Online and offline business cooperation optimization method and system of service platform

Abstract

The application provides an online and offline business cooperation optimization method and system of a service platform, relates to the technical field of business management, and comprises the following steps: receiving natural language input data of a user, and constructing a ternary semantic mapping of an intention-transaction-path; activating a multi-source perception channel of the service platform, performing real-time perception of an online and offline state, establishing a state-service limiting factor; performing service scene perception, and configuring a multi-objective optimization channel; taking the ternary semantic mapping of the intention-transaction-path and the state-service limiting factor as inputs, performing path optimization reconstruction, and establishing a path optimization reconstruction result; and performing online and offline business cooperation optimization management. The application solves the technical problem of low service efficiency caused by the fragmentation of online and offline processes and different standards in the prior art, realizes dynamic cooperation reconstruction and intelligent scheduling of online and offline services through semantic recognition and multi-objective optimization, and enhances service quality and efficiency.

Description

Technical Field

[0001] This application relates to the field of business management technology, and in particular to methods and systems for optimizing online and offline business collaboration on service platforms. Background Technology

[0002] Existing service platforms generally adopt an architecture that separates online operations from offline processing, leading to severe process discontinuities and data sharing obstacles, becoming a key technical bottleneck restricting service efficiency. On the one hand, online and offline service processes are fragmented. Due to the lack of unified data models, interface specifications, and process definition standards during the construction of service platforms in different regions and departments, there are significant differences in the technical implementation of service process design and approval procedures between online platforms and offline windows. After completing an application through online channels, physical materials still need to be submitted repeatedly or form data needs to be re-entered at the offline window, resulting in redundant execution of business logic and unnecessary duplication of process nodes, significantly increasing operational burden and processing time. On the other hand, the heterogeneity of online and offline standard systems hinders collaboration. The lack of standardization and uniformity in material formats and business statuses between different service platforms makes it difficult to seamlessly connect cross-channel data and prevents mutual recognition of service procedures, severely reducing processing efficiency. In addition, existing service platforms generally adopt static configuration processes, lacking the ability to monitor business processes in real time, optimize them dynamically, and adapt them to individual needs. This makes it difficult to meet the increasingly diversified and complex service demands and also limits the ability to coordinate and allocate online and offline service resources, resulting in uneven utilization or idle service resources, which further restricts the improvement of overall service efficiency.

[0003] In summary, existing technologies suffer from low service efficiency due to the disconnect between online and offline processes and inconsistent standards. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for optimizing online and offline business collaboration on a service platform, in order to solve the technical problem in the prior art that the service efficiency is low due to the disconnect between online and offline processes and inconsistent standards.

[0005] In view of the above problems, this application provides a method and system for optimizing online and offline business collaboration on a service platform.

[0006] Firstly, this application provides a method for optimizing online and offline business collaboration on a service platform. This method is implemented through an online and offline business collaboration optimization system for the service platform. The method includes: receiving natural language input data from a user; performing semantic recognition and item extraction based on the natural language input data; constructing a ternary semantic mapping of intent-item-path based on a service knowledge graph; activating the service platform's multi-source perception channels; performing real-time online and offline state perception; establishing state-service constraint factors; performing service scenario perception; configuring multi-objective optimization channels using the service scenario perception results; using the ternary semantic mapping of intent-item-path and the state-service constraint factors as input; performing path optimization and reconstruction through the multi-objective optimization channels; and establishing path optimization and reconstruction results; and performing online and offline business collaboration optimization management based on the path optimization and reconstruction results.

[0007] Optionally, the natural language input data is preprocessed, including word segmentation preprocessing, standardization processing, named entity recognition processing, and sentiment word recognition processing; based on the text preprocessing results, intent recognition, entity recognition, and relationship extraction are performed to construct a ternary semantic mapping of intent-item-path.

[0008] Optionally, the online sensing channel of the service platform is used to perform online business accessibility sensing to establish a first state-service limitation factor; the image sensing channel of the service platform is used to perform real-time load sensing of offline windows to establish a second state-service limitation factor; the additional sensing channel of the service platform is used to perform location accessibility and historical complaint data sensing to establish a third state-service limitation factor; and a state-service limitation factor is established based on the first state-service limitation factor, the second state-service limitation factor, and the third state-service limitation factor.

[0009] Optionally, time node feature analysis is performed on the online and offline service platforms, and a first matching feature is established based on the time node feature analysis results; historical service data from online and offline are obtained, and after data fusion of the historical service data, service status trend matching is performed based on the first matching feature to establish a service status trend matching result; the real-time status perception result is used as the actual status, and status-service constraint prediction based on the service status trend matching result is performed to establish a compensation constraint factor; the status-service constraint factor is updated based on the compensation constraint factor.

[0010] Optionally, a global goal planning for the service platform is established, and a global perception layer for multi-objective optimization channels is configured according to the global goal planning; the service scenario perception results are extracted into service scenario feature vectors, which include time features, resource features, user urgency features, geographical accessibility features, and service quality features; a weight reconstruction of the multi-objective optimization layer is established according to the service scenario feature vectors; and the channel configuration of the multi-objective optimization channels is completed using the multi-objective optimization layer and the global perception layer.

[0011] Optionally, a virtual identity for the user is established based on the user's behavioral profile, and the virtual identity is used to perform virtual simulation execution of the non-path optimization reconstruction result to establish virtual simulation execution feedback; real user execution feedback based on the path optimization reconstruction result is obtained; dual feedback optimization is performed using the virtual simulation execution feedback and the real user execution feedback, and the multi-objective optimization channel is compensated based on the dual feedback optimization result.

[0012] Optionally, the event is decomposed according to the ternary semantic mapping of intent-event-path to establish N atomic events; the dependency relationship between the N atomic events is identified to establish a directed service chain structure; the path segments are segmented for optimization based on the N atomic events, the directed service chain structure, and the state-service constraint factor according to the multi-objective optimization channel to establish the segmented optimization result; and the path optimization reconstruction result is established according to the segmented optimization result.

[0013] Optionally, the global integration layer of the multi-objective optimization channel is invoked, and after receiving the segmented optimization results, the global integration layer performs execution collaboration analysis on the path segments; and the path optimization reconstruction results are established based on the execution collaboration analysis results.

[0014] Optionally, a synergy effect threshold is established, and the synergy effect threshold is used to evaluate the coordination of the segmented optimization results. When the coordination of the segmented optimization results meets the synergy effect threshold, the optimal result of the corresponding segmented optimization result is eliminated to establish the path optimization reconstruction result.

[0015] Secondly, this application also provides an online-offline business collaboration optimization system for a service platform, used to execute the online-offline business collaboration optimization method for a service platform as described in the first aspect. The online-offline business collaboration optimization system for the service platform includes: a natural language processing module, used to receive natural language input data from users, perform semantic recognition and item extraction based on the natural language input data, and construct a ternary semantic mapping of intent-item-path based on a service knowledge graph; a real-time state perception module, used to activate the multi-source perception channels of the service platform, perform real-time state perception of online and offline operations, and establish state-service constraint factors; a service scenario perception module, used to perform service scenario perception and configure multi-objective optimization channels using the service scenario perception results; a path optimization and reconstruction module, used to take the ternary semantic mapping of intent-item-path and the state-service constraint factors as input, perform path optimization and reconstruction through the multi-objective optimization channels, and establish path optimization and reconstruction results; and a collaboration management module, used to perform online-offline business collaboration optimization management based on the path optimization and reconstruction results.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects:

[0017] By receiving users' natural language input data, performing semantic recognition and item extraction based on the input data, and constructing a ternary semantic mapping of intent-item-path based on a service knowledge graph; activating the service platform's multi-source perception channels to perform real-time online and offline status perception and establish status-service constraint factors; performing service scenario perception and configuring multi-objective optimization channels using the service scenario perception results; using the intent-item-path ternary semantic mapping and the status-service constraint factors as input, performing path optimization and reconstruction through the multi-objective optimization channels, and establishing path optimization and reconstruction results; and performing online and offline business collaborative optimization management based on the path optimization and reconstruction results. In other words, by performing semantic recognition and item extraction on users' natural language input, constructing an intent-item-path ternary semantic mapping, performing online and offline status perception through multi-source perception channels, configuring multi-objective optimization channels based on service scenario perception results, and replanning service paths, dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes are achieved, improving service quality and efficiency.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in 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, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the online and offline business collaboration optimization method for the service platform of this application.

[0021] Figure 2 This is a schematic diagram of the structure of the online and offline business collaboration optimization system for the service platform of this application.

[0022] Figure labeling: Natural Language Processing Module 11, Real-time Status Awareness Module 12, Service Scenario Awareness Module 13, Path Optimization and Reconstruction Module 14, Collaborative Management Module 15. Detailed Implementation

[0023] This application addresses the technical problem of low service efficiency caused by fragmented and inconsistent online and offline processes in existing technologies by providing a method and system for optimizing online and offline business collaboration on a service platform. By performing semantic recognition and item extraction on user-input natural language, a three-dimensional semantic mapping of intent-item-path is constructed. Online and offline status is perceived through multi-source perception channels, and multi-objective optimization channels are configured based on the service scenario perception results to replan service paths. This achieves dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes, improving service quality and efficiency.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for optimizing online and offline business collaboration of a service platform. The method is executed through an online and offline business collaboration optimization system of the service platform, and specifically includes the following steps:

[0026] S100: Receive the user's natural language input data, perform semantic recognition and item extraction based on the natural language input data, and construct a ternary semantic mapping of intent-item-path based on the service knowledge graph.

[0027] Specifically, the system receives natural language input data from users, including data input via voice or text, such as inquiries and applications submitted on government service platforms. This input typically expresses the user's needs in natural language. The system first preprocesses the user's natural language input, including word segmentation, standardization, named entity recognition, and sentiment analysis. Then, it performs semantic recognition and item extraction on the preprocessed natural language input data. Semantic recognition uses language processing techniques to identify semantic information in the natural language input data, including intent, entities, and relationships. Item extraction refers to extracting specific business items from the natural language input data, such as service guides and application conditions.

[0028] A service knowledge graph is a knowledge base represented by a graph structure that stores various data and business rules within the service domain, including information such as concepts, entities, and relationships related to government services. Service knowledge graphs organize and represent various knowledge, concepts, and relationships through nodes and edges, helping to understand the knowledge structure and connections within the service domain. Based on the identified intents and matters, a ternary semantic mapping of intent-matter-path is constructed using the service knowledge graph. This maps the user's natural language intent to specific business matters and specifies the actual processing path for that matter. The service knowledge graph maps the identified intents and matters to corresponding nodes in the graph. For example, if the user's natural language input is "[I want to apply for a certificate, what are the procedures]", the user's intent is to apply for a certificate, the matter is a certificate, and the path is: identity verification → acceptance of matter A → receipt of materials → processing of matter A → acceptance of matter B → processing of matter B.

[0029] By employing semantic recognition and ternary semantic mapping based on service knowledge graphs, the system accurately identifies users' intentions from their natural language input and maps them to specific service items. It then automatically generates a processing path related to that item, guiding users on how to proceed. Based on ternary semantic mapping, the service platform can automatically identify and push service paths relevant to user needs, reducing user waiting time and manual intervention, and improving overall efficiency.

[0030] Furthermore, this application S100 includes:

[0031] The natural language input data is preprocessed, including word segmentation, standardization, named entity recognition, and sentiment word recognition. Based on the preprocessing results, intent recognition, entity recognition, and relationship extraction are performed to construct a ternary semantic mapping of intent-item-path.

[0032] Specifically, text preprocessing is performed on the natural language input data from users, cleaning and formatting the input text to make it more standardized. Text preprocessing includes word segmentation preprocessing, standardization processing, named entity recognition processing, and sentiment word recognition processing. Word segmentation preprocessing involves breaking down the text into individual word units for better understanding and analysis of the text content. For example, "I want to apply for a license" after word segmentation preprocessing yields [I, want, apply, license]. Standardization processing involves uniformly formatting non-standard parts of the text, standardizing proper nouns and abbreviations to ensure consistency in different expressions. Named entity recognition processing is a natural language processing technique that identifies proper nouns in the text, such as locations, times, people, organizations, etc. Sentiment word recognition determines the user's emotional state or attitude, such as positive or negative, by identifying emotional words in the text.

[0033] Based on the text preprocessing results, intent recognition is performed to identify the user's needs and intentions, such as applying for a certificate or license. Entity recognition is also performed to identify key entities in the text, including the service recipient, time, and location, such as the certificate or license itself. Relationship extraction is then performed to extract the relationships between entities, such as the association between the service guide and the application requirements. A ternary semantic mapping of intent-item-path is constructed, where intent is the user's need or purpose (e.g., applying for a certificate or license); item is the specific business or transaction (e.g., certificate or license); and path is the operational process required to complete the item (e.g., submitting identity verification materials → filling out an application form → waiting for review).

[0034] By accurately understanding and processing users' natural language input data, the system can accurately extract users' intentions and tasks, and map them to specific service paths. Based on the ternary semantic mapping of intention, task, and path, it can automatically recommend service processes that match users' needs, avoiding manual intervention.

[0035] S200: Activate the multi-source perception channels of the service platform, perform real-time online and offline status perception, and establish status-service constraint factors.

[0036] Furthermore, this application S200 includes:

[0037] Utilizing the online sensing channel of the service platform, online business accessibility is sensed to establish a first state - service limitation factor; utilizing the image sensing channel of the service platform, real-time load sensing of offline windows is performed to establish a second state - service limitation factor; utilizing the additional sensing channel of the service platform, location accessibility and historical complaint data sensing are performed to establish a third state - service limitation factor; and a state - service limitation factor is established based on the first state - service limitation factor, the second state - service limitation factor, and the third state - service limitation factor.

[0038] Specifically, this involves activating multi-source sensing channels, which are channels that perceive service status in real time through multiple data sources, including online sensing channels, image sensing channels, and additional sensing channels. The online sensing channel is a sensing module that monitors the operational data of the online service platform to obtain information such as its availability, load, and response time. Through the service platform's online sensing channel, online business accessibility is perceived, indicating whether users can successfully access and use online business functions, including access latency, network response, and service openness. Online business accessibility refers to whether users can successfully complete the required online business operations, including whether they can log in to the system, access the corresponding modules, submit materials, and obtain processing results. The online sensing channel attempts to access online services by monitoring system interfaces (such as API status and service monitoring programs). The first limiting factor is established using the following indicators: response time, number of users in the queue, error code frequency, service availability status, and maintenance / downtime status. For example, if a certificate processing service returns an HTTP 503 error for 5 consecutive minutes, with an average response time exceeding 5000ms, and the current number of people in the queue is 42, it is judged as having low accessibility and its status is recorded as unreachable. The first state-service limiting factor is a state variable extracted from the online perception channel, used to reflect the current availability and performance limitations of online services.

[0039] Utilizing the service platform's image sensing channel, real-time load perception of offline windows is achieved. This includes capturing video streams from offline windows via cameras and analyzing them using image processing algorithms to determine the real-time load and establish a second-state service constraint factor. High-definition cameras are installed above each service window on the service platform to capture video streams of queuing and waiting areas. Existing image recognition algorithms are used to analyze the video streams, identifying the number of people, the number of open windows, and the service processing speed. Based on the busyness of the offline windows, a second-state service constraint factor is established. For example, if 18 people are currently queuing at a window, and the historical service rate is 3.5 minutes per person, the estimated waiting time is 63 minutes. If the load scoring model is max(0.2, α(1-T / T)... max )+β(1-P), where T is the expected waiting time; T maxThe maximum waiting time is 60 minutes; 0.2 is the minimum score, a lower limit set to prevent a score of 0 from completely excluding the window; P is the window's historical failure rate (e.g., the proportion of business interruptions or transfers in the past 7 days); α and β are adjustable weights, α+β=1. The current window's historical failure rate is 15%, and with α=0.6 and β=0.4, the focus is more on queuing delay. Based on the load scoring model, the current load is 0.31. Although the estimated queuing time slightly exceeds the maximum waiting time, the window is still considered valuable due to its low failure rate (only 15%). In multi-window scheduling, if other windows generally have scores below 0.3, the window may still be recommended; otherwise, it will be downgraded. The second state—service constraint factor—refers to the service availability index extracted based on the offline window load perception results. It represents the degree of constraint the current state places on the service, including window number, window location, number of people queuing, estimated waiting time, real-time load, etc., reflecting the current load status and service pressure of the offline service window.

[0040] Utilizing the service platform's additional sensing channels, location accessibility and historical complaint data analysis are performed. By using the user's current location and the target service window's geographic location, and leveraging existing map APIs to obtain real-time traffic conditions such as current congestion, the accessibility of the user to the offline service point is calculated, including distance and travel time, to determine if the expected arrival time window is open. Historical complaint rates (e.g., complaints per thousand users), repeat cancellation rates, average processing delays, and negative review rates for the target service at that window are statistically analyzed to assess historical complaint data. For example, if a window has received 20 complaints in the past 30 days, the average complaint severity is considered moderate. For instance, service window A handles real estate registration with a complaint rate of 4.2‰, an average delay of 32.1 minutes, and a negative review rate of 3.8%; service window B handles certificate processing with a complaint rate of 1.3‰, an average delay of 9.8 minutes, and a negative review rate of 1.1%. Combining location accessibility and historical complaint data forms a third state—the service constraint factor—reflecting potential limitations in service quality and user experience.

[0041] To ensure the comparability and fusion of various constraint factors, the first, second, and third state-service constraint factors are normalized, converting all factors into dimensionless values ​​in the 0-1 range. These three constraint factors are then weighted and fused to obtain the state-service constraint factor. The weighting coefficients are typically adjusted based on actual business needs and experimental data, ensuring that the sum of the weighting coefficients for all three is 1. The state-service constraint factor is a comprehensive service status constraint index formed by integrating these multiple dimensions of service status constraint factors, reflecting the overall online and offline service capabilities and user experience. For example, the normalized value of the first state-service limitation factor is 0.982, the normalized value of the second state-service limitation factor is 0.53, and the normalized value of the third state-service limitation factor is 0.42. The weights are set as follows: the weight of the first state-service limitation factor is 0.4, the weight of the second state-service limitation factor is 0.35, and the weight of the third state-service limitation factor is 0.25, resulting in a state-service limitation factor of 0.70. Among these, the second and third state-service limitation factors are negative indicators such as load status and complaint rate. The larger the value, the stronger the limitation. Therefore, the 1-normalized value is used to reflect the negative impact.

[0042] By integrating online service status, offline window load, and additional information related to user experience through multi-source sensing channels, a multi-dimensional status-service constraint factor is established to promptly identify online or offline service bottlenecks, such as interface instability, window congestion, or geographical obstacles, and to provide users with the most suitable service path.

[0043] Furthermore, this application also includes the following steps:

[0044] Time node feature analysis is performed on online and offline service platforms, and a first matching feature is established based on the time node feature analysis results; historical service data from online and offline are acquired, and after data fusion of the historical service data, service status trend matching is performed based on the first matching feature to establish a service status trend matching result; the real-time status perception result is used as the actual status, and status-service constraint prediction based on the service status trend matching result is performed to establish a compensation constraint factor; the status-service constraint factor is updated based on the compensation constraint factor.

[0045] Specifically, time-based feature analysis is performed on online and offline service platforms. This involves statistically analyzing service behavior and business volume data across different time periods (e.g., hours, half-hours, holidays, weekdays) to extract key time-related features. Time-series data from online and offline service platforms is collected, such as hourly business volume, queue time, and processing speed. Statistical analysis (mean, variance) is used to extract time features, forming primary matching features, such as peak hours, off-peak hours, and holiday patterns. For example, a peak business period is from 9:00 AM to 11:00 AM on weekends, with an average request volume of 1500 requests per hour.

[0046] Acquire historical online and offline service data, which includes data sets such as online and offline business request volume, processing time, success rate, queue length, and user feedback over a past period. Perform data fusion on the historical service data, merging, cleaning, and standardizing the online and offline historical service data according to dimensions such as time and business type to form a unified and consistent service dataset. For example, on a weekday morning at 9:00 AM, if the number of people queuing at the offline window is 20 and the number of online requests is 1400, these are merged into a unified data entry.

[0047] Trend matching is performed based on the first matching feature. This involves matching historical online and offline service data against the first matching feature to determine service status trends. The result determines the service status trend. By analyzing historical service data, such as user traffic and service response time, characteristics at different time points are identified, and service status trend matching is performed based on these characteristics. The service status trend matching result is used to predict future service status.

[0048] Real-time status awareness results are real-time data acquired from online and offline service platforms, reflecting the current actual business load and response status. Using these real-time status awareness results as the actual state, and inputting service status trend matching results, status-service constraint prediction is performed. The service status trend matching results are then used to predict potential service constraints or bottlenecks in the future, resulting in a compensation constraint factor. For example, if the real-time status awareness results predict that the number of people queuing in the next hour will reach 30, a 50% increase compared to the historical average for the same period, the compensation constraint factor will increase from 0.6 to 0.8. This compensation constraint factor is then applied to the original status-service constraint factor, achieving dynamic adjustment and resulting in an updated status-service constraint factor that better reflects the real and dynamic service status. For example, if the offline business volume is 1400, the offline queue number is 25, the historical average queue number is 20, the current difference from the historical average is 25%, the predicted queue number is 30, the current constraint factor is 0.6, the calculated compensation constraint factor is 0.8, and the updated constraint factor is 0.66.

[0049] By analyzing time-node features and matching trends, we can accurately control the service status at different time periods. By combining historical data trend information with the current real-time status, we can enhance the ability to predict future service pressure. We introduce compensation constraint factors and dynamically adjust them to address trend deviations, making the service constraint factors more consistent with the actual operating status and reducing misjudgments.

[0050] S300: Perform service scenario awareness and configure multi-objective optimization channels using the service scenario awareness results.

[0051] Furthermore, this application S300 includes:

[0052] Establish a global goal planning for the service platform, and configure a global perception layer for multi-objective optimization channels based on the global goal planning; extract the service scenario perception results into service scenario feature vectors, which include time features, resource features, user urgency features, geographical accessibility features, and service quality features; establish a weight reconstruction for the multi-objective optimization layer based on the service scenario feature vectors; and complete the channel configuration of the multi-objective optimization channels using the multi-objective optimization layer and the global perception layer.

[0053] Specifically, this involves establishing a global goal plan for the service platform, which is a set of optimal objectives set based on the overall development goals of the service platform, such as maximizing the benefits for service recipients, improving service efficiency, diversifying service supply, and reducing service acquisition costs. Maximizing the benefits for service recipients includes high user satisfaction and high success rates; improving service efficiency includes shorter service times and faster response times, fully realizing consistency in goals, processes, and standards between online and offline government services, providing service recipients with responsive, seamless, high-quality, and efficient government services; diversifying service supply includes broader service coverage and more diverse channels, making service targeting more precise, service models more integrated, and service channels more diverse; and reducing service acquisition costs includes shorter waiting times and lower user travel costs, resulting in fewer departments directly involved, fewer steps involved, less time spent, and less material required from service recipients, comprehensively forming a value orientation that prioritizes service operational efficiency.

[0054] Based on the global goal planning, a global perception layer for multi-objective optimization channels is configured. The multi-objective optimization channel is a scheduling and control process that balances and solves multiple objectives (the multiple optimization objectives mentioned above). It integrates multiple information layers, evaluation layers, and decision layers to comprehensively optimize paths and resources. The global perception layer is a layer in the multi-objective optimization channel, used to perceive and obtain global status information of the service platform, such as the current system load, service resource distribution, and overall user satisfaction.

[0055] Service scenario perception is performed to obtain service scenario perception results, which are the real-time perception results of the service platform on the service scenario, such as the processing time, the user's urgency, and geographical location. The multiple dimensions of information involved in the service scenario are transformed into a numerical vector that can be used for calculation and optimization, including time characteristics (such as whether it is a peak period or a holiday), resource characteristics (such as whether window resources are tight and network response speed), user urgency characteristics (such as whether the user has clearly marked an urgent application), geographical accessibility characteristics (such as the physical distance between the user and the offline outlet or the convenience of transportation), and service quality characteristics (such as the historical evaluation score or complaint rate of a certain business department).

[0056] Based on the service scenario feature vectors, a weight reconstruction of the multi-objective optimization layer is established. In other words, based on the current service scenario, it determines which feature vector to prioritize and adjusts the optimization objective weights accordingly. For example, when users are in a great hurry, the efficiency objective weight is increased, while the weights of multiple service objectives are decreased. The reconstructed weights, along with data from the global perception layer, are used as input to configure the multi-objective optimization channel—the one best suited to the current service.

[0057] Furthermore, this application also includes the following steps:

[0058] A virtual identity for the user is established based on the user's behavioral profile. The virtual identity is then subjected to virtual simulation execution based on the non-path optimization reconstruction result, and virtual simulation execution feedback is established. Real user execution feedback based on the path optimization reconstruction result is obtained. Dual feedback optimization is performed using the virtual simulation execution feedback and the real user execution feedback, and the multi-objective optimization channel is compensated based on the dual feedback optimization result.

[0059] Specifically, the ternary semantic mapping of intent-item-path and state-service constraint factors are input into a multi-objective optimization channel for path optimization. Considering multiple objectives simultaneously (such as efficiency, satisfaction, and resource cost), the optimal service path is obtained. Real user feedback records after processing transactions based on the path optimization and reconstruction results are obtained, including actual time taken, actual satisfaction rating, and actual number of people in the queue. For example, Path A: Fully online → Estimated time 70 minutes, predicted satisfaction 4.1; Path B: Online appointment + offline fast track → Time taken 48 minutes, satisfaction 4.6; Path C: Fully offline → Time taken 90 minutes, satisfaction 3.9. According to the multi-objective optimization channel, Path B is selected as the path optimization and reconstruction result, resulting in an actual time of 43 minutes, an actual satisfaction rating of 4.8, and an actual number of people in the queue of 21.

[0060] Based on user behavior profiles, a virtual user identity is established. This is a modeled user entity built based on user behavior profiles (such as preferences, historical behavior, sensitivity, and behavioral patterns) to simulate user decision-making and behavioral responses. The virtual identity is built based on the user's interaction history on the platform over the past 30 days, frequently handled tasks, average waiting tolerance, form completion speed, and mobile device preference. This includes the user's historical preferences, acceptable queueing time, distance tolerance, and complaint records. In the virtual environment, the service process is simulated based on the virtual identity, but without path optimization or reconstruction. Feedback information is collected during the virtual simulation, such as simulated user satisfaction and service response time, to obtain virtual simulation execution feedback. For example, suppose the user's behavior profile shows that the user frequently accesses the service platform between 9:00 AM and 10:00 AM and is sensitive to service response time. Based on this behavior profile, a virtual user identity is built, and the user's behavior during this time period is simulated in the virtual environment. During the virtual simulation, it is found that the service response time is relatively long and the simulated user's satisfaction is relatively low during this period. Based on the feedback from this virtual simulation, service processes can be optimized, such as increasing service resources between 9:00 AM and 10:00 AM to improve service response time and user satisfaction.

[0061] Dual feedback optimization is achieved by combining simulated and real user feedback. The results of this dual feedback optimization are then used to compensate the multi-objective optimization channel. By comparing the simulation and real results, problems and deficiencies in the service process are identified and optimized. This is done by adjusting the parameters and weights in the multi-objective optimization channel to better meet user needs and improve service efficiency. Dual feedback optimization refers to combining simulated and real feedback to correct model parameters and compensate weights in the multi-objective optimization channel, thereby improving the effectiveness of subsequent path recommendations. Through compensation in the multi-objective optimization channel, the service process is optimized and restructured, improving service quality and efficiency.

[0062] S400: Using the ternary semantic mapping of intent-item-path and the state-service constraint factor as input, the path optimization and reconstruction is performed through the multi-objective optimization channel to establish the path optimization and reconstruction result.

[0063] Furthermore, this application S400 includes:

[0064] The event is decomposed according to the ternary semantic mapping of intent-event-path to establish N atomic events; the dependency relationship between the N atomic events is identified to establish a directed service chain structure; the path segment optimization is performed based on the N atomic events, the directed service chain structure, and the state-service constraint factor according to the multi-objective optimization channel to establish the segment optimization result; the path optimization and reconstruction result is established according to the segment optimization result.

[0065] The global integration layer of the multi-objective optimization channel is invoked. After receiving the segmented optimization results, the global integration layer performs execution collaboration analysis on the path segments. The path optimization reconstruction results are established based on the execution collaboration analysis results.

[0066] Establish a threshold for synergistic effect breakthrough, and use the threshold for synergistic effect breakthrough to evaluate the coordination of the segmented optimization results; when the coordination of the segmented optimization results meets the threshold for synergistic effect breakthrough, the optimal result of the corresponding segmented optimization result is eliminated to establish the path optimization reconstruction result.

[0067] Specifically, the ternary semantic mapping of intent-item-path is decomposed into N atomic items. An atomic item is the smallest indivisible service unit, such as submitting an application, reviewing documents, issuing a certificate, obtaining approval, and receiving the certificate. Dependencies between the N atomic items are identified, establishing a directed service chain structure. This involves identifying the sequence and constraints between atomic items, using atomic items as nodes and dependencies as directed edges to form a directed service chain structure, representing the execution order or parallel structure between items. For example, document upload must be after identity verification, document review must be before certificate issuance, and certificate issuance must be before certificate disbursement. A simple directed service chain structure is obtained as follows: Name Approval → Identity Verification → Document Upload → Approval → Certificate Issuance → Payment → Certificate Disbursement.

[0068] The multi-objective optimization channel inputs N atomic items, a directed service chain structure, and state-service constraint factors to perform segmented optimization of the path segments. This involves segment-by-segment optimization, selecting the optimal processing method or execution resources for each path segment to generate a comprehensive optimal path. In other words, each segment is optimized using the multi-objective optimization channel to obtain segmented optimization results. The global integration layer of the multi-objective optimization channel receives the segmented optimization results and aggregates them for overall execution coordination analysis. The segmented optimization results are locally optimal processing solutions obtained through independent optimization of each path segment (i.e., a specific stage of the business process). Path segment execution coordination analysis evaluates the synergistic effect of solutions combined between path segments, determining whether these locally optimal solutions can work efficiently together, avoiding the problem of poor overall performance due to local optima. The global integration layer calculates and compares coordination indicators such as resource competition, time coherence, and service quality between path segments to determine whether these locally optimal solutions can cooperate smoothly to meet the overall service objectives.

[0069] The directed service chain structure is divided into several continuous path segments. Each path segment consists of an adjacent atomic item, representing a stage in the business process. For each path segment, based on current state information (such as offline window load, online service accessibility, user geolocation, etc.) and pre-defined multi-objective optimization indicators (such as time cost, service quality, user satisfaction, etc.), the optimal processing path or execution plan for that segment is independently found through a multi-objective optimization algorithm. For each path segment, among all available processing methods and resources, the trade-offs of each optimization objective are evaluated and weighed to ultimately determine the optimal path plan for that segment. After completing the independent optimization of all path segments, the optimal plans for each path segment are sequentially concatenated according to the chain structure to generate the comprehensive optimal path for the entire business process.

[0070] A threshold for synergy effect is set to measure the rationality and execution efficiency of the overall path segment coordination. If the synergy evaluation of one or more path segment combinations does not meet the threshold, indicating poor synergy, it means that the combination of the current locally optimal solutions cannot bring good overall execution results. Eliminating locally optimal results that do not meet the synergy criteria—that is, removing the optimal solutions for certain path segments—may revert to suboptimal solutions or re-search for solutions, in order to improve the coordination of the overall process and avoid global degradation caused by local optima. In other words, in segmented optimization, each segment may contain multiple solutions; therefore, the synergy analysis is a global analysis process, and the locally optimal solution may not necessarily be the globally optimal solution.

[0071] Through global integration and coordination, an optimized and restructured path optimization result is formed, serving as the final execution plan for business processes by the service platform. The path optimization restructured result is the final comprehensive optimized path formed after global integration and adjustment. By decomposing business processes into atomic items and modeling dependencies, a visual and logically clear directed service chain structure is constructed, facilitating fine-grained optimization. Combined with state-service constraint factors, segmented optimization is achieved, enabling each path to flexibly adapt to dynamic resources and constraints. The global integration layer, through collaborative analysis and elimination mechanisms, solves the problem of local optima leading to overall suboptimality, ensuring global path optimization and execution coordination, thereby significantly improving the efficiency of online and offline service processes and user satisfaction, and optimizing resource utilization and service quality.

[0072] S500: Perform online and offline business collaboration optimization management based on the path optimization and reconstruction results.

[0073] Specifically, based on the path optimization and restructuring results, tasks suitable for online processing are automatically assigned to online channels, while tasks suitable for offline processing are arranged at physical service points. Factors such as user preferences, geographical location, and resource availability are considered to rationally schedule the execution time and sequence of each step. The service capabilities of the online platform (e.g., server load, response time) and the personnel and equipment resource status of offline service windows are dynamically monitored. Service allocation is adjusted through real-time data feedback to prevent resource overload or idleness. Seamless transitions between online and offline processing stages are ensured; for example, online approval results are automatically pushed to offline windows, and offline processing completion information is promptly fed back to the online system, avoiding duplicate submissions and waiting delays. Utilizing the analysis of time characteristics and user urgency characteristics in path restructuring, the timing of online-offline connections is optimized to reduce user waiting time and processing steps, improving overall efficiency and satisfaction. Real-time execution data and user feedback from online and offline businesses are collected through a monitoring platform, and combined with path optimization results, business collaboration solutions are continuously adjusted and optimized to form a closed-loop management system.

[0074] For example, items A and B are suitable for online processing, which is expected to save users an average of 20 minutes of queuing time and 5 seconds of processing time; items C and D require offline processing, with 5 staff members assigned to the on-site service window, and an average processing time of 15 minutes per item; automatic information push is set up in the online-offline connection link, and the information transmission delay is less than 2 seconds. Through collaborative optimization management, user satisfaction with online processing increased by 45%, and the average completion time decreased by 15%; the utilization rate of offline window resources increased to 85%, and the number of people queuing during peak periods decreased by 20%; the overall business processing cycle was shortened by 5%, and the user complaint rate decreased by 50%. Through online-offline business collaborative optimization management based on path optimization reconstruction results, efficient connection and resource optimization allocation between service channels were achieved, effectively avoiding resource waste and redundant processing procedures, improving user experience and business processing efficiency, ensuring the smooth execution of business processes and the dynamic adaptability of the system, and greatly promoting the improvement of service quality and satisfaction.

[0075] In summary, the online-offline business collaboration optimization method of the service platform provided in this application has the following beneficial effects:

[0076] By receiving users' natural language input data, performing semantic recognition and item extraction based on the input data, and constructing a ternary semantic mapping of intent-item-path based on a service knowledge graph; activating the service platform's multi-source perception channels to perform real-time online and offline status perception and establish status-service constraint factors; performing service scenario perception and configuring multi-objective optimization channels using the service scenario perception results; using the intent-item-path ternary semantic mapping and the status-service constraint factors as input, performing path optimization and reconstruction through the multi-objective optimization channels, and establishing path optimization and reconstruction results; and performing online and offline business collaborative optimization management based on the path optimization and reconstruction results. In other words, by performing semantic recognition and item extraction on users' natural language input, constructing an intent-item-path ternary semantic mapping, performing online and offline status perception through multi-source perception channels, configuring multi-objective optimization channels based on service scenario perception results, and replanning service paths, dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes are achieved, improving service quality and efficiency.

[0077] Example 2: Based on the same inventive concept as the online-offline business collaboration optimization method for the service platform in Example 1, this application also provides an online-offline business collaboration optimization system for the service platform. Please refer to the appendix. Figure 2 The online and offline business collaboration optimization system of the service platform includes:

[0078] Natural Language Processing Module 11 is used to receive natural language input data from users, perform semantic recognition and item extraction based on the natural language input data, and construct a three-element semantic mapping of intent-item-path based on the service knowledge graph; Real-time Status Awareness Module 12 is used to activate the multi-source awareness channels of the service platform, perform real-time status awareness of online and offline operations, and establish status-service constraint factors; Service Scenario Awareness Module 13 is used to perform service scenario awareness and configure multi-objective optimization channels using the service scenario awareness results; Path Optimization and Reconstruction Module 14 is used to take the three-element semantic mapping of intent-item-path and the status-service constraint factors as input, perform path optimization and reconstruction through the multi-objective optimization channels, and establish path optimization and reconstruction results; Collaborative Management Module 15 is used to perform online and offline business collaborative optimization management based on the path optimization and reconstruction results.

[0079] Furthermore, the natural language processing module 11 in the online-offline business collaboration optimization system of the service platform is also used for:

[0080] The natural language input data is preprocessed, including word segmentation, standardization, named entity recognition, and sentiment word recognition. Based on the preprocessing results, intent recognition, entity recognition, and relationship extraction are performed to construct a ternary semantic mapping of intent-item-path.

[0081] Furthermore, the real-time status sensing module 12 in the online-offline business collaboration optimization system of the service platform is also used for:

[0082] Utilizing the online sensing channel of the service platform, online business accessibility is sensed to establish a first state - service limitation factor; utilizing the image sensing channel of the service platform, real-time load sensing of offline windows is performed to establish a second state - service limitation factor; utilizing the additional sensing channel of the service platform, location accessibility and historical complaint data sensing are performed to establish a third state - service limitation factor; and a state - service limitation factor is established based on the first state - service limitation factor, the second state - service limitation factor, and the third state - service limitation factor.

[0083] Furthermore, the real-time status sensing module 12 in the online-offline business collaboration optimization system of the service platform is also used for:

[0084] Time node feature analysis is performed on online and offline service platforms, and a first matching feature is established based on the time node feature analysis results; historical service data from online and offline are acquired, and after data fusion of the historical service data, service status trend matching is performed based on the first matching feature to establish a service status trend matching result; the real-time status perception result is used as the actual status, and status-service constraint prediction based on the service status trend matching result is performed to establish a compensation constraint factor; the status-service constraint factor is updated based on the compensation constraint factor.

[0085] Furthermore, the service scenario perception module 13 in the online-offline business collaboration optimization system of the service platform is also used for:

[0086] Establish a global goal planning for the service platform, and configure a global perception layer for multi-objective optimization channels based on the global goal planning; extract the service scenario perception results into service scenario feature vectors, which include time features, resource features, user urgency features, geographical accessibility features, and service quality features; establish a weight reconstruction for the multi-objective optimization layer based on the service scenario feature vectors; and complete the channel configuration of the multi-objective optimization channels using the multi-objective optimization layer and the global perception layer.

[0087] Furthermore, the service scenario perception module 13 in the online-offline business collaboration optimization system of the service platform is also used for:

[0088] A virtual identity for the user is established based on the user's behavioral profile. The virtual identity is then subjected to virtual simulation execution based on the non-path optimization reconstruction result, and virtual simulation execution feedback is established. Real user execution feedback based on the path optimization reconstruction result is obtained. Dual feedback optimization is performed using the virtual simulation execution feedback and the real user execution feedback, and the multi-objective optimization channel is compensated based on the dual feedback optimization result.

[0089] Furthermore, the path optimization and reconstruction module 14 in the online-offline business collaboration optimization system of the service platform is also used for:

[0090] The event is decomposed according to the ternary semantic mapping of intent-event-path to establish N atomic events; the dependency relationship between the N atomic events is identified to establish a directed service chain structure; the path segment optimization is performed based on the N atomic events, the directed service chain structure, and the state-service constraint factor according to the multi-objective optimization channel to establish the segment optimization result; the path optimization and reconstruction result is established according to the segment optimization result.

[0091] Furthermore, the path optimization and reconstruction module 14 in the online-offline business collaboration optimization system of the service platform is also used for:

[0092] The global integration layer of the multi-objective optimization channel is invoked. After receiving the segmented optimization results, the global integration layer performs execution collaboration analysis on the path segments. The path optimization reconstruction results are established based on the execution collaboration analysis results.

[0093] Furthermore, the path optimization and reconstruction module 14 in the online-offline business collaboration optimization system of the service platform is also used for:

[0094] Establish a threshold for synergistic effect breakthrough, and use the threshold for synergistic effect breakthrough to evaluate the coordination of the segmented optimization results; when the coordination of the segmented optimization results meets the threshold for synergistic effect breakthrough, the optimal result of the corresponding segmented optimization result is eliminated to establish the path optimization reconstruction result.

[0095] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The online and offline business collaboration optimization method and specific examples of the service platform in Embodiment 1 are also applicable to the online and offline business collaboration optimization system of the service platform in this embodiment. Through the foregoing detailed description of the online and offline business collaboration optimization method of the service platform, those skilled in the art can clearly understand the online and offline business collaboration optimization system of the service platform in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0096] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0097] Obviously, those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for optimizing online and offline business collaboration on a service platform, characterized in that, include: Receive natural language input data from users, perform semantic recognition and item extraction based on the natural language input data, and construct a three-element semantic mapping of intent-item-path based on the service knowledge graph; Activate the multi-source perception channels of the service platform, perform real-time online and offline status perception, and establish status-service constraint factors; Perform service scenario awareness and configure multi-objective optimization channels using the service scenario awareness results; Using the ternary semantic mapping of intent-item-path and the state-service constraint factor as input, path optimization and reconstruction are performed through the multi-objective optimization channel to establish the path optimization and reconstruction result; Based on the path optimization and reconstruction results, online and offline business collaborative optimization management is carried out. The activation service platform's multi-source sensing channels perform real-time online and offline status sensing and establish status-service constraint factors, including: Utilize the online sensing channels of the service platform to perceive the accessibility of online services and establish the first state - service limiting factors; By utilizing the image perception channel of the service platform, real-time load perception of offline windows is performed to establish a second state - service constraint factor; By utilizing the additional sensing channels of the service platform, location accessibility and historical complaint data sensing are implemented to establish a third state - service limitation factor; Establish a state-service restriction factor based on the first state-service restriction factor, the second state-service restriction factor, and the third state-service restriction factor; The provision of configuring multi-objective optimization channels using service scenario perception results includes: Establish a global goal plan for the service platform, and configure a global perception layer for multi-goal optimization channels based on the global goal plan; The service scenario perception results are extracted into a service scenario feature vector, which includes time features, resource features, user urgency features, geographical accessibility features, and service quality features. Based on the service scenario feature vector, establish a weight reconstruction for the multi-objective optimization layer; The multi-objective optimization layer and the global perception layer are used to complete the channel configuration of the multi-objective optimization channel; The step of performing path optimization and reconstruction through the multi-objective optimization channel to establish path optimization and reconstruction results includes: Based on the ternary semantic mapping of intent-item-path, the items are decomposed to create N atomic items; Establish dependency relationships between the N atomic items and build a directed service chain structure; Based on the multi-objective optimization channel, segmented optimization is performed on the path segments based on N atomic items, the directed service chain structure, and the state-service constraint factor, and segmented optimization results are established. Establish path optimization and reconstruction results based on the segmented optimization results; The step of establishing path optimization and reconstruction results based on segmented optimization results includes: The global integration layer of the multi-objective optimization channel is invoked, and after receiving the segmented optimization results, the global integration layer performs a collaborative analysis of the path segments. Establish path optimization and reconstruction results based on the results of the execution collaboration analysis; The step of establishing path optimization and reconstruction results based on the results of collaborative analysis includes: Establish a threshold for synergistic effect breakthrough, and use the threshold for synergistic effect breakthrough to evaluate the synergistic effect of the segmented optimization results. When the synergistic evaluation of the segmented optimization results satisfies the threshold of the synergistic effect, the optimal result of the corresponding segmented optimization result is eliminated in order to establish the path optimization reconstruction result.

2. The online and offline business collaboration optimization method of the service platform as described in claim 1, characterized in that, The real-time perception of online and offline status, and the establishment of status-service constraint factors, include: Perform time node feature analysis on online and offline service platforms, and establish the first matching feature based on the time node feature analysis results; Obtain historical service data from both online and offline sources, fuse the historical service data, perform service status trend matching based on the first matching feature, and establish a service status trend matching result. Using the real-time state perception results as the actual state, perform state-service constraint prediction based on the service state trend matching results, and establish compensation constraint factors. The state-service constraint factor is updated based on the compensation constraint factor.

3. The online and offline business collaboration optimization method of the service platform as described in claim 1, characterized in that, The online and offline business collaborative optimization management based on the path optimization and reconstruction results includes: A virtual identity for a user is established based on the user's behavioral profile. The virtual identity is then reconstructed through non-path optimization, and the virtual simulation execution result is executed. A virtual simulation execution feedback is then established. Obtain real user feedback based on the path optimization and reconstruction results; The virtual simulation execution feedback and the user's actual execution feedback are used for dual feedback optimization, and the multi-objective optimization channel is compensated based on the dual feedback optimization results.

4. The online and offline business collaboration optimization method of the service platform as described in claim 1, characterized in that, The step of performing semantic recognition and event extraction based on the natural language input data includes: The natural language input data is preprocessed, including word segmentation preprocessing, standardization processing, named entity recognition processing, and sentiment word recognition processing. Based on the text preprocessing results, intent recognition, entity recognition, and relationship extraction are performed to construct a ternary semantic mapping of intent-item-path.

5. A service platform's online and offline business collaboration optimization system, characterized in that, The steps for implementing the online-offline business collaboration optimization method of the service platform according to any one of claims 1 to 4, wherein the online-offline business collaboration optimization system of the service platform comprises: The natural language processing module is used to receive natural language input data from users, perform semantic recognition and item extraction based on the natural language input data, and construct a three-element semantic mapping of intent-item-path based on the service knowledge graph. The real-time status awareness module is used to activate the multi-source awareness channels of the service platform, perform real-time online and offline status awareness, and establish status-service constraint factors. The service scenario awareness module is used to perform service scenario awareness and configure multi-objective optimization channels using the service scenario awareness results; The path optimization and reconstruction module is used to take the ternary semantic mapping of intent-item-path and the state-service constraint factor as input, perform path optimization and reconstruction through the multi-objective optimization channel, and establish the path optimization and reconstruction result. The collaborative management module is used to perform online and offline business collaborative optimization management based on the path optimization and reconstruction results.

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