured information of user roles; a publishing and interaction module for supporting users to select and publish structured descriptions of service supply or service demand according to type, and completing the initial user-to-user interaction; a connection identification and matching module for identifying BB, BC, and CC types of connections based on the service demand published by the user, and based on the connection type, calling a matching engine to calculate the matching degree and pushing a result list ranked by matching degree to the user; and a matching connection module for providing communication tools between users after matching and providing transaction facilitation tools.

[0042] Reference Figure 1 As shown, another aspect of the present invention provides a service matching method based on a multi-level user structure and dynamic connections, employing the aforementioned service matching system based on a multi-level user structure and dynamic connections, and including the following steps:

[0043] S1. Users publish content on the platform that includes service supply or service demand;

[0044] S2. For service requests published by demand-side users, identify the connection type;

[0045] S3. Based on the connection type, call the matching engine and calculate the matching degree;

[0046] S4. Push a list of results sorted by matching degree to users with demand;

[0047] S5. The demand-side user selects a service provider from the results list, and the matching is completed after both parties agree.

[0048] S6. After the service is completed, the system guides both parties to provide feedback.

[0049] In a specific embodiment, the user role structure management module specifically includes:

[0050] On the platform side, the platform operator uses the system to set relevant rules. As the core organizer and manager of the system, the platform operator is specifically responsible for global parameters such as user management, rule formulation, matching engine operation, result distribution, information display, security assurance, compliance monitoring, and incentive mechanism implementation.

[0051] The adaptation layer integrates a database of relevant policies, industry standards, and / or compliance requirements to constrain and guide system operation. This layer ensures that service processes, qualification certifications, and contract specifications comply with regulatory requirements. The platform, based on the information from the adaptation layer, conducts pre-process or in-process compliance reviews and provides information prompts for user registration, service posting, and transaction matching.

[0052] The server-side component, used by industry service providers (B-end users), stores structured information about these providers. These industry service providers include various enterprises, merchants, and professional organizations within the industry.

[0053] The ordinary user client is used by ordinary users (C-end users) and stores the structured information of ordinary users;

[0054] Furthermore, on both the server and the user side, users can complete identity authentication, establish a multi-dimensional tag library based on service capabilities, and call the publishing interaction module and connection module to publish service supply or service demand, respond to relevant business, and provide evaluations.

[0055] The multidimensional tag library is generated jointly by user-submitted content and system behavior analysis results, and is structured and stored on the corresponding role's end. Dimensional tags can include: identity / qualification tags, such as industry, company size, and professional certificates; capability / supply tags, such as professional skills, service projects, and success stories; demand tags, such as frequently purchased services and desired resource types; and interest / scenario tags, such as industry communities, hobbies, and frequently visited areas. For example, on the service side, the multidimensional tag library for industry service providers can include business type, service region, available scale, and historical reviews; ordinary users can also create similar multidimensional tag libraries to reflect services they can perform personally, such as participating in CC mutual aid.

[0056] The difference between the server-side and the ordinary user-side lies in the following: On the server-side, industry service providers need to complete industry qualification certifications, act as B-end users to publish service offerings or service demands, and respond to B2B and B2C business. On the ordinary user-side, ordinary users act as C-end users to publish service offerings or service demands and respond to B2C and C2C business.

[0057] In addition, the platform can support customized service demand and capability description template tag libraries for specific industries to reduce the difficulty of user input and matching, and help users to specify their service needs or service supply capabilities into a structured description.

[0058] Furthermore, when users (both B-end and C-end users) publish service offerings or service demands through the publishing interaction module, they can choose their preferred connection type. For example, when a C-end user publishes a service demand, they can choose "Find professional vendors (B-end)" or "Find peer experience (C-end)".

[0059] For service requests published by users, the connection identification and matching module first determines whether the user has selected a connection type preference. If so, it processes the request according to the connection type selected by the user; otherwise, it automatically determines the connection type based on the published content and the user's identity.

[0060] For B2B connections, the connection identification and matching module needs to identify and process collaboration / service needs between B-end users within the industry. For example, if a need contains keywords such as "supply chain integration," "OEM manufacturing," or "enterprise-level software procurement," and the publisher is a certified enterprise, it is automatically identified as a B2B connection. For example, a florist is looking for a high-quality photography team for long-term and stable cooperation.

[0061] For B2C connections, the connection identification and matching module needs to identify and process the needs of B-end users to provide professional services to C-end users. For example, a photography studio may provide wedding photography services for newlyweds.

[0062] For C-end connections, the connection identification and matching module needs to identify and handle service requests such as mutual assistance and experience sharing between C-end users. For example, user A shares DIY wedding tips with user B, and user B provides a small amount of points in return.

[0063] After determining the connection type, the matching engine uses deep learning methods to process relevant information from natural language descriptions, based on the structured information of the demand side (either B-end or C-end users) (such as keywords, tags, budget, region, time, etc. extracted from the service demand description) and the multi-dimensional tag library of the service provider (either B-end or C-end users) (service scope, skills, pricing, reputation, etc.). This information is then vectorized into features, and based on the feature vectors of the demand side and the service provider, the matching degree reflecting the suitability between the demand and the service capability is calculated.

[0064] In addition, the matching engine automatically determines the importance of the current service request and, based on the importance, incorporates different levels of compliance rules (such as whether the service provider has the necessary qualifications) into the adaptation layer. Before calculating the matching degree, the matching engine removes matching results that do not meet the compliance rules. For high-importance service requests, a regulatory electronic contract is generated between users before matching is established, and service performance is tracked. If there is a dispute during service performance or regarding service acceptance results, arbitration is conducted by an external agency based on the rules preset in the adaptation layer.

[0065] After the matching engine calculates the matching degree, it pushes a list of results sorted by matching degree to the interface corresponding to the requester (server-side or ordinary user-side interface) according to different connection types. Such as a list of service providers or a list of collaborators, the matching is completed after both parties agree.

[0066] After matching is completed, the matching connection module provides communication tools between matched users, such as in-site messaging, instant messaging, appointment forms, etc.; and provides transaction facilitation tools, such as online contract template calling, payment guarantee tools, and distinguishes between professional contracts (corresponding to BB and BC connections) and personal mutual aid agreements (corresponding to CC connections).

[0067] Furthermore, after the service is completed, the system guides both parties to provide feedback and collects evaluation data. The evaluation content includes feedback on the matching degree, and the data can be used to improve the matching model and establish a user credit system.

[0068] In addition, for CC connections, a contribution value / points system can be set up to incentivize C-end users to share valuable information (experience, strategies) or provide non-paid mutual assistance.

[0069] Furthermore, since CC connections mainly involve mutual assistance and experience sharing among C-end users, when calculating the matching degree of CC connection types, the matching engine can consider not only the matching of service needs but also the personality matching between C-end users.

[0070] For example, for end-users (C-end), similar to the aforementioned multi-dimensional tag library based on service capabilities, a personality tag library can also be established. This can be achieved by collecting users' basic information and behavioral data, analyzing the personality traits reflected in this data using a large language model, generating personality tags and assigning tag scores. These personality tags are then dynamically updated as data is continuously collected, completing the establishment of the personality tag library. Furthermore, this can be combined with an interpersonal circular model to simultaneously satisfy the principles of similarity and complementarity. In this model, the love axis represents traits such as affinity, friendliness, warmth, care, empathy, solidarity, and closeness in interpersonal relationships, while the dominance axis represents traits such as dominance, control, power, confidence, ambition, and influence in interpersonal relationships.

[0071] When calculating the match score for CC connection types, the matching engine first normalizes the tag scores of all personality tags. For any personality tag, its normalized tag score is represented by p. Then, its corresponding love axis score is calculated using the following formula. With the dominant axis score :

[0072] ;

[0073] ;

[0074] in, and These represent the love axis coefficient and dominance axis coefficient corresponding to the personality label, respectively. For any personality label, the descriptive text of that personality label is also input into a pre-trained large language model to obtain the love axis coefficient output by the large language model. With the dominance axis coefficient In this embodiment, and The values ​​are all between -1 and 1; This reflects the correlation between the personality tag and the corresponding traits of the love axis. The closer the value is to -1, the stronger the negative correlation. The closer the value is to 1, the stronger the positive correlation. The closer the value is to 0, the lower the correlation; similarly, The value reflects the correlation between the personality tag and the trait corresponding to the ruling axis.

[0075] The love axis score corresponding to each personality tag is obtained through this calculation method. With the dominant axis score All scores are between -1 and 1. (Love axis score) The closer the score is to 1, the higher the affinity, which may reflect a personality trait such as extreme friendliness, warmth, and care for others; the love axis score The closer a score is to -1, the lower the affinity, potentially reflecting personality traits such as indifference, aloofness, or hostility. (Dominant Axis Score) The closer the score is to 1, the higher the dominance level, which may reflect extreme dominance, high control, and self-confidence. The closer to -1, the lower the dominance, which may reflect personality traits such as compliance, passivity, and lack of control.

[0076] After determining the love axis score and dominance axis score corresponding to all personality tags of a user, the personality matching degree between any two users is calculated in the following way;

[0077] The matching value 'c' between any two personality tags is calculated using the following formula:

[0078] ;

[0079] in, This represents the difference in the love axis scores corresponding to these two personality tags. This represents the sum of the dominance axis scores corresponding to these two personality tags.

[0080] For any two users, let the number of personality tags for the first user be m and the number of personality tags for the second user be n. Then, the matching value of any personality tag of the first user and any personality tag of the second user can be calculated using the above formula. A total of m*n matching values ​​are calculated. Then, the average of these m*n matching values ​​is taken as the personality matching degree between the two users. The smaller the personality matching degree, the more matched the personalities of the two users are.

[0081] Understandably, this calculation method means that the lower the difference in the love axis scores between two users, the lower the match score; that is, the closer the love axis-related traits (affinity, friendliness, warmth, care, empathy, solidarity, closeness, etc.) are between the two users, the more compatible their personalities are, conforming to the aforementioned similarity principle. On the other hand, the closer the sum of the dominance axis scores of two users is to 0, the lower the match score; that is, the more complementary the dominance axis-related traits (dominance, control, power, confidence, ambition, influence, etc.) are between the two users, the more compatible their personalities are, conforming to the aforementioned complementarity principle.

[0082] After calculating the personality matching score, the final matching score of the CC connection type is obtained by weighted summing the personality matching score and the matching score corresponding to the service requirements.

[0083] In summary, the service matching system and method based on a multi-layered user structure and dynamic connections provided by this invention solves the problem that existing platforms struggle to efficiently meet cross-role and multi-dimensional service needs, improving the accuracy, efficiency, and compliance of service matching. Specifically, this invention achieves:

[0084] 1. Efficient resource allocation: Clearly define roles and connection types, break down information barriers, and enable industry resources, individual capabilities, and platform capabilities to be optimally integrated within a compliant framework, greatly improving the utilization rate and matching efficiency of service resources.

[0085] 2. More precise demand fulfillment: The structured and categorized connection model (BB, BC, CC) combined with the intelligent recommendation engine significantly improves the matching accuracy and user experience satisfaction between service providers and consumers (whether B or C).

[0086] 3. Rationalization of the industry ecosystem: By internalizing policy requirements into platform rules, the compliance and transparency of service supply in the industry (especially in industries involving regulation) are improved, which helps to promote the healthy development and standardized management of the industry.

[0087] 4. Diverse user experience: Provides full-scenario service support options from professional (BC) to mutual assistance (CC) to meet users' differentiated and multi-level service needs.

[0088] 5. Improved Personality Compatibility: For CC-type connections, personality compatibility is considered to improve the stability of matching results and user satisfaction.

[0089] 6. High scalability: It has strong universality and can be quickly replicated and applied to other comprehensive vertical industries (such as education, housekeeping, home decoration, elderly care, community services, etc.) by adjusting role definitions and rule base.

[0090] 7. Constructing a comprehensive service relationship network: The core of this invention lies in constructing a service relationship management structure and method that integrates multiple real-time dynamic service connections (BB, BC, CC) into a unified platform network, and achieving effective operation.

[0091] Finally, it should be noted that the above description is only an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A service matching system based on a multi-level user structure and dynamic connections, characterized in that, include: The user role structure management module is used to manage the structured information of user roles; The publishing interaction module allows users to select and publish structured descriptions of service supply or service demand by type, and complete the initial user interaction. The connection identification and matching module is used to identify BB, BC and CC types of connections based on the service requests published by users, and to call the matching engine to calculate the matching degree based on the connection type, and push a list of results ranked by matching degree to the user. The matching and connection module provides communication tools between users after a match is made, and also provides transaction facilitation tools.

2. The service matching system based on multi-level user structure and dynamic connection according to claim 1, characterized in that, The user role types managed by the user role structure management module specifically include: The platform side is used by the platform provider to set relevant rules. The server-side is used by industry service providers to store their structured information. The regular user client is used by regular users and stores their structured information. The adaptation layer integrates a database of relevant policies, industry standards, and / or compliance requirements to constrain and guide system operation.

3. The service matching system based on multi-level user structure and dynamic connection according to claim 2, characterized in that, On both the server and the user side, users can complete identity authentication, establish a multi-dimensional tag library based on service capabilities, and call the publishing interaction module and connection module to publish service supply or service demand, respond to relevant business, and make evaluations. The difference between the server-side and the regular client-side is: On the server side, industry service providers need to complete industry qualification certification, publish service supply or service demand as B-end users, and respond to BB and BC businesses. On the ordinary user side, ordinary users, as C-end users, publish service supply or service demand and respond to BC and CC business.

4. The service matching system based on multi-level user structure and dynamic connection according to claim 3, characterized in that, When users publish service offerings or service demands through the publishing interaction module, they can select their connection type preferences.

5. The service matching system based on a multi-level user structure and dynamic connection according to claim 4, characterized in that, For a user's published service request, the connection identification and matching module first determines whether the user has selected a connection type preference. If so, it processes the request according to the connection type selected by the user; otherwise, it automatically determines the connection type based on the published content and the user's identity. After determining the connection type, the matching engine performs feature vectorization based on the structured information of the demand side and the multi-dimensional tag library of the service provider. Then, based on the feature vectors of the demand side and the service provider, it calculates the matching degree that reflects the adaptability between the demand and the service capability.

6. The service matching system based on a multi-level user structure and dynamic connection according to claim 5, characterized in that, The matching engine also automatically determines the importance of the current service requirement and, based on the importance, incorporates different levels of compliance rules into the adaptation layer. Before calculating the matching degree, the matching engine removes matching results that do not meet the compliance rules.

7. The service matching system based on multi-level user structure and dynamic connection according to claim 6, characterized in that, For high-priority service requests, a regulatory electronic contract is generated before matching between users, and service performance is tracked. If there is a dispute during service performance or regarding the service acceptance results, an external institution will be involved in arbitration based on the rules preset in the adaptation layer.

8. The service matching system based on a multi-level user structure and dynamic connection according to claim 5, characterized in that, For CC type connections, the matching engine not only calculates the matching degree reflecting the compatibility between demand and service capabilities, but also calculates the personality matching degree between users, and then performs a weighted sum of the two matching degrees to obtain the final matching degree for the CC connection type.

9. The service matching system based on multi-level user structure and dynamic connection according to claim 1, characterized in that, After the service is completed, the system guides both parties to provide feedback and collects evaluation data, including feedback on the degree of matching.

10. A service matching method based on a multi-level user structure and dynamic connections, characterized in that, The service matching system based on a multi-level user structure and dynamic connection as described in any one of claims 1-9 includes the following steps: S1. Users publish content on the platform that includes service supply or service demand; S2. For service requests published by demand-side users, identify the connection type; S3. Based on the connection type, call the matching engine and calculate the matching degree; S4. Push a list of results sorted by matching degree to users with demand; S5. The demand-side user selects a service provider from the results list, and the matching is completed after both parties agree. S6. After the service is completed, the system guides both parties to provide feedback.

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