Online consultation system and online consultation method for trusted data space, equipment and storage medium

Abstract

The invention discloses an online consultation system and method for a trusted data space, equipment and a storage medium, and relates to the technical field of information processing. The obtaining module is used for obtaining a standardized question consultation card corresponding to a target user through an online consultation model adaptive to a trusted data space data interaction protocol; the engineer recommendation module is used for matching the standardized question consultation card with a preset engineer dynamic portrait library to determine a target engineer; the multi-party conference initiating and assisting module is used for establishing an online conference channel between the target user and the target engineer based on an identity credibility authentication mechanism and providing an online consultation assisting function for the online conference channel; and the service record generation module is used for acquiring consultation process data generated by the target user and the target engineer in the online conference channel and generating an online consultation result. According to the invention, the overall consultation efficiency and user experience can be improved.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to an online consultation system, method, device and storage medium for trusted data spaces. Background Technology

[0002] Current trusted data spaces lack online customer service systems, while traditional software platforms mostly employ one-on-one chat models, such as WeChat customer service, web-based customer service, and telephone customer service. These systems play a crucial role in handling basic customer inquiries. Trusted data spaces, however, are data circulation and utilization infrastructures that connect multiple stakeholders based on consensus rules, enabling data resource sharing and co-creation of data element value—an application ecosystem. However, traditional customer systems and models are completely inadequate for complex professional issues such as data elementization and data circulation and utilization.

[0003] On the one hand, traditional customer service systems do not support simultaneous online service from multiple parties. When users encounter specialized or complex issues, general customer service personnel often cannot resolve them independently, requiring the involvement of engineers specializing in the field. However, users have limited avenues for contacting these engineers, relying instead on customer service personnel relaying information layer by layer, leading to inefficient problem-solving. On the other hand, existing customer service systems have relatively limited functionality, primarily relying on text, screenshots, or telephone communication. When dealing with complex issues, repeated communication is required to understand the customer's problem, which not only significantly reduces the user experience but also increases communication and time costs. Summary of the Invention

[0004] The main objective of this application is to provide an online consultation system, method, device, and storage medium for trusted data spaces, aiming to solve the problems of low problem-solving efficiency and high communication costs in existing online customer service systems when dealing with complex issues.

[0005] To achieve the above objectives, this application proposes an online consultation system for a trusted data space. The system includes a multi-channel user demand access and preprocessing module, an engineer recommendation module, a multi-party meeting initiation and support module, and a service record generation module. The multi-channel user demand access and preprocessing module is used to obtain standardized question consultation cards corresponding to target users through an online consultation model adapted to the trusted data space data interaction protocol. The engineer recommendation module is used to match the standardized question and consultation card with a preset dynamic profile database of engineers to determine the target engineer; The multi-party meeting initiation and assistance module is used to establish an online meeting channel between the target user and the target engineer based on the trusted identity authentication mechanism of the trusted data space, and to provide online consultation assistance functions for the online meeting channel. The service record generation module is used to acquire consultation process data generated by the target user and the target engineer in the online meeting channel, and generate online consultation results.

[0006] In one possible implementation, the online consultation system for trusted data space further includes an engineer tagging and dynamic management module. The engineer tagging and dynamic management module is used to collect basic engineer information, past project experience and historical service records of multiple candidate engineers, analyze the matching domain tags corresponding to each candidate engineer, and perform many-to-many mapping between each candidate engineer and the matching neighborhood tags to generate an engineer profile corresponding to each candidate engineer.

[0007] In one possible implementation, the online consultation system for trusted data space further includes a meeting anomaly handling module. The meeting operation and anomaly handling module is used to save the meeting interaction record and the current consultation progress when an anomaly signal is detected, and to re-establish the online meeting channel. If the reconstruction fails, a meeting anomaly record is generated, and an abnormal consultation channel is created based on the meeting interaction record, the current consultation progress, and the original participants.

[0008] In one possible implementation, the online consultation system for trusted data space further includes a multi-dimensional service quality assessment module. This module is used to obtain structured service records, user satisfaction survey results, and engineer follow-up task completion status, calculate problem resolution rate, average service duration, user satisfaction score, and engineer's problem resolution rate for similar issues, and generate a multi-dimensional service quality report.

[0009] In one possible implementation, the online consultation system for trusted data space further includes a service data mining and system self-optimization module. This module is used to analyze historical service records and multi-dimensional service quality reports to identify engineer tag optimization points; synchronize these engineer tag optimization points to the preset engineer dynamic profile library; and obtain historical service records and user feedback to fine-tune the online consultation model.

[0010] In one possible implementation, the online consultation system for trusted data space further includes a multimodal interaction adaptation module, which is used to receive text, voice streams, equipment fault screenshots, equipment real-scene images and 3D modeling transmitted in the online conference channel, and generate consultation process data. The multimodal interaction adaptation module is also used to analyze the fault features in the device fault screenshot using image recognition technology, and align the fault features with the 3D model modeling, allowing the target engineer to annotate the 3D model modeling; and to call the screen projection interface to synchronize the target engineer's operation demonstration to the online meeting channel.

[0011] To achieve the above objectives, this application proposes an online consultation method for a trusted data space, the method comprising: A standardized question and consultation card corresponding to the target user is obtained through an online consultation model that adapts to the trusted data space data interaction protocol; The standardized problem consultation cards are matched with a preset dynamic engineer profile database to identify the target engineer; An online meeting channel is established between the target user and the target engineer based on a trusted identity authentication mechanism in a trusted data space, and online consultation assistance is provided for the online meeting channel. The system acquires consultation process data generated between the target user and the target engineer in the online meeting channel and generates online consultation results.

[0012] In one possible implementation, matching the standardized problem consultation card with a preset dynamic engineer profile database to identify the target engineer includes: Based on the user profile tags and user intent tags of the standardized question consultation card, several candidate engineers whose tag matching degree meets the preset threshold are selected from the preset engineer dynamic profile library; Based on the tag matching degree and the problem-solving rate and response speed of each candidate engineer, calculate the consultation recommendation score corresponding to each candidate engineer and generate an engineer recommendation list; The target engineer is determined by sorting the engineers in the engineer recommendation list.

[0013] Furthermore, to achieve the above objectives, this application also proposes an online consultation device for a trusted data space, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the online consultation method for a trusted data space as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the online consultation method for trusted data space as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the online consultation method for trusted data spaces as described above.

[0016] This application provides an online consultation system, method, device, and storage medium for a trusted data space. The online consultation system for a trusted data space includes: a multi-channel user demand access and preprocessing module, used to obtain standardized question consultation cards corresponding to target users through an online consultation model adapted to the trusted data space data interaction protocol; an engineer recommendation module, used to match the standardized question consultation cards with a preset dynamic engineer profile database to determine target engineers; a multi-party meeting initiation and assistance module, used to establish an online meeting channel between the target user and the target engineer based on the trusted data space's trusted identity authentication mechanism, and to provide online consultation assistance functions for the online meeting channel; and a service record generation module, used to obtain the target user and the target engineer's information. The consultation process data generated by engineers in the online meeting channel is used to generate online consultation results, thereby transforming users' scattered consultation needs into structured information. This avoids increased communication costs caused by vague descriptions of needs. Based on standardized question cards and a pre-set dynamic engineer profile database, target engineers are matched to reduce resource mismatch and improve engineer matching accuracy. In addition, while establishing an online meeting channel, consultation assistance functions are provided to help target users and engineers efficiently convey complex information (such as details of equipment failure). This solves the limitations of traditional customer service, which relies solely on text / voice to convey professional issues. Ultimately, a complete service chain is formed from need access and engineer matching to meeting communication and result recording, avoiding the problems of "broken information transfer and no follow-up service" in traditional customer service, and improving overall consultation efficiency and user experience. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A system architecture diagram provided for the online consultation system used in this application for trusted data space; Figure 2 A sequence diagram of meeting initiation provided for the online consultation system for the trusted data space in this application; Figure 3 A sequence diagram of the meeting's conclusion provided for the online consultation system used in this application for trusted data space; Figure 4 A simplified flowchart illustrating the online consultation process for the trusted data space provided in this application. Figure 5 This is a flowchart illustrating an embodiment of the online consultation method for trusted data spaces provided in this application. Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the online consultation method for trusted data space in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device, big data service platform, or online consultation system for trusted data spaces capable of performing the above functions. The following description uses an online consultation system for trusted data spaces as an example to illustrate this embodiment and the subsequent embodiments.

[0024] Based on this, embodiments of this application provide an online consultation system for a trusted data space, referring to... Figure 1 , Figure 1 This is a system architecture diagram provided for the online consultation system for trusted data space in this application.

[0025] In this embodiment, the system includes a multi-channel user demand access and preprocessing module, an engineer recommendation module, a multi-party meeting initiation and assistance module, and a service record generation module. The multi-channel user demand access and preprocessing module is used to obtain standardized question consultation cards corresponding to target users through an online consultation model adapted to the trusted data space data interaction protocol. It should be noted that the multi-channel user demand access and preprocessing module refers to the core module in the online consultation system responsible for receiving consultation requests from target users from different channels and transforming these fragmented requests into structured cards to avoid inefficiency caused by disorganized demand information. The online consultation model refers to an algorithmic model with capabilities such as Natural Language Processing (NLP) and image recognition, not simply a logical model. This model needs to be pre-trained with professional domain data (such as data element engineering problems and equipment failure cases) and can parse various types of demand information, including text, voice, and images. The target user refers to the entity that initiates a question consultation using the online consultation system, which can be an individual user or a corporate user. The standardized question consultation card refers to the structured demand document output by the module, containing fixed fields such as a demand identifier (unique ID), basic information of the target user, core description of the problem (e.g., paper jam in the printer feed), problem type label (e.g., office equipment repair - printer failure), and urgency level (e.g., high / medium / low).

[0026] Furthermore, as data is the fifth factor of production, it needs to be transformed from a resource into an asset through compliant circulation, secure sharing, and collaborative creation. However, traditional online consulting systems lack the ability to securely manage and extract value from data elements, making it difficult to meet the data interaction needs of multiple stakeholders. Therefore, there is an urgent need for a secure data interaction method that can be conducted in a trusted data space to solve the above problems. For example, a manufacturing company needs to provide a consulting agency with production process data (including equipment operating parameters and capacity data) to obtain optimization solutions. Through the trusted data space module of this system, the company's data is uploaded after being encrypted and identified. The consulting agency can only obtain the data analysis results and cannot access the original data. The entire data usage process is traceable, which not only ensures data security but also realizes the value transformation of data elements. An industry association uses this system to gather market research data from its member companies. Relying on the value co-creation capabilities of the trusted data space, it integrates and forms an industry trend report under the premise of compliance, providing data support for consulting decisions of various companies and realizing the collaborative value-added of data elements.

[0027] Furthermore, the Trusted Data Space data interaction protocol refers to a set of protocols that conform to the Trusted Data Space technical specifications and are used to ensure that data is trustworthy in terms of identity, data, transmission, and use during the interaction process. These protocols include data encryption transmission protocols, identity mutual recognition protocols, and data access control protocols, ensuring that user-required data meets the security requirements of the Trusted Data Space during access and processing, and preventing data leakage or unauthorized access. Therefore, this system, within the Trusted Data Space framework, adds data identification, hierarchical access control, and end-to-end data storage functions. Data identification assigns a unique trusted identifier to each piece of consultation-related data; hierarchical access control sets access rules according to "consultation scenario - data type - scope of use"; and end-to-end data storage uses blockchain technology to record the data flow trajectory, ensuring the compliance and traceability of data element circulation.

[0028] In one possible implementation, this module can provide an automatic determination function for the urgency of needs. The online consultation model automatically marks the urgency of standardized problem consultation cards by analyzing keywords in the problem description (such as "unusable" and "urgent"), the module to which the target user belongs (such as "the needs of production system operation and maintenance users are urgent by default"), and the scope of the problem's impact (such as "printer failure affecting 100 people in the office"). At the same time, it supports the target user to manually adjust the urgency level, and the system prioritizes the handling of high-urgency needs based on the urgency level.

[0029] Specifically, this module interfaces with various channels (APP, webpage, WeChat Work, etc.) to receive text / voice / image requests from target users. The online consultation model, adapted to the Trusted Data Space data interaction protocol, must first complete identity verification and data encryption / decryption upon receiving user request data before calling the online consultation model for parsing. Text requests directly extract intent and keywords; voice requests are first converted to text and then parsed; and image requests are extracted using image recognition to extract fault features. This enables information completion and standardization. The model combines the target user's historical operation records (such as the device models of previous inquiries) and the current module (e.g., determined by login information as "Office Equipment Maintenance Module") to complete missing fields in the request information (e.g., automatically filling in the device model) and generate standardized problem consultation cards according to a preset template. This module then verifies the completeness of the fields in the cards (e.g., whether the problem type label is missing) and logical consistency (e.g., "high urgency but no specific impact description"). If the verification passes, the card is output to the engineer recommendation module; if the verification fails, feedback is sent to the target user to supplement compliance information. In this step, the online consultation model's adaptation to the Trusted Data Space data interaction protocol is reflected in two aspects: First, in the data receiving stage, the model verifies the legitimacy of the target user's identity through the identity mutual recognition mechanism in the protocol, allowing only users who have completed registration and obtained authorization in the Trusted Data Space to initiate requests, thus preventing unauthorized users from accessing the platform. Second, in the data processing stage, the model's parsing and transformation of user request data must comply with the data access control requirements in the protocol, extracting only information directly related to the consultation request and not acquiring unauthorized data of the target user (such as the user's sensitive business data), ensuring that the data processing process complies with the compliance requirements of the Trusted Data Space.

[0030] The engineer recommendation module is used to match the standardized question and consultation card with a preset dynamic profile database of engineers to determine the target engineer; It's important to note that the engineer recommendation module is the core module responsible for accurate "demand-engineer" matching within the online consultation system used in the trusted data space. Its core purpose is to address the pain points of traditional customer service, which involves multiple layers of information forwarding and low efficiency in connecting with professional engineers. Through automated matching, it allows target users to quickly connect with suitable professionals. The preset dynamic engineer profile library refers to a built-in database that stores structured information about all candidate engineers. This includes basic engineer information (ID, name, contact information), domain tags (e.g., network failure, printer repair), real-time status (online / idle / busy, detected via long-term connection), and capability indicators (similar problem resolution rate, average response time, user satisfaction), etc. The profile library needs to be updated in real-time with engineer status and capability indicators to ensure timely and accurate matching. The target engineer refers to the engineer output by the module that best matches the standardized problem consultation card. This can be one or more engineers (e.g., complex problems require collaboration from multiple professional engineers) to support multi-party online services. If there are multiple engineers, the module needs to synchronously match their collaborative service permissions and division of labor.

[0031] Specifically, in one embodiment, the tag matching degree calculation adopts the cosine similarity algorithm, which converts the problem type tags (such as office equipment repair, printer paper feed failure) of the standardized problem consultation card into vectors, and calculates the similarity with the domain tag vector of the engineer profile. The higher the similarity, the higher the basic matching score. The capability index calculation adopts the weighted summation method, and the weight allocation prioritizes the higher resolution rate of similar problems. The final recommendation score = tag matching degree score × 40% + capability index score × 60%, ensuring that the matching result not only meets the need type, but also has high resolution capability.

[0032] The multi-party meeting initiation and assistance module is used to establish an online meeting channel between the target user and the target engineer based on the trusted identity authentication mechanism of the trusted data space, and to provide online consultation assistance functions for the online meeting channel. It's important to note that the multi-party meeting initiation and support module refers to the core module within the online consultation system for trusted data spaces, responsible for building a real-time communication platform between target users and engineers, and providing intelligent support tools. The online meeting channel refers to the temporary audio and video communication link established by the module, not a simple chat window. It requires creating an independent meeting room by calling video conferencing APIs (such as signaling servers or streaming media services), including core elements such as room ID, participant permissions, and audio / video stream transmission. Target users and engineers are the core participants in the online meeting channel. If the issue involves multiple professional fields (such as data element engineering + system maintenance), the module can support adding multiple engineers (such as business experts and technical personnel). The online consultation support function refers to the additional tools provided by the module to improve meeting communication efficiency, such as real-time problem summary generation, solution library matching, and AR fault labeling, ensuring that complex problems can be resolved efficiently.

[0033] Furthermore, the trusted data space identity authentication mechanism refers to the mechanism used in the trusted data space to verify the legitimacy of the participants' identities and ensure that the participants' identities are authentic and traceable. It usually includes identity verification based on digital certificates, identity storage based on blockchain, cross-domain identity mutual recognition, etc., to ensure that only target users and target engineers who have passed identity authentication can access the online meeting channel, and to prevent unauthorized personnel from breaking into the meeting and causing data leakage.

[0034] Specifically, the process of establishing an online meeting channel by the module includes: First, after receiving the engineer matching result, the module verifies the identities of the target user and the target engineer through the trusted data space's identity authentication mechanism (e.g., verifying both parties' digital certificates and confirming the validity of their identities in the trusted data space); second, based on the identity authentication result and the demand identifier of the standardized question and consultation card, the module calls the meeting service interface to create an independent online meeting room, generating a unique meeting room ID and a secure access link, while configuring meeting parameters (e.g., meeting validity period, participant permissions, and data transmission encryption method); Second, the module sends an invitation notification containing "meeting room ID, room link, and question summary (from the standardized question and consultation card)" to the client of the target user and the target engineer. The notification method supports pop-up windows and reminder sounds (e.g., an invitation message pops up on the engineer's interface and a reminder sound plays); Third, the target user and the target engineer join the room by clicking the room link or entering the meeting ID. The module verifies the participant's identity (matching the demand identifier and the engineer ID). After successful verification, the audio and video streams are synchronized, completing the establishment of the online meeting channel and ensuring the security and stability of the communication link. (See reference for details.) Figure 2 as well as Figure 3 .

[0035] Furthermore, the online consultation assistance function can include an AR fault annotation tool. For equipment fault consultation scenarios, the target user can take a real-world video of the faulty equipment with their mobile phone. The module calls the AR engine to align the video with a preset 3D model of the equipment (such as a printer assembly model). The target engineer can then annotate the faulty parts on the 3D model (such as the paper feed roller being worn and needing replacement). The annotations are superimposed onto the target user's real-world video in real time, solving the problem of difficult description and understanding of complex equipment faults. At the same time, it supports real-time matching of the solution library. The module analyzes the communication content in the meeting in real time, connects to the solution library built based on historical service records, and pushes the best solution steps for similar problems (such as "Steps to solve paper jam in printer paper feed: 1. Power off; 2. Open the paper feed cover; 3. Remove foreign objects"), improving the efficiency of problem solving.

[0036] In addition, the online consultation assistance function needs to work in conjunction with other modules to form a functional closed loop: the AR annotation files and solution push records generated by the assistance function need to be synchronized to the service record generation module as part of the consultation process data; if it is found in the meeting that the target engineer cannot solve the problem independently (such as involving cross-domain knowledge), the module can call the engineer recommendation module, add tags based on the current problem (such as "printer paper feed roller repair + hardware replacement"), and match other engineers with the corresponding tags to join the meeting, so as to support multi-person online collaborative service and ensure that complex problems can be solved through multi-party collaboration.

[0037] The service record generation module is used to acquire consultation process data generated by the target user and the target engineer in the online meeting channel, and generate online consultation results.

[0038] It should be noted that the service record generation module is the core module in the online consultation system used in the trusted data space, responsible for retaining data from the entire consultation process and outputting structured results. Its core purpose is to address the pain points of traditional customer service, such as the lack of complete service records and the inability to trace and follow up afterward, providing a basis for service quality assessment, engineer capability iteration, and secondary follow-up on issues. Consultation process data refers to all consultation-related data generated in the online meeting channel, covering three categories: "basic meeting information, interaction records, and auxiliary tool data." Basic meeting information includes meeting room ID, participants (target user ID + target engineer ID), meeting start and end times, and meeting status (normal end / abnormal interruption); interaction records include "text chat records, voice-to-text records, and participant operation records (such as screen projection start time)"; auxiliary tool data includes AR annotation files, solution push records, and device failure screenshots. All data must be linked along a timeline to ensure traceability.

[0039] Furthermore, the online consultation result refers to the structured service document output by the module, which includes fields such as service record ID (unique identifier), consultation process data summary (e.g., meeting duration 15 minutes, problem type printer paper feed failure), problem resolution status (resolved / unresolved), solution details (e.g., cleaning foreign objects from the paper feed), target user feedback (e.g., satisfaction rating), and follow-up tasks (e.g., callback time for unresolved issues).

[0040] Specifically, the process of acquiring consultation process data and generating online consultation results needs to distinguish between two scenarios: normal meeting termination and abnormal meeting termination. First, a normal meeting end means the target user or engineer initiates the end command. The module first pushes a "Problem Resolved Status Confirmation" pop-up to the target user, indicating the resolution status based on feedback; then it pushes a "Solution Entry" pop-up to the target engineer, asking them to supplement the solution details; finally, it integrates all consultation process data to generate online consultation results, which can be used as a reference. Figure 4 .

[0041] Secondly, "abnormal meeting termination" refers to a meeting interrupted due to unforeseen circumstances such as network outages or device offline. The module automatically retrieves the consultation process data before the interruption, marks the meeting status as "abnormal interruption," clearly marks "unresolved" when generating online consultation results, and records the interruption time and possible reasons (e.g., "Network interruption at 15:20 on October 31, 2025"). At the same time, it triggers subsequent follow-up tasks, synchronizing the results to the target engineer, who can then proactively call back the target user using the recorded contact information (e.g., phone number, email) to avoid any issues being overlooked.

[0042] In one possible implementation, the service record generation module can provide a service record classification and retrieval function, supporting the retrieval of online consultation results by service record ID, target user ID, problem type tag, meeting time, resolution status, etc., which makes it convenient for administrators to trace the service process and engineers to view historical cases; at the same time, it supports exporting online consultation results to Excel or PDF format, which is convenient for enterprises to conduct service quality statistics and analysis.

[0043] In addition, after the module generates online consultation results, it needs to collaborate with other modules to form a data closed loop: synchronize the online consultation results to the engineer tagging and dynamic management module to update the historical service records of the target engineer (such as the number of times similar problems are solved +1); synchronize them to the multi-dimensional service quality evaluation module as the basic data for calculating problem resolution rate and user satisfaction; and synchronize them to the service data mining and system self-optimization module as the data source for training the online consultation model and optimizing engineer tags, ensuring that the online consultation system used in the trusted data space can continuously iterate and improve its service capabilities.

[0044] This embodiment transforms users' scattered consultation needs into structured information, avoiding increased communication costs caused by vague descriptions of needs. Based on standardized consultation cards and a pre-set dynamic engineer profile database, it matches target engineers, reducing resource mismatch and improving engineer matching accuracy. It then establishes an online meeting channel while providing consultation assistance functions, helping target users and engineers efficiently convey complex information (such as details of equipment malfunctions). This solves the limitations of traditional customer service, which relies solely on text / voice to convey professional issues. Ultimately, it forms a complete service chain from need access and engineer matching to meeting communication and result recording, avoiding the problems of "broken information transfer and no follow-up service" in traditional customer service, and improving overall consultation efficiency and user experience.

[0045] In one feasible implementation, the online consultation system for trusted data space further includes an engineer tagging and dynamic management module. The engineer tagging and dynamic management module is used to collect basic engineer information, past project experience and historical service records of multiple candidate engineers, analyze the matching domain tags corresponding to each candidate engineer, and perform many-to-many mapping between each candidate engineer and the matching neighborhood tags to generate an engineer profile corresponding to each candidate engineer.

[0046] It should be noted that the engineer tagging and dynamic management module is the core module responsible for building detailed engineer capability profiles in the online consultation system used in the trusted data space. Its core purpose is to address the pain point of traditional customer service's inability to accurately match professional engineers, providing the engineer recommendation module with engineer profiles that have clearly defined service scopes and capabilities, thus avoiding resource mismatch. Candidate engineers refer to all qualified professionals within the system, such as operations engineers, technicians, and business experts, and must be suitable for service needs in professional fields such as data element engineering and equipment maintenance. Basic engineer information refers to the candidate engineer's identity and contact data, including engineer ID (unique identifier), name, contact information, department, and professional skills certificates (such as network engineer certification, equipment repair qualifications, etc.). Past project experience refers to the candidate engineer's record of professional projects, such as participating in a data element platform fault repair project for a company in 2023 or being responsible for the maintenance of 100 office printers in 2024. This should include the project area, project responsibilities, and project deliverables, but there are no restrictions on this.

[0047] Furthermore, historical service records refer to the historical data of services provided by candidate engineers through the online consultation system used in the trusted data space. This includes the users served, the types of problems handled, the problem resolution rate, and user satisfaction, used for dynamic iterative evaluation of engineer capabilities. Matching domain tags refer to the professional service tags assigned to candidate engineers by the module, such as network failures, system optimization, office equipment repair, and data element engineering issues. These tags must precisely correspond to the problem type tags of the target users. Many-to-many mapping refers to the flexible mapping between candidate engineers and matching domain tags through database association tables. That is, one engineer can correspond to multiple tags (e.g., possessing both "network failure" and "system optimization" tags), and one tag can correspond to multiple engineers (e.g., multiple engineers possessing the "printer failure" tag). Engineer profiles refer to the structured engineer capability files output by the module, including basic engineer information, a set of matching domain tags, capability indicators (similar problem resolution rate, average response time), and real-time online status.

[0048] Specifically, the system obtains basic engineer information by connecting to the human resources system, past project experience by connecting to the project management system, and historical service records by connecting to the service record generation module, ensuring comprehensive data sources. Then, it uses keyword extraction algorithms (such as TF-IDF) to extract core domain keywords (such as "printer" and "troubleshooting") from project experience and historical service records, matches them with a preset tag library to determine matching domain tags, and achieves a many-to-many mapping between engineers and tags through a database association table. It integrates basic information, tags, and competency indicators to generate engineer profiles, which are synchronized to the preset dynamic engineer profile library in real time.

[0049] Furthermore, the core value of the module lies in "dynamic tag iteration," which differs from a static tag system: the module will periodically (e.g., weekly) update the matching domain tag weights of candidate engineers based on newly generated historical service records (e.g., if an engineer's recent "printer failure" problem resolution rate reaches 95%, then the tag weight will increase); if an engineer has not handled the problem corresponding to a certain tag for a long period of time (e.g., has not handled the "network failure" problem for 3 months), then the corresponding tag weight will decrease, ensuring that the engineer profile can reflect changes in service capabilities in real time.

[0050] In one possible implementation, this module can provide an "auto-label completion" function: when a candidate engineer participates in a new field project or handles a new type of problem, the module can automatically add corresponding matching field labels for him by analyzing the project description or problem labels, without the need for manual addition, so as to achieve a high degree of automation and high efficiency in profile generation.

[0051] This embodiment, through the design of an engineer tagging and dynamic management module, generates engineer profiles that include tags and capability indicators (such as the resolution rate of similar problems). This intuitively presents the engineer's service scope and professional level, providing accurate data support for the engineer recommendation module. It avoids recommendation bias caused by ambiguous information, ensuring that the professional problems of target users can be quickly matched with engineers with corresponding capabilities. This solves the pain point of "resource mismatch" in traditional customer service. The clear tag classification allows the engineer recommendation module to directly match the corresponding engineer according to the user's problem tags without having to screen them one by one, shortening the matching time and improving consultation efficiency.

[0052] In one feasible implementation, the online consultation system for trusted data space further includes a meeting anomaly handling module. The meeting operation and anomaly handling module is used to save the meeting interaction record and the current consultation progress when an anomaly signal is detected, and to re-establish the online meeting channel. If the reconstruction fails, a meeting anomaly record is generated, and an abnormal consultation channel is created based on the meeting interaction record, the current consultation progress, and the original participants.

[0053] It should be noted that the meeting anomaly handling module is the core module in the trusted data space online consultation system responsible for ensuring meeting continuity and preventing service interruptions due to abnormal interruptions. It ensures that even in the event of network failures or device offline issues, consultations between target users and engineers can continue, improving service stability. Anomaly signals refer to sudden conditions that cause the online meeting channel to be interrupted, including network interruption signals (such as a participant's client disconnecting from the signaling server), device offline signals (such as a target user's mobile phone being turned off), and system failure signals (such as a temporary downtime of the streaming media service). Anomalies can be detected through long-connection heartbeat packets.

[0054] Furthermore, meeting interaction records refer to all interaction data generated before the online meeting channel was interrupted, including text chat logs, speech-to-text, screen projection operation records, AR-annotated files, etc. Current consultation progress refers to the problem-solving stage at the time of meeting interruption, such as problem description completion, fault cause localization, solution demonstration, etc., which needs to be automatically marked by the module to provide context for subsequent channel reconstruction or creation of abnormal consultation channels. Online meeting channel refers to the original meeting link that the module needs to reconstruct, ensuring consistency with the original channel's functionality. Meeting anomaly records refer to the structured anomaly document generated by the module when channel reconstruction fails, including the anomaly meeting ID, anomaly time, anomaly reason, saved meeting interaction record links, and original participant information. Original participants refer to the participants in the online meeting channel before the abnormal interruption, i.e., the target user and target engineer. If multiple parties are involved, other collaborating engineers are also included; it must be ensured that the abnormal consultation channel can accurately associate with the original participants. Abnormal consultation channel refers to the alternative communication link created by the module for the original participants when rebuilding the online meeting channel fails. This can be achieved through methods such as phone callback, temporary WeChat conversations, email communication, etc., without restriction.

[0055] Specifically, the system prioritizes rebuilding the online meeting channel. Upon detecting an anomaly, the module immediately triggers the "data saving - channel rebuilding" process, saving the meeting interaction records to the database. Simultaneously, it calls the meeting API to recreate a meeting room with the same ID and automatically pushes the rebuilt room link to the original participants. If the participants reconnect within 1 minute, the current consultation progress is restored. If the rebuild fails (e.g., the participants' network is continuously interrupted), the system initiates the creation of an abnormal consultation channel. Based on the participants' contact information (e.g., the target user's phone number, the engineer's WeChat account), it automatically initiates a callback or pushes a temporary session invitation, synchronizing the saved meeting interaction records to ensure that participants can continue communicating based on the historical progress and avoid repeating the same description of the problem.

[0056] In one possible implementation, the meeting anomaly handling module can provide intelligent diagnosis of anomaly causes: by analyzing the characteristics of the anomaly signal (such as only the target user's end disconnecting or all participants disconnecting simultaneously), combined with network status monitoring data (such as network fluctuations in the target user's area), it can automatically diagnose the cause of the anomaly (such as unstable network on the target user's end or temporary failure of the system's streaming media service), and note it in the meeting anomaly record. At the same time, it can push an explanation of the cause of the anomaly to the original participants (such as "Your network fluctuations caused the meeting to be interrupted, and a call-back channel has been created for you"), thereby improving users' understanding and acceptance of anomaly handling.

[0057] This embodiment, through the design of a meeting anomaly handling module, avoids information loss due to network interruptions or equipment failures, solves the pain point of "repeated communication after abnormal interruption" in traditional consultations, ensures service continuity, and prioritizes re-establishing the online meeting channel, reusing the original meeting link parameters (such as room ID and participation permissions) to reduce service interruption time.

[0058] In one feasible implementation, the online consultation system for trusted data space further includes a multi-dimensional service quality assessment module. The multi-dimensional service quality assessment module is used to obtain structured service records, user satisfaction survey results, and engineer follow-up task completion status, calculate problem resolution rate, average service duration, user satisfaction score, and engineer's problem resolution rate for similar issues, and generate a multi-dimensional service quality report.

[0059] It should be noted that the multi-dimensional service quality assessment module refers to the core module responsible for quantitatively evaluating service effectiveness in the online consultation system used in the trusted data space. Structured service records refer to the basic data source acquired by the module, output by the service record generation module. User satisfaction survey results refer to the target users' evaluation data of the service; the system automatically pushes out a survey questionnaire after the meeting, including overall satisfaction, engineer professionalism rating, and problem-solving timeliness rating. Engineer follow-up task completion status refers to the execution data of engineers' subsequent follow-up tasks for unresolved issues, such as whether users were called back on time, whether the problem was ultimately resolved, and the follow-up duration. The problem resolution rate is calculated as "number of resolved service records ÷ total number of service records × 100%". The average service duration is calculated as "total duration of all normally concluded meetings ÷ number of normally concluded service records". The duration statistics range from the meeting start time to the normal meeting end time, excluding the duration of abnormally interrupted meetings to avoid affecting data accuracy.

[0060] Furthermore, user satisfaction scores are calculated using a weighted average, such as "overall satisfaction (60%) + professionalism score (20%) + timeliness score (20%)", with the score ranging from 1 to 5. The formula for calculating the engineer's problem-solving rate for similar issues is "number of resolved records for a certain type of problem (e.g., printer malfunction) by an engineer ÷ total number of records for that type of problem handled by the engineer × 100%". The multi-dimensional service quality report refers to the structured evaluation document output by the module, including overall service indicators (problem-solving rate, average service time, overall satisfaction), individual engineer indicators (problem-solving rate for similar issues, individual satisfaction), and typical case analyses (high-satisfaction cases, unresolved cases). The report must include charts (e.g., indicator trend charts, engineer capability radar charts) to ensure the evaluation results are intuitive and easy to understand.

[0061] Specifically, the system obtains structured service records by connecting to the service record generation module, obtains satisfaction results by connecting to the user survey system, and obtains follow-up task completion status by connecting to the engineer task management module to ensure comprehensive data. Then, it calculates multi-dimensional indicators according to preset formulas, integrates indicator data and typical cases, presents them in a chart format, generates a multi-dimensional service quality report, and synchronizes it to the system administrator, service data mining and system self-optimization module.

[0062] Furthermore, the module's evaluation cycle needs to support both "real-time" and "periodic" dimensions to meet the needs of different scenarios: Real-time evaluation means that for each structured service record generated, the module immediately calculates the corresponding single record indicators (such as the service duration and resolution status of the record) and updates the real-time indicator dashboard; Periodic evaluation means generating multi-dimensional service quality reports on a daily, weekly, and monthly basis to analyze service trends within the period (such as "this week's problem resolution rate has increased by 5% compared to last week") and changes in engineer capabilities (such as "Engineer Li's printer fault resolution rate has increased from 90% to 95%)", providing a basis for system administrators to adjust service resources (such as increasing the service allocation of highly capable engineers).

[0063] In one possible implementation, the multi-dimensional service quality assessment module can provide an alert function for abnormal indicators: preset thresholds for each assessment indicator (such as a minimum threshold of 85% for problem resolution rate and a maximum threshold of 30 minutes for average service time). When a periodic indicator is lower or higher than the threshold (such as "average service time this week is 35 minutes > 30 minutes"), the module automatically triggers an alert, pushes the alert information to the system administrator, analyzes the cause of the abnormality (such as "new engineers are not proficient in service, resulting in increased time"), and proposes optimization suggestions (such as "arrange for new engineers to participate in printer fault repair training").

[0064] This embodiment transforms the vague concept of "service quality" into measurable data by calculating core indicators such as problem resolution rate and average service duration. This avoids the subjective bias of traditional evaluations, provides an objective basis for the system's service effectiveness, and also clearly distinguishes the professional advantages of different engineers.

[0065] In one feasible implementation, the online consultation system for trusted data space further includes a service data mining and system self-optimization module. The service data mining and system self-optimization module is used to analyze historical service records and multi-dimensional service quality reports to mine engineer tag optimization points; synchronize the engineer tag optimization points to the preset engineer dynamic profile library; and obtain historical service records and user feedback to fine-tune the online consultation model.

[0066] It should be noted that the Service Data Mining and System Self-Optimization module refers to the core module in the online consultation system used in the trusted data space, responsible for driving continuous system iteration and improving the intelligence level of services. Engineer tag optimization points refer to the specific directions for adjusting engineer tags discovered by the module through data mining, including tag addition, tag weight adjustment, and tag deletion. User feedback refers to the evaluations and suggestions provided by target users after the service is completed, used to pinpoint the optimization direction of the online consultation model.

[0067] Specifically, by analyzing the correlation between engineer tags and problem resolution rates in historical service records (e.g., engineers with the 'HP printer repair' tag have a 15% higher resolution rate than those with only the 'printer malfunction' tag), we can identify areas for tag optimization (e.g., suggesting adding the 'HP printer repair' tag to more engineers). This information is then synchronized to a pre-defined dynamic engineer profile database. For example, we can use association rule algorithms (such as Apriori) to analyze the correlation between "engineer tags and problem resolution rates" in historical service records, and use error case analysis to pinpoint the parsing defects of the online consultation model and identify areas for optimization. Furthermore, by analyzing requirement cases where the model parses incorrectly in historical service records and "problem descriptions that the model cannot understand" in user feedback, we can extract optimization data, fine-tune the online consultation model, and improve the accuracy of requirement parsing.

[0068] In one possible implementation, the module can provide an optimization effect verification function: after pushing the engineer tag optimization points to the preset engineer dynamic profile library, it tracks the change in the engineer's similar problem resolution rate within the next month; after fine-tuning the online consultation model, it calculates the subsequent demand parsing accuracy rate; if the optimization effect does not meet expectations, it re-analyzes the data and adjusts the optimization points to ensure the effectiveness of self-optimization.

[0069] This embodiment avoids the disconnect between static labels and engineer capabilities, ensuring more accurate subsequent engineer recommendations. At the same time, it eliminates the need for manual adjustment of labels and model parameters, as the module automatically completes the entire process of mining, optimization, and synchronization, reducing manual maintenance costs and allowing the system to continuously evolve as service data accumulates.

[0070] In one feasible implementation, the online consultation system for trusted data space further includes a multimodal interaction adaptation module, which is used to receive text, voice streams, equipment fault screenshots, equipment real-scene images and 3D modeling transmitted in the online conference channel, and generate consultation process data. It should be noted that the multimodal interaction adaptation module refers to the core module in the online consultation system used in the trusted data space, responsible for integrating various interaction forms such as text, voice, and images to generate structured consultation process data. Through multimodal data integration, it enables target users and target engineers to convey complex information more intuitively and efficiently. Text refers to the text information input by target users and target engineers in the online meeting channel. Voice stream refers to the real-time voice data transmitted by participants through microphones. Equipment fault screenshots refer to static images taken by target users regarding equipment malfunctions (such as photos of foreign objects in the printer's paper feed tray). Live equipment images refer to dynamic images of the malfunctioning equipment taken by target users through the equipment's camera (such as a real-time display of the printer's paper feeding process). Compared to static screenshots, these images better reflect the dynamic process of the malfunction (such as the "paper feeding action at the moment of a paper jam"). The module needs to support real-time image streaming to ensure that target engineers can simultaneously observe the equipment status.

[0071] Furthermore, 3D modeling refers to the pre-built 3D structural models (such as printer assembly models and server internal structure models) for complex equipment (such as industrial machinery and servers). These models are stored in advance by the system according to the equipment model. Target users can call the corresponding 3D model based on the faulty equipment model. The module needs to support model loading and real-time interaction to provide a carrier for subsequent fault annotation. Consultation process data refers to the structured data set output by the module, including multimodal raw data, data association information (such as the text transcription of a certain audio segment, the fault description corresponding to a certain screenshot), and timeline markers.

[0072] Specifically, the module receives text, voice streams, equipment fault screenshots / real-world images of equipment, and 3D model models by connecting to the data stream interface of the online meeting channel. It assigns a unique data identifier to each type of data to ensure traceability. Then, it links the various types of data along a timeline, performs real-time text-to-speech processing on the voice streams, and binds the 3D model models to the corresponding equipment models to form structured data entries of "time - data type - data content - association identifier". Finally, it integrates all structured data entries to generate consultation process data containing multimodal data links and relationships.

[0073] In one possible implementation, the module can provide multimodal data compression and encryption functions: for high-definition equipment fault screenshots, equipment real-scene images, and 3D modeling, lightweight compression algorithms (such as JPEG 2000 and GLB format compression) are used to reduce data transmission bandwidth usage and ensure smooth online meeting channels; at the same time, the transmitted multimodal data is encrypted (such as using the AES encryption algorithm) to prevent the leakage of equipment fault details and user privacy information.

[0074] The multimodal interaction adaptation module is also used to analyze the fault features in the device fault screenshot using image recognition technology, and align the fault features with the 3D model modeling, allowing the target engineer to annotate the 3D model modeling; and to call the screen projection interface to synchronize the target engineer's operation demonstration to the online meeting channel.

[0075] It should be noted that image recognition technology refers to the algorithm used by the module to analyze screenshots of equipment malfunctions. This algorithm must be pre-trained with models based on equipment malfunction cases in a specialized field, possessing the ability to identify the location and type of malfunction to ensure the professionalism and accuracy of the analysis results. Fault characteristics refer to the key fault information extracted from the screenshots by image recognition technology, such as "Fault location: printer paper feed, Fault type: foreign object residue (paper fragments), Fault severity: minor (undamaged parts)." The screen sharing interface refers to the technical interface called by the module to achieve screen sharing. Target engineers can use this interface to synchronize operation demonstrations on their local devices (such as "3D model annotation process" or "animated repair steps") to the online meeting channel, ensuring that target users can clearly observe each step of the operation and avoid misunderstandings caused by vague text descriptions. Operation demonstrations refer to the real-time operations performed by target engineers to convey solutions, such as "demonstrating the steps of disassembling the paper feed cover on a 3D model" or "playing an animated tutorial on cleaning foreign objects from the printer paper feed." The module must ensure the real-time nature and clarity of the demonstration footage.

[0076] Specifically, the module calls a pre-trained image recognition model to extract fault features from equipment fault screenshots. Through a coordinate mapping algorithm (associating the pixel coordinates of the screenshot with the spatial coordinates of the 3D model), the fault features are precisely aligned to the corresponding parts of the 3D model, generating "fault feature - model part" correlation data. At the same time, the module provides the target engineer with a visual annotation tool. The engineer can select the fault area on the 3D model, add text descriptions (such as "There is a piece of paper here"), and draw maintenance guide lines (such as "Remove the cover in this direction"). The annotation content is converted into digital signals in real time and synchronized to the target user's client interface through an online conference channel.

[0077] Furthermore, the target engineer initiates an operation demonstration (such as demonstrating the 3D model annotation process or playing maintenance animations). The module calls the screen projection interface to transmit the demonstration screen to the online conference channel in real time, while ensuring that the demonstration screen is synchronized with the engineer's voice explanation. The target user can watch the demonstration in real time on the client and provide real-time feedback via text / voice if there are any questions, forming a closed loop of analysis-annotation-demonstration-interaction.

[0078] In one possible implementation, the module can provide an automatic solution matching function based on fault characteristics: after parsing the fault characteristics, the module automatically connects to the solution library, matches the optimal solution steps for similar faults, and pushes the solution to the target engineer in the form of "text + 3D model animation". The engineer can directly annotate and demonstrate based on the solution, reducing the time spent manually searching for solutions and further improving service efficiency.

[0079] Based on this, embodiments of this application provide an online consultation method for a trusted data space, referring to... Figure 5 , Figure 5 This is a flowchart illustrating an embodiment of the online consultation method for trusted data spaces provided in this application.

[0080] In this embodiment, the online consultation method for trusted data spaces includes steps S71-S74: Step S71: Obtain the standardized question consultation card corresponding to the target user through the online consultation model adapted to the trusted data space data interaction protocol; Step S72: Match the standardized problem consultation card with the preset engineer dynamic profile database to determine the target engineer; Specifically, a multi-dimensional weighted matching algorithm is used. Based on the problem type tags of standardized problem consultation cards, candidate engineers with the same or related tags are selected from the preset dynamic engineer profile library. Then, the candidate engineers are weighted and scored according to their problem-solving rate, average response time, and user satisfaction, and a ranking result is generated. The engineer with the highest score and currently online is selected as the target engineer. If the engineer is busy, the engineer is replaced in order of ranking to ensure that an available engineer is quickly matched and to ensure the timeliness of the matching result.

[0081] Step S73: Establish an online meeting channel between the target user and the target engineer based on the trusted identity authentication mechanism of the trusted data space, and provide online consultation assistance function for the online meeting channel; Specifically, the system uses the trusted authentication mechanism of the trusted data space to call the meeting API to create temporary meeting rooms, automatically send meeting invitations to target users (both the target engineers), verify their identities, and connect them to the online meeting channel to ensure link security; it automatically activates corresponding auxiliary functions based on the question type of the standardized question card (such as loading image annotation tools for equipment failure issues and screen sharing functions for system configuration issues); the module synchronizes the audio and video streams, text messages, and auxiliary function operations of both parties in real time, and also connects to the meeting anomaly handling module to ensure rapid recovery in the event of channel anomalies.

[0082] Step S74: Obtain the consultation process data generated by the target user and the target engineer in the online meeting channel, and generate the online consultation result.

[0083] This embodiment forms a complete service chain from demand access and engineer matching to meeting communication and result recording, thereby improving overall consultation efficiency and user experience.

[0084] In one feasible implementation, matching the standardized problem consultation card with a preset dynamic engineer profile database to identify the target engineer includes: Step S81: Based on the user profile tags and user intent tags of the standardized question consultation card, select several candidate engineers whose tag matching degree meets the preset threshold from the preset engineer dynamic profile library. It's important to note that user profile tags refer to the user characteristic tags included in the standardized problem consultation card, while user intent tags refer to the core requirement tags extracted from user needs. Together, they form the core basis for matching, ensuring that the selected candidate engineers are both compatible with user attributes and can meet specific needs. The preset threshold is a quantitative standard for selection (e.g., tag matching degree ≥ 70%), preventing engineers with low matching degrees from entering the candidate pool and reducing subsequent waste of computational resources.

[0085] Specifically, the user profile tags and user intent tags of the standardized problem consultation cards are converted into vectors; the engineer tag vectors are extracted from the preset engineer dynamic profile library, and then the cosine similarity (i.e. tag matching degree) between the user tag vector and the engineer tag vector is calculated. Engineers with similarity ≥ preset threshold are selected as candidate engineers to ensure that the engineers in the candidate pool have basic service capabilities.

[0086] Step S82: Based on the tag matching degree and the similar problem-solving rate and response speed of each candidate engineer, calculate the consultation recommendation score corresponding to each candidate engineer and generate an engineer recommendation list; It's important to note that tag matching is the base score (reflecting the engineer's suitability for the needs), similar problem resolution rate is the capability score (reflecting service quality), and response speed is the efficiency score (reflecting service timeliness). These three factors combine to form a multi-dimensional evaluation system, avoiding the problem of accurate matching but poor service caused by a single tag. The consultation recommendation score is a comprehensive quantitative result, calculated using a weighted algorithm; a higher score indicates a more suitable engineer for the current needs. The engineer recommendation list is a sorted list of candidate engineers, arranged from highest to lowest recommendation score.

[0087] Specifically, the system obtains the tag matching degree, similar problem resolution rate, and response speed of candidate engineers, calculates the recommendation score according to the preset weight, and sorts all candidate engineers in descending order of recommendation score to generate an engineer recommendation list containing engineer ID, recommendation score, and core tags. The system also marks the real-time online status of engineers to ensure that the sorting results reflect both suitability and real-time service capabilities.

[0088] Step S83: Determine the target engineer according to the engineer sorting in the engineer recommendation list.

[0089] Specifically, the online status of the first engineer in the engineer recommendation list is checked (by connecting to the real-time status interface of the engineer tagging and dynamic management module). If the engineer is "idle", he is directly identified as the target engineer and a service invitation is sent to him. If the first engineer is "busy", the subsequent engineers in the list are checked in turn, and the first "idle" engineer is selected as the target engineer. In the fourth step, if all candidate engineers are busy, a "queue waiting list" is generated by sorting them according to the recommendation score, and the estimated waiting time is fed back to the user to ensure a smooth service process.

[0090] This embodiment uses user profile tags and intent tags for initial screening to quickly identify candidate engineers highly relevant to the needs, preventing irrelevant engineers from entering the recommendation process, reducing resource waste, and improving matching efficiency. At the same time, it combines tag matching degree, similar problem resolution rate, and response speed to calculate the recommendation score, taking into account suitability, professional ability, and service efficiency, avoiding the problem of "accurate matching but poor service" caused by a single dimension evaluation, and ensuring that the recommended engineers are of overall excellence.

[0091] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0092] This application provides an online consultation device for a trusted data space, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the online consultation method for a trusted data space described in Embodiment 1 above.

[0093] The following is for reference. Figure 6This document illustrates a structural schematic diagram of an online consultation device suitable for implementing embodiments of this application in a trusted data space. The online consultation device for a trusted data space in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 6 The online consultation device for trusted data spaces shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0094] like Figure 6 As shown, the online consultation device for trusted data space may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory 1002 or a program loaded from storage device 1003 into random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of the online consultation device for trusted data space. The processing unit 1001, read-only memory 1002, and random access memory 1004 are interconnected via bus 1005. Input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to input / output interface 1006: input devices 1007 including, for example, touch screens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows online consultation devices for trusted data spaces to communicate wirelessly or wiredly with other devices to exchange data. While the figures show online consultation devices for trusted data spaces with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0095] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0096] The online consultation device for trusted data spaces provided in this application, employing the online consultation method for trusted data spaces described in the above embodiments, can solve the technical problems in the background art. Compared with the prior art, the beneficial effects of the online consultation device for trusted data spaces provided in this application are the same as the beneficial effects of the online consultation method for trusted data spaces provided in the above embodiments, and other technical features in the online consultation device for trusted data spaces are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0097] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0098] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0099] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the online consultation method for a trusted data space as described in the above embodiments.

[0100] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0101] The aforementioned computer-readable storage medium may be included in an online consultation device for a trusted data space; or it may exist independently and not assembled into an online consultation device for a trusted data space.

[0102] The aforementioned computer-readable storage medium carries one or more programs that, when executed by an online consultation device in a trusted data space, cause the online consultation device in the trusted data space to: Obtain standardized question cards for target users through an online consultation model; The standardized problem consultation cards are matched with a preset dynamic engineer profile database to identify the target engineer; Establish an online meeting channel between the target user and the target engineer, and provide online consultation assistance functions for the online meeting channel; The system acquires consultation process data generated between the target user and the target engineer in the online meeting channel and generates online consultation results.

[0103] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0106] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described online consultation method for a trusted data space, thereby solving the technical problems in the background art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the online consultation method for a trusted data space provided in the above embodiments, and will not be repeated here.

[0107] This application provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the online consultation method for a trusted data space as described above.

[0108] The computer program product provided in this application can solve the technical problems in the background art. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiments of this application are the same as the beneficial effects of the online consultation method for trusted data space provided in the above embodiments, and will not be repeated here.

[0109] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An online consultation system for a trusted data space, characterized in that, The system includes a multi-channel user demand access and preprocessing module, an engineer recommendation module, a multi-party meeting initiation and assistance module, and a service record generation module. The multi-channel user demand access and preprocessing module is used to obtain standardized question consultation cards corresponding to target users through an online consultation model adapted to the trusted data space data interaction protocol. The engineer recommendation module is used to match the standardized question and consultation card with a preset dynamic engineer profile database to determine the target engineer; The multi-party meeting initiation and assistance module is used to establish an online meeting channel between the target user and the target engineer based on the trusted identity authentication mechanism of the trusted data space, and to provide online consultation assistance functions for the online meeting channel. The service record generation module is used to acquire consultation process data generated by the target user and the target engineer in the online meeting channel, and generate online consultation results.

2. The online consultation system for a trusted data space as described in claim 1, characterized in that, The online consultation system for trusted data space also includes an engineer tagging and dynamic management module. This module collects basic engineer information, past project experience, and historical service records of multiple candidate engineers to analyze the matching domain tags corresponding to each candidate engineer. It then performs a many-to-many mapping between each candidate engineer and the matching neighborhood tags to generate an engineer profile corresponding to each candidate engineer.

3. The online consultation system for a trusted data space as described in claim 1, characterized in that, The online consultation system for trusted data space also includes a meeting anomaly handling module. The meeting operation and anomaly handling module is used to save the meeting interaction record and the current consultation progress when an anomaly signal is detected, and to re-establish the online meeting channel. If the reconstruction fails, an abnormal meeting record is generated, and an abnormal consultation channel is created based on the meeting interaction record, the current consultation progress, and the original participants.

4. The online consultation system for a trusted data space as described in claim 1, characterized in that, The online consultation system for trusted data space also includes a multi-dimensional service quality assessment module. This module is used to obtain structured service records, user satisfaction survey results, and engineer follow-up task completion status, calculate problem resolution rate, average service duration, user satisfaction score, and engineer's problem resolution rate for similar issues, and generate a multi-dimensional service quality report.

5. The online consultation system for a trusted data space as described in claim 1, characterized in that, The online consultation system for trusted data space also includes a service data mining and system self-optimization module. This module is used to analyze historical service records and multi-dimensional service quality reports to identify engineer tag optimization points; synchronize these engineer tag optimization points to the preset engineer dynamic profile library; and obtain historical service records and user feedback to fine-tune the online consultation model.

6. The online consultation system for a trusted data space as described in claim 1, characterized in that, The online consultation system for trusted data space also includes a multimodal interaction adaptation module, which is used to receive text, voice streams, equipment fault screenshots, equipment real-scene images and 3D modeling transmitted in the online conference channel, and generate consultation process data. The multimodal interaction adaptation module is also used to analyze the fault features in the device fault screenshot through image recognition technology, and align the fault features with the three-dimensional model modeling, supporting the target engineer to annotate the three-dimensional model modeling; The screen projection interface is invoked to synchronize the target engineer's operation demonstration to the online meeting channel.

7. An online consultation method for a trusted data space, characterized in that, include: A standardized question and consultation card corresponding to the target user is obtained through an online consultation model that adapts to the trusted data space data interaction protocol; The standardized problem consultation cards are matched with a preset dynamic engineer profile database to identify the target engineer; An online meeting channel is established between the target user and the target engineer based on a trusted identity authentication mechanism in a trusted data space, and online consultation assistance is provided for the online meeting channel. The system acquires consultation process data generated between the target user and the target engineer in the online meeting channel and generates online consultation results.

8. The online consultation method for a trusted data space as described in claim 7, characterized in that, The step of matching the standardized problem consultation card with a preset dynamic engineer profile database to identify the target engineer includes: Based on the user profile tags and user intent tags of the standardized question consultation card, several candidate engineers whose tag matching degree meets the preset threshold are selected from the preset engineer dynamic profile library; Based on the tag matching degree and the problem-solving rate and response speed of each candidate engineer, calculate the consultation recommendation score corresponding to each candidate engineer and generate an engineer recommendation list; The target engineer is determined by sorting the engineers in the engineer recommendation list.

9. An online consultation device for a trusted data space, characterized in that, The online consultation device for a trusted data space includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the online consultation method for a trusted data space as described in any one of claims 7 to 8.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the online consultation method for a trusted data space as described in any one of claims 7 to 8.

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