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

Abstract

The invention provides an online and offline business collaborative optimization method and system for a service platform, and relates to the technical field of business management, and the method comprises the steps: receiving natural language input data of a user, and constructing an intention-matter-path ternary semantic mapping; activating a multi-source sensing channel of the service platform, executing online and offline state real-time sensing, and establishing a state-service limiting factor; service scene perception is executed, and a multi-target optimization channel is configured; performing path optimization reconstruction by taking the intention-item-path ternary semantic mapping and the state-service limiting factor as input, and establishing a path optimization reconstruction result; and carrying out online and offline business collaborative optimization management. Through the method and the device, the technical problem of relatively low service handling efficiency caused by online and offline flow splitting and different standards in the prior art is solved, dynamic collaborative reconstruction and intelligent scheduling of online and offline services are realized through semantic recognition and multi-objective optimization, and the service quality and efficiency are enhanced.

Description

Technical Field

[0001] The present application relates to the field of business management technology, and in particular to a method and system for collaboratively optimizing online and offline business of a service platform. Background Art

[0002] Existing service platforms generally adopt an architecture that separates online operations from offline processing. This leads to significant process discontinuities and data sharing barriers, becoming a key technical bottleneck hindering service efficiency. First, there is a fragmented nature of online and offline service processes. Due to the lack of unified data models, interface specifications, and process definition standards across different regions and departments, significant differences exist in the technical implementation of service process design, approval processes, and other aspects between online and offline platforms. After completing an application online, physical documents must be submitted or form data re-entered at offline counters. This results in redundant business logic execution and unnecessary duplication of process nodes, significantly increasing operational burden and processing time. Second, the heterogeneity of online and offline standard systems hinders collaboration. A lack of standardization across different service platforms regarding document formats and service status makes it difficult to seamlessly integrate data across channels, preventing mutual recognition of service links and significantly reducing processing efficiency. In addition, existing service platforms generally adopt static configuration processes, lacking the ability to monitor business processes in real time, dynamically optimize and adapt them to individual needs. This makes it difficult to meet increasingly diversified and complex service demands, and also limits the ability to coordinate and allocate online and offline service resources, resulting in uneven or idle utilization of service resources, further limiting the improvement of overall service efficiency.

[0003] In summary, the existing technology has technical problems such as low service efficiency due to the separation of 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 collaborative optimization of online and offline business of a service platform, so as to solve the technical problem in the prior art of low service efficiency due to the separation of online and offline processes and inconsistent standards.

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

[0006] In the first aspect, the present application provides an online and offline business collaborative optimization method for a service platform, which is implemented by an online and offline business collaborative optimization system of the service platform, wherein the online and offline business collaborative optimization method for the service platform includes: receiving natural language input data from users, performing semantic recognition and matter extraction based on the natural language input data, and constructing a ternary semantic mapping of intention-matter-path based on the service knowledge graph; activating the multi-source perception channel of the service platform, performing real-time perception of online and offline states, and establishing a state-service restriction factor; performing service scenario perception, and configuring a multi-objective optimization channel using the service scenario perception results; taking the ternary semantic mapping of intention-matter-path and the state-service restriction factor as input, performing path optimization reconstruction through the multi-objective optimization channel, and establishing a path optimization reconstruction result; and performing online and offline business collaborative optimization management based on the path optimization reconstruction result.

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

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

[0009] Optionally, a 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 of the online and offline platforms is 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 a status-service restriction prediction based on the service status trend matching result is performed to establish a compensation restriction factor; the status-service restriction factor is updated according to the compensation restriction factor.

[0010] Optionally, a global goal plan for the service platform is established, and a global perception layer of a multi-objective optimization channel is configured according to the global goal plan; the service scenario perception result is extracted as a service scenario feature vector, and the service scenario feature vector includes time characteristics, resource characteristics, user urgency characteristics, geographical accessibility characteristics, and service quality characteristics; a weight reconstruction of a multi-objective optimization layer is established according to the service scenario feature vector; and the channel configuration of the multi-objective optimization channel is completed using the multi-objective optimization layer and the global perception layer.

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

[0012] Optionally, matters are decomposed according to the ternary semantic mapping of intention-matter-path to establish N atomic matters; dependency relationships between the N atomic matters are identified to establish a directed service chain structure; segmented optimization of path segments based on the N atomic matters, the directed service chain structure, and the state-service restriction factor is performed according to the multi-objective optimization channel to establish a segmented optimization result; and a path optimization reconstruction result is established based on the segmented optimization result.

[0013] Optionally, the global integration layer of the multi-objective optimization channel is called, and after the global integration layer receives the segmented optimization result, execution collaborative analysis of the path segments is performed; and a path optimization reconstruction result is established according to the execution collaborative analysis result.

[0014] Optionally, a collaborative effect breakthrough threshold is established, and the collaborative effect breakthrough threshold is used to perform a coordinated collaborative evaluation of the segmented optimization results; when the coordinated collaborative evaluation of the segmented optimization results meets the collaborative effect breakthrough threshold, the optimal result of the corresponding segmented optimization result is eliminated to establish a path optimization reconstruction result.

[0015] In the second aspect, the present application also provides an online and offline business collaborative optimization system of a service platform, which is used to execute the online and offline business collaborative optimization method of the service platform as described in the first aspect, wherein the online and offline business collaborative optimization system of the service platform includes: a natural language processing module, which is used to receive the user's natural language input data, perform semantic recognition and matter extraction based on the natural language input data, and construct a ternary semantic mapping of intention-matter-path based on the service knowledge graph; a real-time state perception module, which is used to activate the multi-source perception channel of the service platform, perform real-time state perception of online and offline, and establish a state-service restriction factor; a service scenario perception module, which is used to perform service scenario perception and configure a multi-objective optimization channel using the service scenario perception results; a path optimization and reconstruction module, which is used to take the ternary semantic mapping of intention-matter-path and the state-service restriction factor as input, perform path optimization and reconstruction through the multi-objective optimization channel, and establish a path optimization reconstruction result; a collaborative management module, which is used to perform online and offline business collaborative optimization management based on the path optimization and reconstruction result.

[0016] One or more technical solutions provided in this application have at least the following beneficial effects: By receiving the user's natural language input data, performing semantic recognition and item extraction based on the natural language input data, and constructing a ternary semantic mapping of intent, item, and path based on the service knowledge graph; activating the multi-source perception channel of the service platform, performing real-time perception of online and offline status, and establishing a status-service restriction factor; performing service scenario perception, and configuring a multi-objective optimization channel using the service scenario perception results; using the ternary semantic mapping of intent, item, and path and the status-service restriction factor as input, performing path optimization and reconstruction through the multi-objective optimization channel, and establishing a path optimization reconstruction result; and performing online and offline business collaborative optimization management based on the path optimization and reconstruction result. In other words, by performing semantic recognition and item extraction on the natural language input by the user, constructing a ternary semantic mapping of intent, item, and path, performing online and offline status perception through the multi-source perception channel, configuring a multi-objective optimization channel based on the service scenario perception results, and replanning the service path, the dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes are achieved, improving service quality and efficiency.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, which can be implemented in accordance with the contents of the description, and to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically listed below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in this application or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0019] Figure 1 This is a flow chart of the online and offline business collaborative optimization method of this application service platform.

[0020] Figure 2 This is a structural diagram of the online and offline business collaborative optimization system of this application service platform.

[0021] Explanation of the accompanying drawings: natural language processing module 11, real-time status perception module 12, service scenario perception module 13, path optimization and reconstruction module 14, collaborative management module 15. DETAILED DESCRIPTION

[0022] This application solves the technical problem of low service efficiency in the existing technology due to the separation of online and offline processes and inconsistent standards by providing an online and offline business collaborative optimization method and system for the service platform. By performing semantic recognition and item extraction on the natural language input by the user, a ternary semantic mapping of intention-item-path is constructed, and online and offline state perception is performed through multi-source perception channels. Based on the service scenario perception results, a multi-objective optimization channel is configured, and the service path is re-planned, dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes are achieved, thereby improving service quality and efficiency.

[0023] Below, the technical solutions in this application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited to the example embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. It should also be noted that, for the convenience of description, only the parts related to this application, rather than all of them, are shown in the accompanying drawings.

[0024] For example, see the attached Figure 1 The present application provides a method for collaboratively optimizing online and offline services of a service platform, wherein the method is executed by an online and offline service collaborative optimization system of the service platform, and the method specifically includes the following steps: S100: Receive natural language input data from the user, perform semantic recognition and item extraction based on the natural language input data, and construct a ternary semantic mapping of intention-item-path based on the service knowledge graph.

[0025] Specifically, natural language input data input by users is received, including natural language data input by users through voice or text, such as consultation questions and application items entered by users on the government service platform, which usually express needs in the form of natural language. First, the user's natural language input is preprocessed, including word segmentation preprocessing, standardization processing, named entity recognition processing, sentiment tendency word recognition processing, etc. Semantic recognition and item extraction are performed on the natural language input data after text preprocessing. Semantic recognition is to identify the semantic information in the natural language input data through language processing technology, including intentions, entities, relationships, etc.; item extraction refers to extracting specific business items from the natural language input data, such as service guidelines, handling conditions, etc.

[0026] The service knowledge graph is a knowledge base represented by a graph structure. It stores various data and business rules within the service domain, including concepts, entities, relationships, and other information related to government services. The service knowledge graph organizes and represents various types of 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, the service knowledge graph constructs a ternary semantic mapping of intent-matter-path. This mapping maps the user's natural language intent input to a specific business matter and specifies the actual processing path for each matter. Using the service knowledge graph, the identified intents and matters are mapped to corresponding nodes in the graph. For example, if the user's natural language input data is [I want to apply for a license, what are the procedures?], the user intent is to apply for a license, the matter is a license, and the path is identity verification → Item A processing → Item A receipt → Item A processing → Item B processing → Item B processing.

[0027] Through semantic recognition and ternary semantic mapping based on the service knowledge graph, the service platform accurately identifies the user's intent from natural language input, maps it to a specific service item, and automatically generates a relevant processing path for that item, guiding the user on how to proceed. Based on ternary semantic mapping, the service platform can automatically identify and push service paths related to user needs, reducing user wait time and manual intervention, and improving overall service efficiency.

[0028] Furthermore, the present application S100 includes: The natural language input data is subjected to text preprocessing, and the text preprocessing includes word segmentation preprocessing, standardization processing, named entity recognition processing, and sentiment tendency word recognition processing; intent recognition, entity recognition, and relationship extraction are performed based on the text preprocessing results to construct a ternary semantic mapping of intent-matter-path.

[0029] Specifically, text preprocessing is performed on the natural language input data entered by the user, cleaning and formatting the input text to make it more standardized. Text preprocessing includes word segmentation preprocessing, standardization, named entity recognition, and sentiment word recognition. Word segmentation preprocessing involves breaking the text into individual word units to better understand and analyze the text content. For example, after word segmentation preprocessing, "I want to apply for a license" results in [I, want, apply, license]. Standardization involves uniformly formatting non-standard parts of the text and standardizing proper nouns and abbreviations in the text to ensure that different expressions in the text are consistent. Named entity recognition is a natural language processing technology that identifies proper nouns in text, such as places, times, people, organizations, and proper nouns. Sentiment word recognition involves identifying emotional words in the text to determine the user's emotional state or attitude, such as positive or negative.

[0030] Perform intent recognition based on the text preprocessing results to identify the user's needs, such as applying for a license. Perform entity recognition based on the text preprocessing results to identify key entities in the text, including the service object, time, location, etc., such as licenses. Perform relationship extraction based on the text preprocessing results to extract the relationship between entities, such as the association between service guidelines and application conditions. Construct a ternary semantic mapping of intent-matter-path, where intent is the user's need or purpose (such as applying for a license); matter is a specific business or transaction (such as a license); and path is the operational process required to complete the matter (such as submitting identity documents → filling out an application form → waiting for review).

[0031] By accurately understanding and processing the user's natural language input data, the user's intentions and matters can be accurately extracted and mapped to specific service paths. Based on the ternary semantic mapping of intentions, matters and paths, the service process that matches the user's needs can be automatically recommended to the user, avoiding manual intervention.

[0032] S200: Activate the multi-source perception channel of the service platform, perform real-time perception of online and offline status, and establish status-service restriction factors.

[0033] Furthermore, the present application S200 includes: Utilize the online perception channel of the service platform to perceive the online business reachability and establish a first state-service restriction factor; utilize the image perception channel of the service platform to perform real-time load perception of the offline window and establish a second state-service restriction factor; utilize the additional perception channel of the service platform to perform location reachability and historical complaint data perception and establish a third state-service restriction 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.

[0034] Specifically, a multi-source perception channel is activated, which provides real-time perception of service status through multiple data sources, including online, image, and additional perception channels. The online perception channel monitors operational data from the online service platform to obtain information about its availability, load, and response time. Through the service platform's online perception channel, online service accessibility is perceived, assessing whether users can successfully access and use online service functions, including access latency, network response, and service openness. Online service accessibility refers to whether users can successfully complete required online operations, including logging into the system, accessing the corresponding modules, submitting documents, and obtaining processing results. The online perception channel attempts to access online services by monitoring system interfaces (such as API status and service monitoring programs). A primary limiting factor is established using the following metrics: response time, number of users in queue, error code frequency, service appointment availability, and maintenance downtime status. For example, if a license application service returns HTTP 503 error for five consecutive minutes, with an average response time exceeding 5000ms and a current queue of 42 people, it is considered to have low accessibility and recorded as unreachable. The first state - service limitation factor is a state variable extracted from the online perception channel, which is used to reflect the degree of limitation of the current availability and performance of the online service.

[0035] Utilize the image perception channel of the service platform to perceive the real-time load of offline windows. For example, collect the video stream of offline windows through cameras, and use image processing algorithms to analyze the video stream to obtain the real-time load of offline windows and establish the second state - service restriction factor. Install high-definition cameras above each business processing window of the service platform to collect video image streams of queues and waiting areas. Analyze the video image stream using the existing image recognition algorithm to identify the number of people, the number of open windows, the speed of business processing, etc. Establish the second state - service restriction factor based on the busyness of the offline window. For example, if it is detected that there are currently 18 people queuing at a certain window and the historical service rate is 3.5 minutes / person, the expected 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 maxis the maximum waiting time, such as 60 minutes; 0.2 is the minimum score, a lower limit set to prevent a score of 0 from completely eliminating the window; P is the window's historical failure rate (e.g., the proportion of service interruptions or transfers over the past seven days); α and β are adjustable weights, where α + β = 1. The current window has a historical failure rate of 15%, and α = 0.6 and β = 0.4 are set, placing greater emphasis on queue delay. According to the load scoring model, the current load is 0.31. Although the estimated queue 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 the scores of other windows are generally below 0.3, the window may still be recommended; otherwise, it will be downgraded. The second state, the service constraint factor, is a service availability indicator extracted from offline window load sensing results. It indicates the degree of service constraint imposed by the current state. It includes window number, window location, number of queue members, estimated wait time, and real-time load, reflecting the current load and service pressure of the offline service window.

[0036] The service platform leverages additional sensing channels to analyze location accessibility and historical complaint data. Based on the user's current location and the geographic location of the target service window, existing map APIs are used to obtain real-time traffic conditions, such as current traffic congestion. The user's accessibility to the offline service point, including distance and travel time, is calculated to determine whether the user's estimated arrival time window is open. Historical complaint data is collected for the target service window, including the complaint rate (e.g., number of complaints per thousand people), repeated cancellation rate, average processing delay, and negative review rate. For example, if a service window received 20 complaints in the past 30 days, the average complaint severity is medium. For example, service center 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 center window B handles license and permit 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 creates a third state—the service constraint factor—that reflects potential limitations to service quality and user experience.

[0037] To ensure that each restriction factor is comparable and aggregatable, the first, second, and third state-service restriction factors are normalized, converting all factors into dimensionless values between 0 and 1. The first, second, and third state-service restriction factors are weighted and combined to form the state-service restriction factor. The weight coefficients are typically adjusted based on actual business needs and test data to ensure that the sum of the three weight coefficients is 1. The state-service restriction factor is a comprehensive service status restriction indicator formed by integrating the service status restriction factors from the above multiple dimensions, reflecting the overall service capabilities and user experience online and offline. For example, the first state-service limiting factor is normalized to 0.982, the second state-service limiting factor is normalized to 0.53, and the third state-service limiting factor is normalized to 0.42. The weights are set to 0.4 for the first state-service limiting factor, 0.35 for the second state-service limiting factor, and 0.25 for the third state-service limiting factor. The resulting state-service limiting factor is 0.70. The second and third state-service limiting factors are negative indicators such as load status and complaint rate. The larger the value, the stronger the restriction. Therefore, 1-normalized value is used to reflect the negative impact.

[0038] Through multi-source perception channels, we integrate online service status, offline window load, and additional information related to user experience to establish a multi-dimensional status-service restriction factor. This allows us to promptly identify online or offline service bottlenecks, such as unstable interfaces, window congestion, or geographical barriers, and provide users with the most appropriate service path.

[0039] Furthermore, the present application further comprises the following steps: Perform time node feature analysis on online and offline service platforms, and establish a first matching feature based on the time node feature analysis results; obtain historical service data online and offline, perform data fusion on the historical service data, perform service status trend matching based on the first matching feature, and establish a service status trend matching result; use the real-time status perception result as the actual status, perform status-service restriction prediction based on the service status trend matching result, and establish a compensation restriction factor; update the status-service restriction factor based on the compensation restriction factor.

[0040] Specifically, we conduct time node feature analysis on online and offline service platforms. This involves statistically analyzing service behavior and traffic volume data across different time periods (e.g., hourly, half-hourly, holiday, and weekday) to extract key time-related features. We collect time series data from online and offline service platforms, such as hourly traffic volume, queue length, and processing speed. We use statistical analysis (mean and variance) to extract temporal features and generate primary matching features, such as peak hours, off-peak hours, and holiday patterns. For example, weekends are peak hours from 9:00 AM to 11:00 AM, with an average request volume of 1,500 requests per hour.

[0041] Obtain historical online and offline service data, i.e., data sets such as the volume of online and offline service requests, processing time, success rate, queue length, and user feedback over the past period. Perform data fusion on this historical service data, combining, cleaning, and standardizing online and offline historical service data by time, service type, and other dimensions to form a unified and consistent service dataset. For example, at 9:00 AM on a weekday, the offline counter queue is 20 people, while the number of online requests is 1,400. These data entries are then merged into a single data entry.

[0042] Trend matching is performed based on the first matching feature. This involves matching historical online and offline service data against service status trends based on the first matching feature to obtain a service status trend matching result and determine the service status trend. By analyzing historical service data, such as user visits and service response time, characteristics at different time points are determined, and service status trend matching is performed based on these characteristics. The service status trend matching result is the matching result obtained based on the service status trend matching and is used to predict future service status.

[0043] Real-time state perception results are real-time data on the current state of the online and offline service platforms, reflecting the current actual business load, response status, and other factors. The real-time state perception results are used as the actual state and input into the service state trend matching results to perform state-service constraint prediction. This prediction is used to predict possible service constraints or bottlenecks in the future and generate a compensation constraint factor. For example, if the real-time state perception results predict that the number of queues within the next hour will reach 30, a 50% increase compared to the same period historically, the calculated compensation constraint factor will increase from 0.6 to 0.8. The compensation constraint factor is applied to the original state-service constraint factor to achieve dynamic adjustment, resulting in an updated state-service constraint factor that better reflects the actual and dynamic service state. For example, if the offline business volume is 1,400, the offline queue number is 25, the historical average queue number is 20, and the current difference is 25%. The predicted queue number is 30, the current constraint factor is 0.6, and the calculated compensation constraint factor is 0.8, resulting in an updated constraint factor of 0.66.

[0044] Through time node feature analysis and trend matching, we can achieve precise control of service status in different time periods. Combining the trend information of historical data and the current real-time status, we can enhance the ability to predict future service pressure. By introducing compensating limit factors and dynamically adjusting trend deviations, we can make service limit factors more consistent with actual operating status and reduce misjudgments.

[0045] S300: Execute service scenario perception, and use the service scenario perception results to configure a multi-objective optimization channel.

[0046] Furthermore, the present application S300 includes: Establish a global goal plan for the service platform, and configure a global perception layer of a multi-objective optimization channel according to the global goal plan; extract the service scenario perception results as a service scenario feature vector, wherein the service scenario feature vector includes time features, resource features, user urgency features, geographical accessibility features, and service quality features; establish a weight reconstruction of a multi-objective optimization layer according to the service scenario feature vector; and use the multi-objective optimization layer and the global perception layer to complete the channel configuration of the multi-objective optimization channel.

[0047] Specifically, we will establish an overall goal plan for the service platform, which is a set of optimization goals set based on the overall development goals of the service platform, such as maximizing the benefits of service recipients, making services more efficient, providing more diverse service offerings, and reducing service acquisition costs. Maximizing the benefits of service recipients includes high user satisfaction and a high success rate; more efficient service operations include shorter service times and faster responses, ensuring that online and offline government services are consistent in goals, processes, and standards, providing service recipients with responsive, seamless, high-quality, and efficient government services; more diverse service offerings include a wider range of service items and diverse channels, making services more targeted, service models more integrated, and service channels more diverse; and reducing service acquisition costs include shorter waiting times and lower travel costs for users, ensuring that service recipients directly face fewer departments, go through fewer steps, spend less time, and prepare less materials, thus fully forming a value orientation that prioritizes service operation efficiency.

[0048] According to the global goal planning, the global perception layer of the multi-objective optimization channel is configured. The multi-objective optimization channel is a scheduling control process that performs trade-offs under multiple objectives (the multiple optimization objectives mentioned above). It integrates multiple information levels, evaluation levels, and decision-making levels to comprehensively optimize paths and resources. The global perception layer is a level in the multi-objective optimization channel, used to perceive and obtain the global status information of the service platform, such as the current system load, service resource distribution, and overall user satisfaction.

[0049] Service scenario perception is performed to obtain service scenario perception results, namely the service platform's real-time perception of the service scenario, such as processing time, user urgency, geographic location, etc. The multiple dimensions of information involved in the service scenario are converted into a numerical vector that can be used for calculation and optimization. These include time characteristics (such as whether it is peak period or holiday), resource characteristics (such as window resource shortage and network response speed), user urgency characteristics (such as whether the user clearly marked the emergency declaration), geographical accessibility characteristics (such as the physical distance between the user and offline outlets or transportation convenience), and service quality characteristics (such as the historical evaluation score or complaint rate of a business department).

[0050] Based on the service scenario feature vectors, we reconstruct the weights of the multi-objective optimization layer. Specifically, we determine which feature vectors are prioritized based on the current service scenario and adjust the optimization objective weights accordingly. For example, when the user is in a hurry, we increase the weight of the efficiency objective and decrease the weights of the multiple service objectives. The reconstructed weights are combined with the global perception layer data as input to configure the multi-objective optimization channel that best suits the current service.

[0051] Furthermore, the present application further comprises the following steps: A virtual identity of the user is established based on the user's behavioral portrait, and the virtual identity is used to perform a virtual simulation execution of a non-path optimization reconstruction result to establish virtual simulation execution feedback; real execution feedback from the user based on the path optimization reconstruction result is obtained; dual feedback optimization is performed using the virtual simulation execution feedback and the real execution feedback from the user, and the multi-objective optimization channel is compensated according to the dual feedback optimization result.

[0052] Specifically, the ternary semantic mapping of intent, task, and path, along with the state-service constraint factor, is input into a multi-objective optimization pipeline for path optimization. Taking into account multiple objectives (such as efficiency, satisfaction, and resource cost), the optimal service path is determined. Real-world user feedback is collected after completing transactions based on the path optimization reconstruction results, including actual time consumption, actual satisfaction ratings, and actual queue length. For example, Path A: Full online process → Estimated time consumption of 70 minutes, predicted satisfaction rating of 4.1; Path B: Online reservation + offline express lane → Estimated time consumption of 48 minutes, predicted satisfaction rating of 4.6; Path C: Full offline process → Estimated time consumption of 90 minutes, predicted satisfaction rating of 3.9. Based on the multi-objective optimization pipeline, Path B is selected as the path optimization reconstruction result, resulting in an actual time consumption of 43 minutes, an actual satisfaction rating of 4.8, and an actual queue length of 21 people.

[0053] Based on the user's behavioral profile, a virtual identity is created. This model is a user entity created based on the user's behavioral profile (e.g., preferences, historical behavior, sensitivity, and behavioral patterns) to simulate user decision-making and behavioral responses. This virtual identity is constructed based on the user's platform interaction history, frequently requested items, average wait tolerance, form filling speed, and mobile preference over the past 30 days. This information includes the user's historical preferences, queue length, distance tolerance, and complaint history. In a virtual environment, the service process is simulated using this virtual identity, but no path optimization reconstruction is performed. Feedback from the virtual simulation is collected, such as simulated user satisfaction and service response time. For example, suppose a user's behavioral profile indicates 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 behavioral profile, a virtual identity is created for the user, and user access to the service platform between 9:00 AM and 10:00 AM is simulated in a virtual environment. The virtual simulation reveals that service response times are long during this time period, resulting in low simulated user satisfaction. Based on the feedback from this virtual simulation, the service process is optimized, such as adding service resources between 9:00 and 10:00 a.m. to improve service response time and user satisfaction.

[0054] Dual feedback optimization is performed using the virtual simulation execution feedback and the user's actual execution feedback, and the multi-objective optimization channel is compensated based on the dual feedback optimization results. By comparing the differences between the simulation results and the actual results, problems and deficiencies in the service process are identified and optimized. By adjusting the parameters and weights in the multi-objective optimization channel, user needs are better met and service efficiency is improved. Dual feedback optimization refers to combining simulated feedback with 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.

[0055] S400: taking the intention-matter-path ternary semantic mapping and the state-service restriction factor as input, performing path optimization and reconstruction through the multi-objective optimization channel, and establishing a path optimization and reconstruction result.

[0056] Furthermore, the present application S400 includes: According to the ternary semantic mapping of intention-matter-path, matters are decomposed to establish N atomic matters; the dependency relationships between the N atomic matters are identified to establish a directed service chain structure; according to the multi-objective optimization channel, segmented optimization of the path segments based on the N atomic matters, the directed service chain structure, and the state-service restriction factor is performed to establish a segmented optimization result; and a path optimization reconstruction result is established based on the segmented optimization result.

[0057] The global integration layer of the multi-objective optimization channel is called, and after receiving the segmented optimization result through the global integration layer, execution collaborative analysis of the path segments is performed; and a path optimization reconstruction result is established according to the execution collaborative analysis result.

[0058] A collaborative effect breakthrough threshold is established, and the collaborative effect breakthrough threshold is used to perform a coordinated collaborative evaluation of the segmented optimization results; when the coordinated collaborative evaluation of the segmented optimization results meets the collaborative effect breakthrough threshold, the optimal result of the corresponding segmented optimization result is eliminated to establish a path optimization reconstruction result.

[0059] Specifically, the ternary semantic mapping of intent-matter-path is decomposed into N atomic matters. An atomic matter is the smallest service unit that cannot be further divided, such as submitting an application, reviewing documents, issuing a certificate, approving a document, and obtaining a certificate. The dependency relationships between the N atomic matters are identified, and a directed service chain structure is established. That is, the order and constraint relationships between the atomic matters are identified, and the atomic matters are used as nodes and the dependency relationships are used as directed edges to form a directed service chain structure, which is used to represent the execution order or parallel structure between matters. For example, material upload must be after identity verification, material review must be before certificate issuance, and certificate issuance must be before certificate issuance, etc., resulting in a simple directed service chain structure: name approval → identity verification → material upload → approval → certificate issuance → payment → certificate issuance.

[0060] N atomic items, a directed service chain structure, and state-service constraint factors are input into the multi-objective optimization channel for segment-by-segment optimization. This involves optimizing the path segment by segment, selecting the optimal handling method or execution resource for each segment to generate a comprehensive optimal path. Specifically, each segment is optimized according to the multi-objective optimization channel to produce a segment-by-segment optimization result. The global integration layer of the multi-objective optimization channel receives the segment-by-segment optimization results and aggregates them for overall execution synergy analysis. The segment-by-segment optimization result represents a locally optimal handling solution obtained through independent optimization of each segment (i.e., a specific stage of the business process). Path segment execution synergy analysis evaluates the synergy between the solutions across the various segments to determine whether these locally optimal solutions can work together efficiently and effectively, thus avoiding the problem of local optima leading to poor overall performance. The global integration layer calculates and compares synergy metrics across the segments, such as resource contention, time alignment, and service quality, to determine whether these locally optimal solutions can smoothly coordinate and meet overall service objectives.

[0061] 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, combined with the current status information (such as offline window load, online service accessibility, user geographic location, etc.), and based on pre-set multi-objective optimization indicators (such as time cost, service quality, user satisfaction, etc.), the multi-objective optimization algorithm is used to independently find the optimal processing path or execution plan for the segment. For each path segment, among all optional processing methods and resources, the trade-offs of each optimization goal are evaluated and weighed, and the optimal path plan for the segment is finally determined. After completing the independent optimization of all path segments, the optimal plans of each path segment are spliced in sequence according to the chain structure to generate a comprehensive optimal path for the entire business process.

[0062] Set a synergy effect breakthrough threshold to measure the rationality and execution efficiency of the overall path segment coordination. If the combination of solutions for one or more path segments does not meet the synergy effect breakthrough threshold in the synergy evaluation, that is, the synergy effect is poor, it means that the current combination of local optimal solutions cannot bring about a good overall execution effect. Eliminate the local optimal results that do not meet the synergy standards, that is, eliminate the optimal solutions for certain path segments, and possibly return to suboptimal solutions or re-search for solutions to make the overall process more coordinated and avoid global degradation caused by local optimality. In other words, in segmented optimization, each segmented optimization may contain multiple solutions. At this time, the collaborative analysis is a global analysis process, and the local optimal solution may not necessarily be the overall optimal solution.

[0063] Through global integration and coordinated adjustments, an optimized and reconstructed path optimization reconstruction result is formed, which serves as the service platform's final execution plan for the business process. The path optimization reconstruction result is the final comprehensive optimization path formed after global integration and adjustment. By splitting the business process into atomic items and modeling dependencies, a visual and logically clear directed service chain structure is constructed to facilitate fine-grained optimization. In combination with state-service constraint factors, segmented optimization is achieved, allowing each path segment to flexibly adapt to dynamic resources and constraints. Through collaborative analysis and elimination mechanisms, the global integration layer solves the problem of local optimality leading to overall suboptimality, ensuring global optimization and execution coordination of the path, thereby significantly improving the efficiency and user satisfaction of online and offline service processes, and optimizing resource utilization and service quality.

[0064] S500: Performing collaborative optimization management of online and offline services based on the path optimization and reconstruction results.

[0065] Specifically, based on the results of path optimization and reconstruction, business process items suitable for online processing are automatically assigned to online channels, while items suitable for offline processing are scheduled to physical service points. Factors such as user preferences, geographic location, and resource availability are also considered to rationally schedule the execution time and sequence of each step. The platform dynamically monitors the service capabilities of the online platform (such as server load and response time) and the staff and equipment resource status of offline service points. Service allocation is adjusted based on real-time data feedback to prevent resource overload or idleness. Seamless integration between online and offline processing stages is ensured. For example, approval results completed online are automatically pushed to offline service points, and information on offline processing completion is promptly fed back to the online system to avoid duplicate submissions and waiting delays. Path reconstruction leverages analysis of time characteristics and user urgency to optimize the timing of online and offline integration, reducing user wait times and processing steps, thereby improving overall processing efficiency and satisfaction. The monitoring platform collects real-time execution data and user feedback from both online and offline businesses. Combined with the results of path optimization, the business collaboration plan is continuously adjusted and optimized, forming a closed-loop management system.

[0066] For example, items A and B are suitable for online processing, which is expected to save users an average of 20 minutes of waiting time and 5 seconds of processing time. Items C and D need to be handled offline, with 5 staff members assigned to the on-site service window, and the average processing time for each item is 15 minutes. Automatic information push is set up in the online and offline connection links, and the information transmission delay is less than 2 seconds. Through collaborative optimization management, online user satisfaction increased by 45%, and the average completion time decreased by 15%; offline window resource utilization increased to 85%, and the number of queues during peak hours decreased by 20%; the overall business processing cycle was shortened by 5%, and the user complaint rate decreased by 50%. Through online and offline business collaborative optimization management based on path optimization reconstruction results, efficient connection and resource optimization between service channels are achieved, effectively avoiding resource waste and redundant processing processes, 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.

[0067] In summary, the online and offline business collaborative optimization method of the service platform provided by this application has the following beneficial effects: By receiving the user's natural language input data, performing semantic recognition and item extraction based on the natural language input data, and constructing a ternary semantic mapping of intent, item, and path based on the service knowledge graph; activating the multi-source perception channel of the service platform, performing real-time perception of online and offline status, and establishing a status-service restriction factor; performing service scenario perception, and configuring a multi-objective optimization channel using the service scenario perception results; using the ternary semantic mapping of intent, item, and path and the status-service restriction factor as input, performing path optimization and reconstruction through the multi-objective optimization channel, and establishing a path optimization reconstruction result; and performing online and offline business collaborative optimization management based on the path optimization and reconstruction result. In other words, by performing semantic recognition and item extraction on the natural language input by the user, constructing a ternary semantic mapping of intent, item, and path, performing online and offline status perception through the multi-source perception channel, configuring a multi-objective optimization channel based on the service scenario perception results, and replanning the service path, the dynamic collaborative reconstruction and intelligent scheduling of online and offline service processes are achieved, improving service quality and efficiency.

[0068] Example 2: Based on the same inventive concept as the online and offline business collaborative optimization method of the service platform in the above-mentioned Example 1, this application also provides an online and offline business collaborative optimization system for the service platform, please refer to the attached Figure 2 , the online and offline business collaborative optimization system of the service platform includes: The natural language processing module 11 is used to 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 intention-item-path based on the service knowledge graph; the real-time status perception module 12 is used to activate the multi-source perception channel of the service platform, perform real-time perception of online and offline status, and establish a status-service restriction factor; the service scenario perception module 13 is used to perform service scenario perception, and configure a multi-objective optimization channel using the service scenario perception result; the path optimization and reconstruction module 14 is used to take the ternary semantic mapping of intention-item-path and the status-service restriction factor as input, perform path optimization and reconstruction through the multi-objective optimization channel, and establish a path optimization and reconstruction result; the collaborative management module 15 is used to perform online and offline business collaborative optimization management based on the path optimization and reconstruction result.

[0069] Furthermore, the natural language processing module 11 in the online and offline business collaborative optimization system of the service platform is also used to: The natural language input data is subjected to text preprocessing, and the text preprocessing includes word segmentation preprocessing, standardization processing, named entity recognition processing, and sentiment tendency word recognition processing; intent recognition, entity recognition, and relationship extraction are performed based on the text preprocessing results to construct a ternary semantic mapping of intent-matter-path.

[0070] Furthermore, the real-time state perception module 12 in the online and offline business collaborative optimization system of the service platform is also used to: Utilize the online perception channel of the service platform to perceive the online business reachability and establish a first state-service restriction factor; utilize the image perception channel of the service platform to perform real-time load perception of the offline window and establish a second state-service restriction factor; utilize the additional perception channel of the service platform to perform location reachability and historical complaint data perception and establish a third state-service restriction 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.

[0071] Furthermore, the real-time state perception module 12 in the online and offline business collaborative optimization system of the service platform is also used to: Perform time node feature analysis on online and offline service platforms, and establish a first matching feature based on the time node feature analysis results; obtain historical service data online and offline, perform data fusion on the historical service data, perform service status trend matching based on the first matching feature, and establish a service status trend matching result; use the real-time status perception result as the actual status, perform status-service restriction prediction based on the service status trend matching result, and establish a compensation restriction factor; update the status-service restriction factor based on the compensation restriction factor.

[0072] Furthermore, the service scenario perception module 13 in the online and offline business collaborative optimization system of the service platform is also used to: Establish a global goal plan for the service platform, and configure a global perception layer of a multi-objective optimization channel according to the global goal plan; extract the service scenario perception results as a service scenario feature vector, wherein the service scenario feature vector includes time features, resource features, user urgency features, geographical accessibility features, and service quality features; establish a weight reconstruction of a multi-objective optimization layer according to the service scenario feature vector; and use the multi-objective optimization layer and the global perception layer to complete the channel configuration of the multi-objective optimization channel.

[0073] Furthermore, the service scenario perception module 13 in the online and offline business collaborative optimization system of the service platform is also used to: A virtual identity of the user is established based on the user's behavioral portrait, and the virtual identity is used to perform a virtual simulation execution of a non-path optimization reconstruction result to establish virtual simulation execution feedback; real execution feedback from the user based on the path optimization reconstruction result is obtained; dual feedback optimization is performed using the virtual simulation execution feedback and the real execution feedback from the user, and the multi-objective optimization channel is compensated according to the dual feedback optimization result.

[0074] Furthermore, the path optimization and reconstruction module 14 in the online and offline business collaborative optimization system of the service platform is also used to: According to the ternary semantic mapping of intention-matter-path, matters are decomposed to establish N atomic matters; the dependency relationships between the N atomic matters are identified to establish a directed service chain structure; according to the multi-objective optimization channel, segmented optimization of the path segments based on the N atomic matters, the directed service chain structure, and the state-service restriction factor is performed to establish a segmented optimization result; and a path optimization reconstruction result is established based on the segmented optimization result.

[0075] Furthermore, the path optimization and reconstruction module 14 in the online and offline business collaborative optimization system of the service platform is also used to: The global integration layer of the multi-objective optimization channel is called, and after receiving the segmented optimization result through the global integration layer, execution collaborative analysis of the path segments is performed; and a path optimization reconstruction result is established according to the execution collaborative analysis result.

[0076] Furthermore, the path optimization and reconstruction module 14 in the online and offline business collaborative optimization system of the service platform is also used to: A collaborative effect breakthrough threshold is established, and the collaborative effect breakthrough threshold is used to perform a coordinated collaborative evaluation of the segmented optimization results; when the coordinated collaborative evaluation of the segmented optimization results meets the collaborative effect breakthrough threshold, the optimal result of the corresponding segmented optimization result is eliminated to establish a path optimization reconstruction result.

[0077] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The online and offline business collaborative optimization method and specific examples of the service platform in Example 1 are also applicable to the online and offline business collaborative optimization system of the service platform in this embodiment. Through the above detailed description of the online and offline business collaborative optimization method of the service platform, those skilled in the art can clearly understand the online and offline business collaborative optimization system of the service platform in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0078] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, for those skilled in the art, several improvements and modifications can be made to the present application without departing from the principles of the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A collaborative optimization method for online and offline services on a service platform, characterized in that: include: Receive natural language input data from the user, perform semantic recognition and event extraction based on the natural language input data, and construct a ternary semantic mapping of intent-event-path based on the service knowledge graph; Activate the multi-source perception channel of the service platform, perform real-time perception of online and offline status, and establish status-service limiting factors; Perform service scenario perception and use the service scenario perception results to configure multi-objective optimization channels; Taking the intention-event-path ternary semantic mapping and the state-service constraint factor as input, performing path optimization and reconstruction through the multi-objective optimization channel, and establishing a path optimization and reconstruction result; Based on the path optimization and reconstruction results, online and offline business collaborative optimization management is carried out.

2. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The multi-source perception channel of the activation service platform performs real-time perception of online and offline status and establishes status-service limiting factors, including: Utilize the online perception channel of the service platform to perceive the online business reachability and establish the first state - service restriction factor; Leveraging the service platform's image perception channel, we perform real-time load sensing of offline windows and establish the second state - the service limiting factor. Utilize the additional perception channel of the service platform to perform location accessibility and historical complaint data perception, and establish the third state - service restriction factor; A state-service limiting factor is established according to the first state-service limiting factor, the second state-service limiting factor, and the third state-service limiting factor.

3. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The execution of real-time online and offline state perception and establishment of state-service limiting factors includes: Perform time node feature analysis on online and offline service platforms, and establish a first matching feature based on the time node feature analysis results; Acquire historical service data online and offline, perform data fusion on 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 limit prediction based on service state trend matching results and establish compensation limit factors; The state-service limiting factor is updated according to the compensation limiting factor.

4. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The configuration of a multi-objective optimization channel using the service scenario perception results includes: Establishing a global target plan for the service platform and configuring a global perception layer of the multi-objective optimization channel according to the global target plan; Extracting the service scenario perception result into a service scenario feature vector, wherein the service scenario feature vector includes time features, resource features, user urgency features, geographical accessibility features, and service quality features; Establishing weight reconstruction of a multi-objective optimization layer according to the service scenario feature vector; The multi-objective optimization layer and the global perception layer are used to complete the channel configuration of the multi-objective optimization channel.

5. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The online and offline business collaborative optimization management based on the path optimization and reconstruction results includes: Establishing a virtual identity of the user based on the user's behavior profile, performing a virtual simulation execution of the non-path optimization reconstruction result on the virtual identity, and establishing virtual simulation execution feedback; Obtaining actual user execution feedback based on the path optimization and reconstruction results; The virtual simulation execution feedback and the user's real execution feedback are used to perform dual feedback optimization, and the multi-objective optimization channel is compensated according to the dual feedback optimization result.

6. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The performing path optimization and reconstruction through the multi-objective optimization channel and establishing a path optimization and reconstruction result includes: Decompose the items according to the ternary semantic mapping of intention-item-path to establish N atomic items; Identify dependencies between the N atomic events and establish a directed service chain structure; Performing segmented optimization of path segments based on N atomic matters, the directed service chain structure, and the state-service constraint factor according to the multi-objective optimization channel, and establishing segmented optimization results; The path optimization reconstruction results are established based on the segment optimization results.

7. The online and offline business collaborative optimization method of the service platform according to claim 6, characterized in that: The step of establishing a path optimization and reconstruction result based on the segmented optimization result includes: Invoking the global integration layer of the multi-objective optimization channel, and performing execution coordination analysis of the path segments after the global integration layer receives the segment optimization results; The path optimization and reconstruction results are established based on the collaborative analysis results.

8. The online and offline business collaborative optimization method of the service platform according to claim 7, characterized in that: The establishing of the path optimization and reconstruction result according to the collaborative analysis result includes: Establishing a collaborative effect breakthrough threshold, and using the collaborative effect breakthrough threshold to perform collaborative evaluation of the segmented optimization results; When the coordinated synergy evaluation of the segmented optimization results satisfies the synergy effect breakthrough threshold, the optimal result of the corresponding segmented optimization results is eliminated to establish a path optimization reconstruction result.

9. The online and offline business collaborative optimization method of the service platform according to claim 1, characterized in that: The performing semantic recognition and item extraction according to the natural language input data includes: Performing text preprocessing on the natural language input data, wherein the text preprocessing includes 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-matter-path.

10. The online and offline business collaborative optimization system of the service platform is characterized by: The steps for implementing the online and offline business collaborative optimization method of the service platform according to any one of claims 1 to 9, wherein the online and offline business collaborative optimization system of the service platform comprises: A natural language processing module is used to receive natural language input data from users, perform semantic recognition and event extraction based on the natural language input data, and construct a ternary semantic mapping of intent-event-path based on the service knowledge graph; The real-time state perception module is used to activate the multi-source perception channel of the service platform, perform real-time online and offline state perception, and establish state-service restriction factors; A service scenario perception module is used to perform service scenario perception and configure a multi-objective optimization channel using the service scenario perception results; a path optimization and reconstruction module, configured to take the intention-event-path ternary semantic mapping and the state-service constraint factor as input, perform path optimization and reconstruction through the multi-objective optimization channel, and establish a path optimization and reconstruction result; The collaborative management module is used to perform collaborative optimization management of online and offline businesses based on the path optimization and reconstruction results.

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