Hotel front-end and back-end collaborative customer service information circulation management method and system

Abstract

The invention provides a hotel front-end and back-end collaborative customer service information circulation management method and system, and is applied to the field of information data processing. According to the invention, through a multi-channel information access and classification mechanism preset based on the hotel terminal, unified access and standardized processing of client multi-source service requests are realized, and the problems of repeated data entry and errors caused by different information formats are effectively solved. By judging the similarity of the service requests, repeated processing is avoided, the data consistency and the processing efficiency are improved, the request source information is converted into the service mirror image, and the autonomous unit is generated in real time in combination with the pre-selected service branch of the customer service team, so that the service process is more accurate and controllable.

Description

Technical Field

[0001] The present invention relates to the field of information data processing, and particularly to a method and system for managing the flow of customer service information in coordination between the front and back ends of a hotel. Background Art

[0002] In modern hotel management, the customer service experience has become a key factor in measuring the operational efficiency and brand competitiveness of a hotel. In traditional hotel customer service, the front desk is mainly responsible for receiving, registering, and communicating with customers, while the back-end service departments such as maintenance, cleaning, guest rooms, and security are responsible for the specific service execution.

[0003] However, customer service requests may come from multiple channels such as phone calls, front desk oral reports, mobile apps, WeChat mini-programs, etc. Existing terminal systems are difficult to uniformly access and process, and the information formats are inconsistent, resulting in situations of duplicate entry or errors when data is shared to the terminal system. Summary of the Invention

[0004] The present invention aims to solve the problem that customer service requests may come from multiple channels, and existing terminal systems are difficult to uniformly access and process, with inconsistent information formats, resulting in situations of duplicate entry or errors when data is shared to the terminal system, and provides a method and system for managing the flow of customer service information in coordination between the front and back ends of a hotel.

[0005] The present invention adopts the following technical means to solve the technical problems:

[0006] The present invention provides a method for managing the flow of customer service information in coordination between the front and back ends of a hotel, including:

[0007] Classifying service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset in the hotel terminal;

[0008] Judging whether the service requests belong to the same service content;

[0009] If so, identifying the source information of the service request, converting the source information into corresponding service mirror information, pushing the service mirror information to the customer service team preset in the hotel terminal, and generating an autonomous unit of the service request to the hotel terminal in real time according to the service branch preselected by the customer service team for the service mirror information, where the autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status;

[0010] Judging whether the autonomous unit can match the execution unit preset in the hotel terminal, where the execution unit specifically includes a human type and a device type;

[0011] If not, activate the information recognition engine preset in the hotel terminal. Through the information recognition engine, construct a fuzzy request corresponding to the autonomous unit. Based on the fuzzy request, divide the service flow of the execution unit. Based on the collaboration density and time lag of the service flow, generate a task congestion heat map for the service request. According to the task congestion heat map, dynamically schedule the execution resources of the hotel terminal. Wherein, the service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path plan, and the task congestion heat map specifically includes a status map and a collaboration heat map.

[0012] Further, in the step of identifying the source information of the service request, converting the source information into corresponding service mirror information, and pushing the service mirror information to the customer service team preset in the hotel terminal, it further includes:

[0013] Based on the content structure of the source information, construct a clone information object for the service request. Wherein, the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone;

[0014] Judge whether the states among the clone information objects are unified;

[0015] If so, activate the management mechanism of the clone information object. According to the management mechanism, allocate the processing cycle of the clone information object, and dynamically generate a view aggregator for the clone information object. Wherein, the management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking, and the processing cycle specifically includes in generation, dispatched, in processing, pending confirmation, and closed.

[0016] Further, after the step of generating the autonomous unit of the service request to the hotel terminal in real time according to the service branch pre-selected by the customer service team for the service mirror information, it further includes:

[0017] Based on the task collaboration type of the autonomous unit, generate a multi-channel collaborative distribution instruction for the customer service team. Wherein, the task collaboration type specifically includes engineering maintenance, cleaning service, and guest room service;

[0018] Judge whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team;

[0019] If not, add the multi-channel collaborative distribution instruction to a preset delay processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delay processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes single-channel tasks, single-channel tasks with subsequent supplementary dispatch, and multi-channel collaborative tasks.

[0020] Further, in the step of activating the information recognition engine preset in the hotel terminal and constructing the fuzzy request corresponding to the autonomous unit through the information recognition engine, it further includes:

[0021] Based on the confidence mechanism preset in the information recognition engine, obtain the virtual intent data after the fuzzy request is input into the information recognition engine;

[0022] Judge whether the virtual intent data reaches the confidence threshold preset by the confidence mechanism;

[0023] If not, convert the fuzzy request into a preset refrigeration request, retain the intent clue of the refrigeration request within a preset time period, collect the information keywords repeatedly entered by the hotel customer into the hotel terminal, and dynamically wake up the refrigeration request according to the same word frequency of the information keywords and the intent clue.

[0024] Further, in the step of judging whether the service requests belong to the same service content, it further includes:

[0025] Based on the space attribute preset in the hotel terminal, obtain the request interval of the service request, where the space attribute specifically includes room location and floor location;

[0026] Judge whether the request interval is within a preset time window;

[0027] If so, identify the trigger frequency of the service request, obtain the group similarity behavior corresponding to the space attribute according to the trigger frequency, and place a preset prompt content in the public area preset in the hotel terminal according to the group similarity behavior, where the prompt content is specifically operation suggestions and service method recommendations.

[0028] Further, in the step of judging whether the autonomous unit can match the execution unit preset in the hotel terminal, it further includes:

[0029] Based on the feedback processing status of the autonomous unit, construct the processing progress of the service request, where the feedback processing status specifically includes received order, in transit, completed, and customer confirmation;

[0030] Judge whether the processing progress is stagnant;

[0031] If so, obtain the regional resource density of the autonomous unit, dynamically adjust the service density of each area of the hotel according to the regional resource density, and adaptively adjust the service cycle of the service request according to the service density.

[0032] Further, before the step of classifying the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset on the hotel terminal, it further includes:

[0033] Identify the identity label preset by the hotel customer;

[0034] Judge whether the identity label belongs to the preset check-in identity;

[0035] If not, based on the identity label, limit the service level of the hotel customer on the hotel terminal, and classify the available service content of the hotel customer according to the service level.

[0036] The present invention also provides a customer service information flow management system for hotel front-end and back-end collaboration, including:

[0037] A classification module for classifying the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset on the hotel terminal;

[0038] A judgment module for judging whether the service requests belong to the same service content;

[0039] An execution module for, if so, identifying the source information of the service request, converting the source information into corresponding service mirror information, pushing the service mirror information to the customer service team preset on the hotel terminal, and generating the autonomous unit of the service request to the hotel terminal in real time according to the service branch pre-selected by the customer service team for the service mirror information, where the autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status;

[0040] A second judgment module for judging whether the autonomous unit can match the execution unit preset on the hotel terminal, where the execution unit specifically includes a human type and a device type;

[0041] A second execution module for, if not, activating the information recognition engine preset on the hotel terminal, constructing a fuzzy request corresponding to the autonomous unit through the information recognition engine, dividing the service flow of the execution unit according to the fuzzy request, generating a task congestion heat map of the service request based on the collaboration density and time lag of the service flow, and dynamically scheduling the execution resources of the hotel terminal according to the task congestion heat map, where the service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path planning, and the task congestion heat map specifically includes a status map and a collaboration heat map.

[0042] Further, the execution module further includes:

[0043] A construction sub-module, configured to construct a clone information object of the service request based on the content structure of the source information, where the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multi-language channel adaptation clone;

[0044] A judgment sub-module, configured to judge whether the states among the clone information objects are unified;

[0045] An execution sub-module, configured to, if so, activate the management mechanism of the clone information object, and according to the management mechanism, allocate a processing cycle of the clone information object, and dynamically generate a view aggregator of the clone information object, where the management mechanism specifically includes read-only fields, differential update, permission control, and version tracking, and the processing cycle specifically includes in generation, dispatched, in processing, to be confirmed, and closed.

[0046] Further, it further includes:

[0047] A generation module, configured to generate a multi-channel collaborative distribution instruction for the customer service team based on the task collaboration type of the autonomous unit, where the task collaboration type specifically includes engineering maintenance, cleaning service, and room service;

[0048] A third judgment module, configured to judge whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team;

[0049] A third execution module, configured to, if not, add the multi-channel collaborative distribution instruction to a preset delayed processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delayed processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes single-channel task, single-channel task with subsequent supplementary dispatch, and multi-channel collaborative task.

[0050] The present invention provides a method and system for managing the flow of customer service information in the front and back ends of a hotel, and has the following beneficial effects:

[0051] Through the multi-channel information access and classification mechanism preset on the hotel terminal, the present invention realizes the unified access and standardized processing of service requests from multiple sources of customers, effectively solving the problems of duplicate data entry and errors caused by inconsistent information formats. By judging the similarity of service requests, duplicate processing is avoided, data consistency and processing efficiency are improved, the request source information is converted into a service mirror, and combined with the preselected service branches of the customer service team, an autonomous unit is generated in real time, making the service process more accurate and controllable. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flowchart of an embodiment of the method for managing the flow of customer service information in the coordination between the front and back ends of the hotel according to the present invention;

[0053] Figure 2 It is a structural block diagram of an embodiment of the system for managing the flow of customer service information in the coordination between the front and back ends of the hotel according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings.

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] Referring to the attached Figure 1 , a method for managing the flow of customer service information in the coordination between the front and back ends of the hotel in an embodiment of the present invention includes:

[0057] S1: Classify the service requests uploaded by hotel customers to the hotel terminal based on the preset information access channels of the hotel terminal;

[0058] S2: Judge whether the service requests belong to the same service content;

[0059] S3: If so, identify the source information of the service request, convert the source information into corresponding service mirror information, push the service mirror information to the customer service team preset on the hotel terminal, and generate an autonomous unit of the service request to the hotel terminal in real time according to the service branches preselected by the customer service team for the service mirror information. The autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status;

[0060] S4: Determine whether the autonomous unit can match the execution units preset in the hotel terminal, where the execution units specifically include human - type and equipment - type;

[0061] S5: If not, activate the information recognition engine preset in the hotel terminal. Through the information recognition engine, construct a fuzzy request corresponding to the autonomous unit, divide the service flow of the execution unit according to the fuzzy request, generate a task congestion heat map of the service request based on the collaborative density and time lag of the service flow, and dynamically schedule the execution resources of the hotel terminal according to the task congestion heat map. The service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path plan, and the task congestion heat map specifically includes a status map and a collaborative heat map.

[0062] In this embodiment, the system classifies service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset in the hotel terminal, and then the system determines whether these service requests belong to the same service content to execute corresponding steps. For example, when the system determines that the service requests uploaded by hotel customers to the hotel terminal do not belong to the same service content, the system will consider that there are obvious differences in intention, execution method, resource requirements or processing logic among the multiple service requests received currently. Different customers or different rooms have submitted requests with similar content but actually different goals. The system will construct independent service mirror information structures for each request with different service contents, maintaining logical isolation between requests to avoid confusion. Each mirror binds its source, timestamp, and context data. At the same time, different service requests are distributed to professional customer service groups to avoid resource conflicts or duplicate responses caused by "one order being sent to multiple recipients". And if multiple different requests come from the same customer (or room) and there is no conflict, they can be guided to be merged into a "parallel service flow" to improve efficiency, otherwise they are scheduled in order of priority. For example, when the system determines that the service requests uploaded by hotel customers to the hotel terminal belong to the same service content, at this time the system will consider that there may be duplicate service events among the multiple service requests received currently. The system will identify the source information of these service requests, convert this source information into corresponding service mirror information, and push this service mirror information to the customer service team preset in the hotel terminal. According to the service branches pre-selected by different customer service teams for the service mirror information, an autonomous unit of the service request is generated in real time to the hotel terminal. The autonomous unit specifically includes an event ID, location attribute, request type, and processing status. By identifying the source information of multiple requests, the system merges multiple requests belonging to the same service content into one autonomous unit, avoiding multiple duplicate work orders from entering the scheduling queue. This deduplication mechanism can prevent the repeated invocation of human resources and equipment resources. Especially during peak periods or when customers perform continuous operations (such as repeatedly pressing the service button), it can significantly reduce the system congestion rate and execution interference, improving the overall service coordination and execution efficiency. At the same time, converting the original request into standardized service mirror information can unify the expression methods from different sources (such as App, voice, front desk), avoiding misunderstandings of service intentions by customer service or the system. The service mirror contains a unified field structure, such as request type, location attribute, timestamp, etc., laying an information foundation for generating autonomous units later, ensuring that the information received at the execution level is clear, complete, and operable. And different customer service teams can preset the processing branches corresponding to the service mirror (such as delivery, repair, cleaning, etc.). The system automatically matches the optimal service path according to the mirror content, realizing intelligent classification and accurate distribution of service requests. The generated autonomous unit not only has a clear event ID and status, but can also be quickly routed to the corresponding position or equipment execution node, thus realizing a high degree of automation and refined management of the service process;Subsequently, the system determines whether these autonomous units can match the execution units preset by the hotel terminal. The execution units specifically include labor types and equipment types to execute corresponding steps. For example, when the system determines that the autonomous unit of the service request can match the execution unit preset by the hotel terminal, the system will consider that the current task can be recognized by the system and the corresponding execution entity can be found. The system will dispatch the task to the mobile terminals of the corresponding post personnel (such as the cleaner's PDA, engineering maintenance tablet), including detailed task content, location, and response time limit, trigger device instructions (such as intelligent delivery robot scheduling, elevator permission opening, air conditioning regulation system automatic response, etc.), execute specific operations, and at the same time update the task status from "pending" to "executing" or "issued", bind the identifier of the execution unit (such as personnel ID, equipment number) to ensure clear and controllable subsequent tracking, and continuously monitor the execution feedback of the execution unit, regularly collect the execution progress, completion status, exception alarms, etc., and feedback them to the hotel central control system or the customer service terminal. When necessary, it can support automatic reminder or exception transfer to manual processing. For example, when the system determines that the autonomous unit of the service request cannot match the execution unit preset by the hotel terminal, at this time, the system will consider that although the current task can be recognized, the corresponding execution entity cannot be found at present. The system will activate the information recognition engine preset by the hotel terminal. Through this information recognition engine, construct a fuzzy request corresponding to the autonomous unit. Based on these fuzzy requests, divide the service flow of the execution unit. The service flow specifically includes the request intention structure, service task decomposition chain, and execution path planning. Based on the collaborative density and time lag of these service flows, generate a task congestion heat map. The task congestion heat map specifically includes a status map and a collaborative heat map. The system identifies its potential intention through the information recognition engine, constructs a fuzzy request, avoids the interruption problem of "no execution path after request recognition", and through intention understanding and task decomposition, can further intelligently generate an execution plan to fill the shortcoming of the traditional system's processing ability for non-standard tasks. At the same time, by constructing a "service flow" (including request intention structure, task decomposition chain, and execution path planning), the system can refine fuzzy or composite requests into executable standard subtasks, plan and schedule paths according to priority and reachability. This way, complex tasks can be decomposed into multi-point concurrent processing units, improving execution efficiency and reducing single-point bottlenecks. And combined with the collaborative density and time lag of the service flow, generate a task congestion heat map (including a status map and a collaborative heat map) to intuitively display the task distribution and resource pressure, which can assist the system in implementing flow control, detour scheduling, or task rearrangement in high-load areas, avoiding resource overload in some areas while other areas are idle, and ultimately achieving the optimization of resource allocation and the balance of service response.

[0063] It should be noted that the source information of the service request is identified, the source information is converted into corresponding service mirror information, the service mirror information is pushed to the customer service team preset in the hotel terminal, and the autonomous unit of the service request is generated in real time according to the service branch preselected by the customer service team for the service mirror information and sent to the hotel terminal. The specific example is as follows:

[0064] Suppose Mr. Zhang, a guest, checks into Room 1802 of the hotel. He sends the same service request through three different methods at 10:06:

[0065] Click the "Add a bath towel" button through the hotel APP;

[0066] Call the room service and say "Please send a bath towel here";

[0067] Say to the voice dialogue speaker "Can you send me another white bath towel?";

[0068] Then the detailed process of the system processing is as follows:

[0069] Step 1, Identify the source information of the service request. The system receives the service requests from different channels respectively and records the following source information:

[0070] Channel Source information field APP Customer ID: ZH1802, Timestamp: 10:06:12, Room number: 1802, Request code: REQ001 Phone Call ID: #1802TEL, Transcription text: "Please send a bath towel over", Room number identification: 1802 Voice assistant Device ID: VOICE-1802, Voice text: "Can you send another white bath towel", Room number: 1802

[0071] The system will judge that these requests have the same source (the same customer, the same room number, and close time), and proceed to the next step;

[0072] Step 2, Convert to service mirror information. The system standardizes different expressions into a structured "service mirror information":

[0073] {

[0074] "room_number":"1802",

[0075] "guest_id":"ZH1802",

[0076] "intent":"Add delivery item",

[0077] "item":"Bath towel",

[0078] "item_color":"White",

[0079] "channel":["APP","Phone","Voice assistant"],

[0080] "timestamp_range":["10:06:12","10:06:45"],

[0081] "priority": "Normal",

[0082] "service_id": "SRV-MIRROR-001"

[0083] }

[0084] This step completes the process of unifying requests such as "unstructured voice, graphic clicks, and semantic ambiguity" into "service mirror information".

[0085] Step 3: Push to the customer service team. The system determines that it is a "room additional item delivery" based on the mirror content and automatically pushes it to the customer service team of the "room service group". This team's system has a preset response branch strategy. For example, if the request is for "bath towel", the delivery robot is given priority. If the robot is busy, a floor attendant is dispatched for manual delivery.

[0086] Step 4: Generate an autonomous unit. After receiving the mirror information, the customer service system automatically generates a schedulable service autonomous unit as follows:

[0087] {

[0088] "event_id": "EVT-1802-20250610-001",

[0089] "location": {

[0090] "room": "1802",

[0091] "floor": 18

[0092] },

[0093] "request_type": "Deliver bath towel",

[0094] "item_detail": {

[0095] "name": "Bath towel",

[0096] "color": "White",

[0097] "quantity": 1

[0098] },

[0099] "channel_merged": ["APP", "TEL", "VOICE"],

[0100] "status": "Pending scheduling",

[0101] "dispatch_type": "Device priority",

[0102] "expected_eta": "5 minutes"

[0103] This unit is then read by the delivery robot scheduling system to perform the delivery operation; if the scheduling fails, it is transferred to the manual task pool.

[0104] In summary, the above example content abstracts the fuzzy requests from three different channels into a service image, avoiding duplicate dispatching. At the same time, due to the consistent information source and different expressions, the system can avoid resource waste or task conflicts caused by repeated requests, and there is no manual intervention in the whole process. The system automatically responds and dispatches based on "semantic standardization + task autonomous unit", greatly improving the response efficiency and customer satisfaction.

[0105] It should be added that the information recognition engine preset in the hotel terminal is activated. Through the information recognition engine, a fuzzy request corresponding to the autonomous unit is constructed. Based on the fuzzy request, the service flow of the execution unit is divided. Based on the collaborative density and time lag of the service flow, a task congestion heat map of the service request is generated. According to the task congestion heat map, the execution resources of the hotel terminal are dynamically scheduled. The specific example is as follows:

[0106] Suppose the customer is in room 1908 of the hotel and says through the voice assistant: "There's something wrong with the room. Can you come and take a look?" Since this request has a fuzzy semantics and cannot be directly classified into the categories of "delivery", "repair", or "customer complaint", the system will perform a detailed breakdown of the steps:

[0107] First, the information recognition engine is activated. The system recognizes the keywords "something wrong" and "take a look" in the voice, determines it as an unstructured request, automatically activates the information recognition engine preset in the terminal, and starts the fuzzy request construction process. By constructing the fuzzy request, the system generates the following "fuzzy request object" in combination with the context data:

[0108] {

[0109] "room number": "1908",

[0110] "trigger time": "2025-06-10T11:42:00",

[0111] "keywords": ["something wrong", "take a look"],

[0112] "historical behaviors": ["ordered room water delivery 2 hours ago", "complained about air conditioner noise at night"],

[0113] "room status data": ["door magnetic abnormal multiple switches", "temperature and humidity sensor short-time disconnection"],

[0114] "System Determined Intention": "Potential Equipment Abnormality / Emotional Comfort Need / Safety Hazard Investigation"}

[0115] Then the system divides the service flow and generates a service flow based on the above fuzzy request. The process includes identifying that the request intention structure is a "non-standard safety request", and suggesting service types: ① Room status detection → ② Security personnel visit → ③ Front desk follow-up inquiry. The background retrieves room sensor data and access control records, arranges engineering personnel to conduct safety inspections on air conditioners and power supplies, notifies the front desk to call the customer to confirm if everything is normal, arranges the floor service robot to deliver an access control reminder card first, and arranges the nearest staff to follow up and visit the customer's room to reduce waiting time;

[0116] Subsequently, a task congestion heat map is generated, and the heat map is constructed as follows:

[0117] Status map data:

[0118] Node Current task count Average response latency Distance to Room 1908 Security A (1902) 1 3 minutes 1 room Engineering B (downstairs) 3 15 minutes 1 floor Service robot C Idle Immediate Same floor

[0119] Collaborative heat map result:

[0120] Multiple "non-standard requests" on the current floor are concentrated in the range of 1910 - 1906, indicating that there may be a systematic failure in the area, and it is necessary to synchronously dispatch the engineering department;

[0121] Finally, the system dynamically schedules the execution resources, and the system makes the following decisions:

[0122] Arrange service robot C to immediately deliver the "Room Safety Reminder Card" to soothe the customer first. After staff A finishes handling room 1902, immediately visit room 1908. Since engineer B is overloaded with tasks, the on-duty staff is arranged to remotely diagnose the status of the air conditioning equipment, send a memo to the front desk, and include it in the batch processing suggestion of "Multiple Room Abnormalities on This Floor". At the same time, the system will feedback customer information (such as through the voice assistant), "We have arranged personnel to go to confirm the status of your room. Please wait a moment. The service robot is about to deliver the reminder card. If you have more specific abnormal content, you can also continue to tell me;";

[0123] In the above example content, even though the semantics is fuzzy, the system can still perform preliminary processing and scheduling in a structured manner. Through the collaborative heat map, the system can identify potential batch problems in advance, reduce the cost of repeated responses, improve service accuracy and customer experience, and at the same time relieve the pressure on front-line manpower.

[0124] In this embodiment, in step S3 of identifying the source information of the service request, converting the source information into corresponding service mirror information, and pushing the service mirror information to the preset customer service team of the hotel terminal, it further includes:

[0125] S31: Based on the content structure of the source information, construct a clone information object of the service request, where the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone;

[0126] S32: Determine whether the states among all the clone information objects are unified;

[0127] S33: If so, activate the management mechanism of the clone information object, and according to the management mechanism, allocate the processing cycle of the clone information object, and dynamically generate a view aggregator of the clone information object, where the management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking, and the processing cycle specifically includes being generated, dispatched, being processed, pending confirmation, and closed.

[0128] In this embodiment, the system constructs a clone information object of the service request based on the content structure of the source information, which specifically includes customer identity information, the original request text, the trigger time, and the request source. The clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone. Then, the system determines whether the states of the respective clone information objects are unified to execute corresponding steps. For example, when the system determines that the states of the respective clone information objects are not unified, the system will consider that there are state disagreements or data out-of-sync in the multi-step transfer of the current service request, that is, the same customer request presents inconsistent service states or interpretation results in different "clones" such as customer communication, execution layer feedback, status tracking, back-end management, and multilingual translation adaptation. The system will compare the task IDs, timestamps, and execution statuses in the customer communication clone, execution clone, tracking clone, etc., and calculate their consistency scores. If the score is lower than the threshold, it will automatically enter the "state reconstruction" process, and at the same time, re-deduce the original service intention from the "original request text + customer identity + trigger time" in the source information. And if the triggered exception severity is high (such as security-related, complaint-related), an artificial customer service will be preferentially assigned. If it is a regular service item, the system will automatically pull the standard response path and re-issue the execution task. For example, when the system determines that the states of the respective clone information objects are unified, at this time, the system will consider that the information between the various links of the current service request from reception to execution, tracking, feedback, management, and then to multilingual adaptation is synchronized and consistent, and there is no state drift, interpretation conflict, or execution deviation. The system will activate the management mechanism of the clone information object. The management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking. According to this management mechanism, the processing cycle of the clone information object will be allocated. The processing cycle specifically includes in generation, dispatched, in processing, to be confirmed, and closed.By activating the management mechanism of the clone information object and introducing read-only fields, differential updates, permission control, and version tracking, the system can effectively prevent service requests from being tampered with or accidentally modified during multiple processing. Even if the service request goes through multiple sub-departments and multiple operation nodes, it can still maintain a highly consistent state view, and the operation history can be traced back according to the version record, improving the transparency and credibility of the system. At the same time, the service request is divided into standardized processing cycle stages such as "generating → dispatched → processing → pending confirmation → closed", enabling the system to have clear identification and process control capabilities for the service status. This not only facilitates the automatic scheduling of human or equipment resources but also unifies the understanding of the service status by different departments, effectively avoiding misunderstandings, duplicate processing, or status misalignment. Moreover, each clone object is subject to a unified management mechanism, ensuring that its behavior has boundary constraints (such as read-only restrictions), dynamic responses (such as differential updates), access security (such as permission control), and historical traceability (such as version tracking) in the system. This helps to control the access and modification of data at different permission levels, accurately identify abnormal updates or tampering behaviors, and support the synchronization and coordination of service requests across languages, roles, and platforms.

[0129] In this embodiment, after step S3 of generating the autonomous unit of the service request to the hotel terminal in real time according to the service branch pre-selected by the customer service team for the service mirror information, the following steps are further included:

[0130] S301: Generate a multi-channel collaborative distribution instruction for the customer service team based on the task collaboration type of the autonomous unit, where the task collaboration type specifically includes engineering maintenance, cleaning service, and room service;

[0131] S302: Determine whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team;

[0132] S303: If not, add the multi-channel collaborative distribution instruction to a preset delayed processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delayed processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes single-channel task, single-channel task with subsequent supplementary dispatch, and multi-channel collaborative task.

[0133] In this embodiment, the system generates multi-channel collaborative distribution instructions for the customer service team based on the task collaboration types of autonomous units. The task collaboration types specifically include engineering maintenance, cleaning services, and room service. Then, the system determines whether these multi-channel collaborative distribution instructions can be assigned to the idle personnel of the customer service team to execute the corresponding steps. For example, when the system determines that the multi-channel collaborative distribution instructions of the customer service team can be assigned to the idle personnel of the customer service team, the system will consider that there is a schedulable and available human resource pool at present, and the indicators such as the skill matching degree, time window, and workload between the task and the personnel meet the scheduling conditions. The system can consider that the task can be received and executed immediately and efficiently. The system will push each multi-channel collaborative distribution instruction to the terminal of the matching idle personnel, mark the task as the "assigned" state, and generate a task tracking node in the system for subsequent status collection, feedback, and timeliness monitoring. At the same time, if the service request corresponds to multiple tasks (for example, a "water leakage event" may involve maintenance + cleaning), the system will issue multiple distribution instructions in parallel to different team members, bind them to the same main task flow in the background, and perform overall status normalization management, and can synchronously display to the customer that "the task has been assigned and the on-site service is expected within a few minutes", improving the customer experience and transparency during the waiting process. For example, when the system determines that the multi-channel collaborative distribution instructions of the customer service team cannot be assigned to the idle personnel of the customer service team, at this time, the system will consider that there is no available human resource pool at present, and it is difficult to match between the task and the personnel. The system will list the multi-channel collaborative distribution instructions in a pre-set delayed processing queue, and dynamically adjust the service path of the multi-channel collaborative distribution instructions according to the enqueue timestamp of the delayed processing queue. The service path specifically includes single-channel tasks, single-channel tasks with subsequent supplementary dispatch, and multi-channel collaborative tasks. According to these service paths, the task time window of the service request is adaptively adjusted. By adding the currently unassignable tasks to the delayed processing queue, the system avoids the blind repeated attempts and scheduling conflicts of tasks when resources are insufficient. Combining the enqueue timestamp and the queue rules, tasks can be processed orderly according to the urgency and waiting duration, ensuring that the task execution is more planned and buffered, improving the stability and fairness of the overall resource scheduling. At the same time, the original multi-channel collaborative tasks are dynamically adjusted to single-channel tasks, supplementary dispatch tasks, or split execution paths according to the executable conditions, enabling the system to keep the service uninterrupted when the personnel are insufficient. This adaptive strategy for task paths enhances the flexibility of the task model and effectively improves the system's response ability in complex scenarios such as peak hours, sudden offline of personnel, and intensive service requests. And according to the task type and service path adjustment, the system will adaptively update the task time window of the service request (such as postponing the expected on-site time, resending the status notice, etc.), avoiding the experience loss caused by the customer having no feedback for a long time, providing a more reasonable buffer time for subsequent execution, and cooperating with the customer front-end status synchronization, which can maintain the customer's right to know and expectation value management of the task status and improve the overall satisfaction.

[0134] It should be noted that the multi-channel collaborative distribution instruction is included in a preset delay processing queue. According to the enqueue timestamp of the delay processing queue, the service path of the multi-channel collaborative distribution instruction is dynamically adjusted, and according to the service path, the task time window of the service request is adaptively adjusted. The specific example is as follows:

[0135] Suppose in a high-end hotel, the guest in room 501 sends a service request through the in-room smart terminal: "There is serious water accumulation in the bathroom. Suspect the drain pipe is blocked. Please handle it as soon as possible.";

[0136] The original planned service path (multi-channel collaboration), the system intelligently analyzes according to the keywords "water accumulation", "drain pipe", "processing" that this task needs to be completed by three types of execution units in collaboration:

[0137] Engineering maintenance personnel: Responsible for detecting the drainage system and dredging it;

[0138] Cleaning personnel: Responsible for cleaning the water accumulation and drying the floor;

[0139] Guest room service personnel: Supplement bath towels and replace wet supplies;

[0140] The system queries the available resources as follows,

[0141] Engineering maintenance personnel: Currently all occupied, and the expected idle time exceeds 25 minutes;

[0142] Cleaning personnel: Busy, no clear release time;

[0143] Guest room service personnel: One person is idle, near room 502;

[0144] Therefore, the delay processing mechanism is started and queued in the delay processing queue. The system packs the maintenance and cleaning tasks into a "delay processing task unit", marks the enqueue timestamp (e.g., 10:30:14), and records its priority (e.g., medium-high). The task of the guest room service personnel is dispatched and executed as an immediate task;

[0145] Subsequently, the service path is dynamically adjusted to generate the following service path variants,

[0146] Path A (immediately feasible): Immediately assign the guest room attendant near room 502 to enter room 501 first, bring clean bath towels, replace the wet floor mat, and comfort the customer;

[0147] Path B (subsequent supplementary dispatch): The cleaning and maintenance tasks are respectively set as delay tasks, and the system continuously monitors the available status of resources;

[0148] Internal note of the system: "This service request is changed to a phased completion mode";

[0149] Subsequently, the task time window is adaptively updated. By adjusting the task time window, the original setting was: "Complete all services within 15 minutes", and after adjustment, it is as follows,

[0150] Guest room replenishment task: Process immediately (expected within 5 minutes);

[0151] Maintenance task: Expected to start no earlier than 10:55;

[0152] Cleaning task: Expected to follow up within 10 minutes after the maintenance is completed;

[0153] Finally, synchronize and feedback to the customer interface (such as TV screen, App, voice broadcast) as follows,

[0154] "Hello, the service request has been started. Staff have been arranged to deliver clean bath towels. Drainage inspection and cleaning are expected to be completed continuously after the staff are available. Thank you for your understanding;

[0155] Regarding the delay queue monitoring and subsequent scheduling, the task heat will increase over time. After 10 minutes, the system finds that the task is still not completed, and the enqueue timestamp continues to increase. The system will automatically increase its priority by one level. When it detects that a cleaning staff member's task ends, it will immediately schedule to Room 501. After the engineering maintenance staff completes the previous task (around 11:00), they will be automatically assigned to Room 501 to handle the remaining problems;

[0156] Final service completion feedback: After all services are completed, the system summarizes the task execution trajectory and time nodes, records the start and end times of the three-stage service, and synchronizes them to the management background for subsequent optimization of the scheduling strategy, and sends a complete service summary card (including apologies, integral rewards, etc.) to the customer;

[0157] To sum up, in the above example content, the system can enable customers to obtain timely responses and relieve their emotions by reasonably arranging hotel resource allocation, even if resources cannot be allocated in time to serve customers. At the same time, the system makes reasonable use of local resources to avoid completely rejecting service, forming a service data closed-loop at the back end, which helps subsequent prediction and allocation optimization, and realizes the intelligent scheduling ability of "off-peak execution, segmented processing, and task reconstruction" during peak hours.

[0158] In this embodiment, the information recognition engine preset in the hotel terminal is activated. In step S5 of constructing the fuzzy request corresponding to the autonomous unit through the information recognition engine, it further includes:

[0159] S51: Based on the confidence mechanism preset in the information recognition engine, obtain the virtual intention data after the fuzzy request is input into the information recognition engine;

[0160] S52: Determine whether the virtual intention data reaches the confidence threshold preset by the confidence mechanism;

[0161] S53: If not, convert the fuzzy request into a preset refrigeration request, retain the intention clue of the refrigeration request within a preset time period, collect the information keywords repeatedly entered by the hotel customer into the hotel terminal, and dynamically wake up the refrigeration request according to the same word frequency of the information keywords and the intention clue.

[0162] In this embodiment, the system, based on the confidence mechanism preset by the information recognition engine, obtains the virtual intention data after these fuzzy requests are input into the information recognition engine, and then the system determines whether the virtual intention data reaches the confidence threshold preset by the confidence mechanism to execute corresponding steps; for example, when the system determines that the virtual intention data after the fuzzy request is input into the information recognition engine can reach the confidence threshold preset by the confidence mechanism, the system will consider that the current fuzzy request can already accurately parse the real intention of the customer by the system. The system will obtain core fields such as user intention keywords, target location, and expected time limit from the parsing result, mark them as "recognized virtual intention", attach a confidence value, and at the same time, according to the recognized intention, construct a corresponding service autonomous unit. When input into this unit, it is still marked as "generated from fuzzy conversion intention", but can be incorporated into the formal scheduling system, and automatically mount the virtual intention to one or more existing service branches. If there are multiple candidate service branches, the one with the highest matching degree will be automatically selected according to the confidence value and service semantic priority; for example, when the system determines that the virtual intention data after the fuzzy request is input into the information recognition engine cannot reach the confidence threshold preset by the confidence mechanism, at this time, the system will consider that the system cannot parse the real intention of the customer for the current fuzzy request. The system will convert these fuzzy requests into pre-set refrigerated requests, retain the intention clues of the refrigerated requests within the pre-set time period, and by collecting the information keywords repeatedly entered by hotel customers into the hotel terminal, according to the same word frequency of these information keywords and the intention clues, dynamically wake up the refrigerated requests; when the system cannot clearly identify the customer's intention, it will not blindly generate tasks, but refrigerate the request to prevent resource waste, service deviation or customer dissatisfaction caused by incorrect scheduling. Through the "intention refrigeration + re-judgment" mechanism, it is ensured that the service execution is based on high confidence, and it is ensured that each operation is more targeted and accurate. At the same time, the system gradually constructs a language preference model of hotel customer behavior by saving the semantic clues and keyword residual data of unrecognized requests. When the same customer inputs similar expressions in a short period of time, the system can be awakened based on "keyword co-occurrence degree" and "context relevance", enabling the originally unrecognized requests to have the ability to regenerate context, continuously optimizing the system's understanding of natural language, and through the method of "refrigerated request + dynamic wake-up", the system establishes a fault-tolerant waiting mechanism. Even if the first input fails, it will not be immediately discarded, but wait for the customer's subsequent behavior for "reconfirmation". This strategy not only improves the system's tolerance for fuzzy and ambiguous semantics, but also provides a more relaxed operation tolerance for customers, enhancing the user experience.

[0163] In step S2 of determining whether the service requests belong to the same service content in this embodiment, it further includes:

[0164] S21: Obtain the request interval of the service request based on the preset spatial attributes of the hotel terminal, where the spatial attributes specifically include room location and floor location;

[0165] S22: Determine whether the request interval is within a preset time window;

[0166] S23: If so, identify the trigger frequency of the service request, obtain the group similarity behavior corresponding to the spatial attribute according to the trigger frequency, and place preset prompt content in the public area preset by the hotel terminal based on the group similarity behavior, where the prompt content is specifically operation suggestions and service method recommendations.

[0167] In this embodiment, the system obtains the request intervals of these service requests based on the spatial attributes preset by the hotel terminal, and the spatial attributes specifically include the room location and the floor location. Then, the system determines whether these request intervals are within the preset time window to execute corresponding steps. For example, when the system determines that the request interval of a service request is not within the preset time window, the system will consider that the current service request exhibits a non-short-term concentrated feature in the time dimension, and there is no strong temporal correlation or tight triggering logic between requests. The system will consider that these requests have spatial correlation but lack temporal coincidence, and do not constitute the processing conditions of "task merging" or "batch triggering". Therefore, these requests should be independently processed according to their respective event IDs, and autonomous units should be generated respectively to avoid merging unrelated events into the same service flow and prevent misaggregation. At the same time, the service status nodes corresponding to the rooms or floors in the hotel terminal spatial attribute map are updated to mark the existence of "scattered requests" in this area. If these nodes continue to heat up within a short period of time, the system can set a threshold to trigger area inspections or preventive task dispatch (such as: notifying the cleaning staff, equipment self-check robots), and if service requests that do not overlap in time and space and have similar content repeatedly appear in a certain spatial area (such as multiple customers reporting that the TV is not working at different times), the system can consider that there is a risk of repeated complaints or an abnormal trend of equipment, and can enter the "abnormal service request aggregation model" to prompt the management to intervene and verify. For example, when the system determines that the request interval of a service request is within the preset time window, the system will consider that these requests have obvious concentration or continuity in the time dimension, belong to the repeated submission of the same customer, or are reminder requests before the task is completed. The system will identify the triggering frequency of these service requests, obtain the group similarity behaviors corresponding to the spatial attributes according to different triggering frequencies, and based on these group similarity behaviors, put the preset prompt content in the public area preset in the hotel terminal. The prompt content is specifically operation suggestions and service method recommendations;By recognizing the concentration and high frequency of requests, the system automatically determines them as possible duplicate submissions or task urgings, thus avoiding multiple dispatching and duplicate processing of the same service event. By uniformly identifying and merging requests, it optimizes the resource scheduling efficiency, reduces manpower waste and redundant equipment calls, and enhances the intelligence level of the overall service process. At the same time, by analyzing group similar behaviors, personalized and perceptible prompt messages (such as "Please wait 3 minutes for the air conditioner to restart and do not submit repeatedly", "If you need to speed up the processing, please confirm the service status through the in-room tablet") are placed in public areas, which can guide the service rhythm without interfering with user privacy, relieve the anxiety of customers caused by waiting or misoperations, improve service transparency and user experience. And through group behavior analysis (for example, multiple adjacent rooms submit "room temperature anomaly" in the same time period), the system can determine potential equipment or systematic problems, timely place public prompt content, trigger background diagnostic tasks, achieve proactive intervention and prevention before the problem reaches its peak, and enhance the robustness and self-healing ability of the hotel service system.;

[0168] In this embodiment, in step S4 of determining whether the autonomous unit can match the execution unit preset by the hotel terminal, it further includes:

[0169] S41: Based on the feedback processing status of the autonomous unit, construct the processing progress of the service request, where the feedback processing status specifically includes received, in transit, completed, and customer confirmation;

[0170] S42: Determine whether the processing progress is stagnant;

[0171] S43: If so, obtain the regional resource density of the autonomous unit, dynamically adjust the service density of each area of the hotel according to the regional resource density, and adaptively adjust the service cycle of the service request according to the service density.

[0172] In this embodiment, the system constructs the processing progress of the service request based on the feedback processing status of the autonomous unit, where the feedback processing status specifically includes received order, in transit, completed, and customer confirmation. Then, the system determines whether the processing progress has stalled to execute corresponding steps. For example, when the system determines that the processing progress of the service request has not stalled, the system will consider that the current service request is in a normal transfer state from dispatch to execution, and the execution units of each link are all providing feedback on the processing status according to the process, without abnormal delay or interruption. The system will update the processing record of the service request in real time according to the status feedback regularly sent by the execution unit (human or equipment type), ensuring the traceability of the entire service link. At the same time, the current processing progress will be synchronized to the customer device (such as the guest room tablet, WeChat mini-program, etc.) in real time, and the customer will be prompted of the current status in the form of a "service progress bar" or a "status reminder card", thereby enhancing the perceptibility and satisfaction of the customer during waiting. And because the processing progress is in a coherent and smooth state, the system does not need to enable an exception handling mechanism (such as task reminder, heat map congestion analysis, or reallocation mechanism), maintaining the automatic execution mode of the current service chain, effectively reducing management intervention and dispatching resource load. For example, when the system determines that the processing progress of the service request has stalled, the system will consider that the current service request is in an abnormal transfer state. The system will obtain the regional resource density of the autonomous unit and dynamically adjust the service density of each area of the hotel according to different regional resource densities. According to the service density, the service cycle of the service request will be adaptively adjusted. By analyzing the resource density of the area where the autonomous unit is located (such as the number of execution units in the same area, the availability of human resources, the equipment response frequency, etc.), the system can quickly identify the "service bottleneck area" or the "human resource tension area", avoiding blind global dispatching and reducing the waste of dispatching resources, achieving precise intervention for abnormally transferred requests. At the same time, according to the service density (that is, the ratio of the aggregation degree of service requests per unit time to the task processing capacity), the system can set a more reasonable service time window or appropriately delay the response cycle for newly incoming service requests in this area, giving the processing link in the high-density area a breathing opportunity, ensuring the priority closed-loop of existing tasks, and avoiding the impact of long-term hanging tasks on the overall service quality. Moreover, the adaptive adjustment of the service cycle can not only improve the sustainability of service execution in congested areas but also reduce the customer complaint rate caused by task accumulation, enhancing the response elasticity of the system to sudden concentrated service requests. The system maintains the "flexible and controllable" state of the overall hotel service network by finely controlling the processing rhythm of tasks.

[0173] It should be noted that obtaining the regional resource density of the autonomous unit, dynamically adjusting the service density of each area of the hotel according to the regional resource density, and adaptively adjusting the service cycle of the service request according to the service density are specifically exemplified as follows:

[0174] Suppose a large chain hotel welcomes a conference group for check-in on a weekend night, and the rooms on the 16th and 17th floors are centrally allocated to the group members; among them, a large number of service requests break out in the east guest room area on the 16th floor within a short period of time, and the system records are as follows:

[0175] Room number Service request Submission time 1602 Replace towel 18:03 1605 Air conditioner not cooling (repair request) 18:04 1607 Send bottled water 18:05 1609 Wall socket without power (repair request) 18:07 1603 Send toothbrush 18:08 1608 Carpet stained (cleaning request) 18:09 1604 Air conditioner still not fixed (repeated reminder) 18:10

[0176] The system automatically analyzes the task situation on the east side of the 16th floor at 18:11, and the process is as follows:

[0177] Step 1, obtain the regional resource density,

[0178] Total number of current service requests: 7

[0179] Idle human resources: 1 floor attendant (number E01)

[0180] Idle equipment resources: 1 service robot (number R02)

[0181] Regional resource density calculation: Total available resources = 2 (E01 + R02), total number of requests = 7, → resource density ≈ 0.29, below the "resource shortage" threshold;

[0182] Step 2, dynamically adjust the service density. The system determines that there is a problem of high service load on the east side of the 16th floor and immediately executes it,

[0183] Service task reorganization: Combine the tasks of "delivering toothbrushes to room 1603" and "delivering water to room 1607" into a single unified delivery task, execute the task of "changing towels in room 1602" by the robot (hanging bag delivery), prioritize arranging the engineering staff to remotely diagnose the problem of "the air conditioner in room 1605 is not cooling", check the status of attendant E02 on the 17th floor. If idle, allocate the task of "no power in the wall socket in room 1609" to E02 for processing, and at the same time exclude duplicate / unnecessary services. For example, "the air conditioner in room 1604 is still not working well" is a duplicate reminder task for "room 1605". The system determines that the status has not been updated and does not assign another task, but only pushes back the current status of "already sent someone to handle it";

[0184] Step 3, adaptively adjust the service cycle. The system sets a new cycle response window for each task:

[0185] Service task Original preset cycle Period after dynamic adjustment Reason explanation Replace towel in Room 1602 Within 10 minutes Within 15 minutes The robot queues the delivery task Air conditioner not cooling in Room 1605 Within 10 minutes Maintain within 10 minutes Fault type tasks are given priority Send water to Room 1607 Within 10 minutes Merge with Room 1603 for 20 minutes Merge task delivery to save round-trip time Send toothbrush to Room 1603 Within 10 minutes Merge with Room 1607 for 20 minutes Same as above Wall socket without power in Room 1609 (dispatch E02) Within 10 minutes Within 15 minutes Support personnel need to be dispatched across floors Air conditioner still not fixed in Room 1604 (reminder) Within 10 minutes Suspend dispatch Same status as Room 1605, no need to repeat dispatch Carpet stain in Room 1608 (cleaning) Within 10 minutes Postpone to within 30 minutes Cleaning tasks can be scheduled during off-peak hours

[0186] Customer prompt synchronization (front desk + customer device). The system synchronizes "Your service request has been received and is being processed. Please wait patiently". For customers whose processing cycle has been adjusted (such as room 1603), the system additionally states: "The current hotel service is busy, and it is expected to complete the toothbrush and bottled water delivery tasks within 20 minutes";

[0187] In summary, in the above example, the system realizes the automatic mitigation of regional load through resource density → service density → cycle adjustment, ensures the priority completion of emergency tasks, intelligently delays non-emergency tasks, improves the utilization efficiency of service resources, and reduces duplicate services and customer complaints.

[0188] In this embodiment, before step S1 of classifying the service requests uploaded by hotel customers to the hotel terminal based on the preset information access channels of the hotel terminal, the following steps are further included:

[0189] S101: Identify the identity tags preset by the hotel customer;

[0190] S102: Determine whether the identity tags belong to the preset check-in identities;

[0191] S103: If not, based on the identity tags, limit the service level of the hotel customer on the hotel terminal, and classify the available service content of the hotel customer according to the service level.

[0192] In this embodiment, the system identifies the identity tags preset by the hotel customer in advance, and then determines whether these identity tags belong to the preset check-in tags to execute corresponding steps; for example, when the system determines that the identity tags preset by the hotel customer belong to the preset check-in tags, the system will consider that the customer has completed the check-in registration, the system confirms that it is a valid check-in customer, and the system will retrieve the customer's check-in information, preference settings, historical service records, etc., to ensure service personalization and continuity. At the same time, according to the identity tags, the system grants the customer corresponding room access rights, facility usage rights, and authorizations for relevant value-added services, and preferentially allocates room service, maintenance response, and customized requirements for check-in customers to enhance the customer experience; for example, when the system determines that the identity tags preset by the hotel customer do not belong to the preset check-in tags, at this time the system will consider that the customer has not completed the check-in registration and cannot be confirmed as a valid check-in customer. The system will limit the service level of the hotel customer on the hotel terminal based on the corresponding identity tags, and classify the available service content of the hotel customer according to different service levels; by judging whether the identity tags are valid check-in tags, the system can prevent non-registered customers from accidentally triggering or misusing service resources, ensure that only check-in customers are provided with complete services, reduce service errors or resource waste caused by unknown identities, and at the same time limit the service level based on the identity tags, enabling the system to intelligently allocate different customer service resources, response priorities, and service content according to the customer status (such as reservation customers, visitors, temporary experience users), thereby improving the overall resource scheduling efficiency. And when it is found that the user's identity tags are not marked as "checked in", the system can timely guide them to complete the check-in registration or registration process, only display basic services (such as consultation, reservation information push), realize service pre-filtering, and improve the user conversion rate and platform experience.

[0193] Reference appendix Figure 2 , which is a customer service information flow management system for hotel front - end and back - end collaboration in an embodiment of the present invention, including:

[0194] A classification module 10, configured to classify service requests uploaded by hotel customers to the hotel terminal based on a preset information access channel of the hotel terminal;

[0195] A judgment module 20, configured to judge whether the service requests belong to the same service content;

[0196] An execution module 30, configured to, if so, identify the source information of the service request, convert the source information into corresponding service mirror information, push the service mirror information to a preset customer service team of the hotel terminal, and generate an autonomous unit of the service request to the hotel terminal in real time according to a pre - selected service branch of the customer service team for the service mirror information, where the autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status;

[0197] A second judgment module 40, configured to judge whether the autonomous unit can match an execution unit preset by the hotel terminal, where the execution unit specifically includes a human - type and a device - type;

[0198] A second execution module 50, configured to, if not, activate an information recognition engine preset by the hotel terminal, through the information recognition engine, construct a fuzzy request corresponding to the autonomous unit, divide the service flow of the execution unit according to the fuzzy request, generate a task congestion heat map of the service request based on the collaborative density and time lag of the service flow, and dynamically schedule the execution resources of the hotel terminal according to the task congestion heat map, where the service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path plan, and the task congestion heat map specifically includes a status map and a collaborative heat map.

[0199] In this embodiment, the classification module 10 classifies the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset in the hotel terminal, and then the judgment module 20 determines whether these service requests belong to the same service content to execute corresponding steps; for example, when the system determines that the service requests uploaded by hotel customers to the hotel terminal do not belong to the same service content, the system will consider that there are obvious differences in intention, execution method, resource requirements or processing logic among the multiple service requests currently received, and different customers or different rooms have submitted requests with similar content but actually different goals. The system will construct independent service mirror information structures for each request with different service contents, maintain logical isolation between requests to avoid confusion, bind the source, timestamp, and context data to each mirror, and at the same time distribute different service requests to professional customer service groups to avoid resource conflicts or duplicate responses caused by "one order being sent to multiple recipients". And if multiple different requests come from the same customer (or room) and there is no conflict, they can be guided to be merged into a "parallel service flow" to improve efficiency, otherwise they will be scheduled in order of priority; for example, when the system determines that the service requests uploaded by hotel customers to the hotel terminal belong to the same service content, at this time the execution module 30 will consider that there may be duplicate service events among the multiple service requests currently received. The system will identify the source information of these service requests, convert this source information into corresponding service mirror information, and push these service mirror information to the customer service team preset in the hotel terminal. According to the service branches pre-selected by different customer service teams for the service mirror information, autonomous units of service requests will be generated in real time to the hotel terminal. The autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status; by identifying the source information of multiple requests, the system merges multiple requests belonging to the same service content to generate an autonomous unit, avoiding multiple duplicate work orders from entering the scheduling queue. This deduplication mechanism can prevent the repeated invocation of human resources and equipment resources. Especially during peak periods or when customers perform continuous operations (such as repeatedly pressing the service button), it can significantly reduce the system congestion rate and execution interference, improve the coordination and execution efficiency of the overall service. At the same time, converting the original requests into standardized service mirror information can unify the expression methods from different sources (such as App, voice, front desk), avoiding misunderstandings of service intentions by customer service or the system. The service mirror contains a unified field structure, such as request type, location attribute, timestamp, etc., laying an information foundation for generating autonomous units subsequently, ensuring that the information received at the execution level is clear, complete, and operable. And different customer service teams can preset the processing branches corresponding to the service mirror (such as delivering items, maintenance, cleaning, etc.). The system automatically matches the optimal service path according to the mirror content, realizing intelligent classification and precise distribution of service requests. The generated autonomous unit not only has a clear event ID and status, but can also be quickly routed to the corresponding position or device execution node, thereby realizing a high degree of automation and refined management of the service process;Subsequently, the second judgment module 40 determines whether these autonomous units can match the execution units preset by the hotel terminal. The execution units specifically include labor types and equipment types to execute corresponding steps. For example, when the system determines that the autonomous units of the service request can match the execution units preset by the hotel terminal, the system will consider that the current task can be recognized by the system and the corresponding execution entity can be found. The system will dispatch the task to the mobile terminals of the corresponding position personnel (such as the cleaner's PDA, engineering maintenance tablet), including detailed task content, location, and response time limit, trigger device instructions (such as intelligent delivery robot scheduling, elevator permission opening, air conditioning regulation system automatic response, etc.), execute specific operations, and at the same time update the task status from "pending" to "in execution" or "issued", bind the identifier of the execution unit (such as personnel ID, equipment number), ensure that subsequent tracking is clear and controllable, and continuously monitor the execution feedback of the execution unit, regularly collect the execution progress, completion status, abnormal alarms, etc., and feedback them to the hotel central control system or customer service terminal. When necessary, it can support automatic reminder or abnormal transfer to manual processing. For example, when the system determines that the autonomous units of the service request cannot match the execution units preset by the hotel terminal, at this time, the second execution module 50 will consider that although the current task can be recognized, the corresponding execution entity cannot be found at present. The system will activate the information recognition engine preset by the hotel terminal. Through this information recognition engine, a fuzzy request corresponding to the autonomous unit is constructed. Based on these fuzzy requests, the service flow of the execution unit is divided. The service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path planning. Based on the collaborative density and time lag of these service flows, a task congestion heat map of the service request is generated. The task congestion heat map specifically includes a status map and a collaborative heat map. The system identifies its potential intention through the information recognition engine, constructs a fuzzy request, avoids the interruption problem of "no execution path after request recognition", and through intention understanding and task decomposition, can further intelligently generate an execution plan to make up for the shortcoming of the traditional system's processing ability for non-standard tasks. At the same time, by constructing a "service flow" (including a request intention structure, a task decomposition chain, and an execution path planning), the system can refine fuzzy or composite requests into executable standard subtasks, plan and schedule paths according to priority and reachability. This method enables complex tasks to be decomposed into multi-point concurrent processing units, improves execution efficiency and reduces single-point bottlenecks, and combines the collaborative density and time lag of the service flow to generate a task congestion heat map (including a status map and a collaborative heat map), intuitively showing the task distribution and resource pressure, which can assist the system in performing flow limiting, detour scheduling, or task rearrangement in high-load areas, avoiding resource overload in some areas while other areas are idle, and finally realizing the optimization of resource allocation and the balance of service response.

[0200] In this embodiment, the execution module further includes:

[0201] A construction sub-module, configured to construct a clone information object of the service request based on the content structure of the source information, where the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multi-language channel adaptation clone;

[0202] A judgment sub-module, configured to judge whether the states among the respective clone information objects are unified;

[0203] An execution sub-module, configured to, if so, activate the management mechanism of the clone information object, and allocate a processing cycle for the clone information object according to the management mechanism, and dynamically generate a view aggregator of the clone information object, where the management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking, and the processing cycle specifically includes being generated, dispatched, being processed, pending confirmation, and closed.

[0204] In this embodiment, the system constructs a clone information object of the service request based on the content structure of the source information, which specifically includes customer identity information, the original request text, the trigger time, and the request source. The clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone. Then, the system determines whether the states among the respective clone information objects are unified to execute corresponding steps. For example, when the system determines that the states among the respective clone information objects are not unified, the system will consider that there are state disagreements or data out-of-sync during the multi-step transfer of the current service request, that is, the same customer request presents inconsistent service states or interpretation results in different "clones" such as customer communication, execution layer feedback, status tracking, back-end management, and multilingual translation adaptation. The system will compare the task IDs, timestamps, and execution statuses in the customer communication clone, execution clone, tracking clone, etc., and calculate their consistency scores. If the score is lower than the threshold, it will automatically enter the "state reconstruction" process, and at the same time, re-deduce the original service intention from the "original request text + customer identity + trigger time" in the source information. And if the triggered anomaly severity is high (such as security-related, complaint-related), an artificial customer service will be preferentially assigned. If it is a regular service item, the system will automatically pull the standard response path and reissue the execution task. For example, when the system determines that the states among the respective clone information objects are unified, at this time, the system will consider that the information among the various links of the current service request from reception to execution, tracking, feedback, management, and then to multilingual adaptation is synchronized and consistent, and there is no state drift, interpretation conflict, or execution deviation. The system will activate the management mechanism of the clone information object, and the management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking. According to this management mechanism, the processing cycle of the clone information object will be allocated, and the processing cycle specifically includes in generation, dispatched, in processing, pending confirmation, and closed.By activating the management mechanism of the clone information object and introducing read-only fields, differential updates, permission control, and version tracking, the system can effectively prevent service requests from being tampered with or modified by mistake during multiple processing operations. Even if the service request goes through multiple sub-departments and multiple operation nodes, it can still maintain a highly consistent state view, and the operation history can be traced back according to the version record, improving the transparency and credibility of the system. At the same time, the service request is divided into standardized processing cycle stages such as "generating → dispatched → processing → pending confirmation → closed", enabling the system to have clear identification and process control capabilities for the service status. This not only facilitates the automatic scheduling of human or equipment resources but also unifies the understanding of the service status by different departments, effectively avoiding misunderstandings, duplicate processing, or status misalignment. Moreover, each clone object is subject to a unified management mechanism, ensuring that its behavior has boundary constraints (such as read-only restrictions), dynamic responses (such as differential updates), access security (such as permission control), and historical traceability (such as version tracking) in the system. This helps to control the access and modification of data at different permission levels, accurately identify abnormal updates or tampering behaviors, and support the synchronization and coordination of service requests across languages, roles, and platforms.

[0205] In this embodiment, it further includes:

[0206] A generation module, configured to generate a multi-channel collaborative distribution instruction for the customer service team based on the task collaboration type of the autonomous unit, where the task collaboration type specifically includes engineering maintenance, cleaning service, and housekeeping service;

[0207] A third judgment module, configured to judge whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team;

[0208] A third execution module, configured to, if not, list the multi-channel collaborative distribution instruction in a preset delayed processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delayed processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes single-channel task, single-channel task with subsequent supplementary dispatch, and multi-channel collaborative task.

[0209] In this embodiment, the system generates multi-channel collaborative distribution instructions for the customer service team based on the task collaboration types of autonomous units. The task collaboration types specifically include engineering maintenance, cleaning services, and room service. Then, the system determines whether these multi-channel collaborative distribution instructions can be assigned to the idle personnel of the customer service team to execute the corresponding steps. For example, when the system determines that the multi-channel collaborative distribution instructions of the customer service team can be assigned to the idle personnel of the customer service team, the system will consider that there is a schedulable and available human resource pool at present, and the indicators such as the skill matching degree, time window, and workload between the task and the personnel meet the scheduling conditions. The system can consider that the task can be received and executed immediately and efficiently. The system will push each multi-channel collaborative distribution instruction to the terminal of the matching idle personnel, mark the task as the "assigned" state, and generate a task tracking node in the system for subsequent status collection, feedback, and timeliness monitoring. At the same time, if the service request corresponds to multiple tasks (for example, a "water leakage event" may involve maintenance + cleaning), the system will issue multiple distribution instructions in parallel to different team members, bind them to the same main task flow in the background, and perform overall status normalization management, and can synchronously display to the customer that "the task has been assigned, and the on-site service is expected within a few minutes" to improve the customer experience and transparency during the waiting process. For example, when the system determines that the multi-channel collaborative distribution instructions of the customer service team cannot be assigned to the idle personnel of the customer service team, at this time, the system will consider that there is no available human resource pool at present, and it is difficult to match between the task and the personnel. The system will list the multi-channel collaborative distribution instructions in a pre-set delayed processing queue, and dynamically adjust the service path of the multi-channel collaborative distribution instructions according to the enqueue timestamp of the delayed processing queue. The service path specifically includes single-channel tasks, single-channel tasks with subsequent supplementary dispatching, and multi-channel collaborative tasks. According to these service paths, the task time window of the service request is adaptively adjusted. By adding the currently unassignable tasks to the delayed processing queue, the system avoids the blind repeated attempts and scheduling conflicts of tasks when resources are insufficient. Combining the enqueue timestamp and the queue rules, tasks can be processed orderly according to the urgency and waiting duration, ensuring that the task execution is more planned and buffered, improving the stability and fairness of the overall resource scheduling. At the same time, the original multi-channel collaborative tasks are dynamically adjusted to single-channel tasks, supplementary dispatching tasks, or split execution paths according to the executable conditions, enabling the system to keep the service uninterrupted when there are insufficient personnel. This adaptive strategy for task paths enhances the flexibility of the task model and effectively improves the system's response ability in complex scenarios such as peak hours, sudden offline of personnel, and intensive service requests. And according to the task type and service path adjustment, the system will adaptively update the task time window of the service request (such as postponing the expected on-site time, resending the status notification, etc.), avoiding the experience loss caused by the customer having no feedback for a long time, providing a more reasonable buffer time for subsequent execution, and cooperating with the customer front-end status synchronization, which can maintain the customer's right to know about the task status and the management of expectations, and improve the overall satisfaction.

[0210] In this embodiment, the second execution module further includes:

[0211] An acquisition sub-module, configured to obtain virtual intent data after inputting the fuzzy request into the information recognition engine based on a confidence mechanism preset by the information recognition engine;

[0212] A second judgment sub-module, configured to judge whether the virtual intent data reaches a confidence threshold preset by the confidence mechanism;

[0213] A second execution sub-module, configured to, if not, convert the fuzzy request into a preset refrigeration request, retain the intent clue of the refrigeration request within a preset time period, collect information keywords repeatedly input by the hotel customer into the hotel terminal, and dynamically wake up the refrigeration request according to the same word frequency between the information keywords and the intent clue.

[0214] In this embodiment, the system, based on the confidence mechanism preset by the information recognition engine, obtains the virtual intent data after these fuzzy requests are input into the information recognition engine, and then the system determines whether the virtual intent data reaches the confidence threshold preset by the confidence mechanism to execute corresponding steps. For example, when the system determines that the virtual intent data after the fuzzy request is input into the information recognition engine can reach the confidence threshold preset by the confidence mechanism, the system will consider that the current fuzzy request can already accurately parse the real intent of the customer by the system. The system will obtain core fields such as user intent keywords, target location, and expected time limit from the parsing result, mark them as "recognized virtual intent", attach a confidence value, and at the same time, according to the recognized intent, construct a corresponding service autonomous unit. When input into this unit, it is still marked as "generated from fuzzy conversion intent", but can be incorporated into the formal scheduling system, and automatically mount the virtual intent to one or more existing service branches. If there are multiple candidate service branches, the one with the highest matching degree will be automatically selected according to the confidence value and service semantic priority. For example, when the system determines that the virtual intent data after the fuzzy request is input into the information recognition engine cannot reach the confidence threshold preset by the confidence mechanism, at this time, the system will consider that the system cannot parse the real intent of the customer for the current fuzzy request. The system will convert these fuzzy requests into preset refrigerated requests, retain the intent clues of the refrigerated requests within a preset time period, and by collecting the information keywords repeatedly entered by hotel customers into the hotel terminal, according to the same word frequency of these information keywords and the intent clues, dynamically wake up the refrigerated request. When the system cannot clearly identify the customer's intent, it will not blindly generate tasks, but refrigerate the request to prevent resource waste, service deviation, or customer dissatisfaction caused by incorrect scheduling. Through the "intent refrigeration + re - judgment" mechanism, it ensures that service execution is based on a high confidence level, and ensures that each operation is more targeted and accurate. At the same time, the system gradually constructs a language preference model of hotel customer behavior by saving the semantic clues and keyword residual data of unrecognized requests. When the same customer inputs similar expressions in a short period, the system can be awakened based on "keyword co - occurrence degree" and "context relevance", enabling the originally unrecognized request to have the ability of context regeneration, continuously optimizing the system's understanding of natural language, and through the method of "refrigerated request + dynamic wake - up", the system establishes a fault - tolerant waiting mechanism. Even if the first input fails, it will not be immediately discarded, but wait for the customer's subsequent behavior for "re - confirmation". This strategy not only improves the system's tolerance for fuzzy and ambiguous semantics, but also provides a more relaxed operation tolerance for customers, enhancing the user experience.

[0215] In this embodiment, the judgment module further includes:

[0216] A second acquisition sub - module, configured to obtain the request interval of the service request based on the space attributes preset in the hotel terminal, where the space attributes specifically include room location and floor location;

[0217] A third judgment sub-module, configured to judge whether the request interval is within a preset time window;

[0218] A third execution sub-module, configured to, if so, identify the triggering frequency of the service request, obtain the group similarity behavior corresponding to the spatial attribute according to the triggering frequency, and place preset prompt content in a public area preset in the hotel terminal according to the group similarity behavior, where the prompt content is specifically operation suggestions and service method recommendations.

[0219] In this embodiment, the system obtains the request intervals of these service requests based on the space attributes preset in advance by the hotel terminal. The space attributes specifically include the room location and the floor location. Then, the system determines whether these request intervals are within the time window preset in advance to execute corresponding steps. For example, when the system determines that the request interval of the service request is not within the preset time window, the system will consider that the current service request exhibits a non-short-term concentrated feature in the time dimension, and there is no strong temporal correlation or tight trigger logic between requests. The system will consider that these requests have spatial correlation but lack temporal overlap and do not constitute the processing conditions for "task merging" or "batch triggering". Therefore, these requests should be independently processed according to their respective event IDs, and autonomous units should be generated separately to avoid merging unrelated events into the same service flow and prevent misaggregation. At the same time, the service status nodes corresponding to the rooms or floors in the hotel terminal space attribute map are updated to mark the existence of "scattered requests" in this area. If these nodes continue to heat up within a short period, the system can set a threshold to trigger area inspections or preventive task dispatching (such as notifying the cleaning staff and equipment self-check robots). And if service requests that do not overlap in time and space and have similar content repeatedly appear in a certain space area (such as multiple customers reporting that the TV is not working at different times), the system can consider that there is a risk of repeated complaints or an abnormal trend of equipment, and can enter the "abnormal service request aggregation model" to prompt the management to intervene and verify. For example, when the system determines that the request interval of the service request is within the preset time window, the system will consider that these requests have obvious concentration or continuity in the time dimension, belong to repeated submissions by the same customer, or are reminder requests before the task is completed. The system will identify the trigger frequency of these service requests, obtain the group similarity behaviors corresponding to the space attributes according to different trigger frequencies, and based on these group similarity behaviors, display the preset prompt content in the public areas preset in the hotel terminal. The prompt content is specifically operation suggestions and service method recommendations;By recognizing the concentration and high frequency of requests, the system automatically determines possible duplicate submissions or task urgings, thus avoiding multiple dispatching and duplicate processing of the same service event. By uniformly identifying and merging requests, it optimizes the resource scheduling efficiency, reduces manpower waste and redundant equipment calls, and improves the intelligence level of the overall service process. At the same time, by analyzing group similar behaviors, personalized and perceptible prompt messages (such as "Please wait 3 minutes for the air conditioner to restart. Do not submit repeatedly." and "If you need to speed up the processing, please confirm the service status through the in-room tablet.") are placed in public areas, which can guide the service rhythm without interfering with user privacy, relieve the anxiety of customers caused by waiting or misoperation, improve service transparency and user experience. And through group behavior analysis (for example, multiple adjacent rooms submit "room temperature anomaly" in the same time period), the system can determine potential equipment or systematic problems, timely place public prompt content, trigger background diagnostic tasks, achieve proactive intervention and prevention before the problem reaches its peak, and enhance the robustness and self-healing ability of the hotel service system.

[0220] In this embodiment, the second judgment module further includes:

[0221] A second construction sub-module, configured to construct the processing progress of the service request based on the feedback processing status of the autonomous unit, where the feedback processing status specifically includes received order, in transit, completed, and customer confirmation;

[0222] A fourth judgment sub-module, configured to judge whether the processing progress is stagnant;

[0223] A fourth execution sub-module, configured to, if so, obtain the regional resource density of the autonomous unit, dynamically adjust the service density of each area of the hotel according to the regional resource density, and adaptively adjust the service cycle of the service request according to the service density.

[0224] In this embodiment, the system constructs the processing progress of the service request based on the backhaul processing status of the autonomous unit. The backhaul processing status specifically includes received order, in transit, completed, and customer confirmation. Then, the system determines whether the processing progress has stalled to execute corresponding steps. For example, when the system determines that the processing progress of the service request has not stalled, the system will consider that the current service request is in a normal flow state from dispatch to execution. The execution units of each link are all providing feedback on the processing status according to the process, without abnormal delay or interruption. The system will update the processing record of the service request in real time based on the status reported by the execution unit (human or equipment type) at regular intervals, ensuring the traceability of the entire service link. At the same time, the system will synchronize the current processing progress to the customer device (such as the guest room tablet, WeChat mini-program, etc.) in real time, and prompt the customer of the current status in the form of a "service progress bar" or a "status reminder card", thereby enhancing the perceptibility and satisfaction of the customer during waiting. And because the processing progress is in a coherent and smooth state, the system does not need to enable an exception handling mechanism (such as task reminder, heat map congestion analysis, or reallocation mechanism), and maintains the automatic execution mode of the current service chain, effectively reducing management intervention and dispatching resource load. For example, when the system determines that the processing progress of the service request has stalled, the system will consider that the current service request is in an abnormal flow state. The system will obtain the regional resource density of the autonomous unit and dynamically adjust the service density of each area of the hotel according to different regional resource densities. Based on this service density, the system will adaptively adjust the service cycle of the service request. By analyzing the resource density of the area where the autonomous unit is located (such as the number of execution units, the availability of human resources, the equipment response frequency, etc. in the same area), the system can quickly identify the "service bottleneck area" or the "area with tight human resources", avoid global dispatching blindly, reduce the waste of dispatching resources, and achieve precise intervention for abnormally flowing requests. At the same time, based on the service density (that is, the ratio of the aggregation degree of service requests per unit time to the task processing ability), the system can set a more reasonable service time window or appropriately delay the response cycle for newly incoming service requests in this area, so that the processing link in the high-density area can get a breathing opportunity, ensure the priority closed-loop of existing tasks, and avoid long-term hanging tasks from affecting the overall service quality. And the adaptive adjustment of the service cycle can not only improve the sustainability of service execution in congested areas, but also reduce the customer complaint rate caused by task backlog, and enhance the response elasticity of the system to sudden concentrated service requests. The system maintains the overall hotel service network in a "flexible and controllable" state by finely controlling the processing rhythm of tasks.

[0225] In this embodiment, it further includes:

[0226] An identification module for identifying the identity tags preset by the hotel customer;

[0227] A fourth judgment module for judging whether the identity tag belongs to the preset check-in identity;

[0228] A fourth execution module, configured to, if not, based on the identity tag, limit the service level of the hotel customer on the hotel terminal, and classify the available service content of the hotel customer according to the service level.

[0229] In this embodiment, the system identifies the identity tags preset by the hotel customer, and then determines whether these identity tags belong to the preset check-in tags to execute corresponding steps. For example, when the system determines that the identity tags preset by the hotel customer belong to the preset check-in tags, the system will consider that the customer has completed the check-in registration, confirm that the customer is a valid check-in customer, and the system will retrieve the customer's check-in information, preference settings, historical service records, etc., to ensure service personalization and continuity. At the same time, according to the identity tags, the system grants the customer corresponding room access rights, facility usage rights, and authorizations for related value-added services, and preferentially allocates room service, maintenance response, and customized requirements for check-in customers to improve the customer experience. For example, when the system determines that the identity tags preset by the hotel customer do not belong to the preset check-in tags, the system will consider that the customer has not completed the check-in registration and cannot be confirmed as a valid check-in customer. The system will limit the service level of the hotel customer on the hotel terminal based on the corresponding identity tags, and classify the available service content of the hotel customer according to different service levels. By judging whether the identity tag is a valid check-in tag, the system can prevent non-registered customers from accidentally triggering or misusing service resources, ensure that only check-in customers are provided with complete services, reduce service errors or resource waste caused by unknown identities, and at the same time limit the service level based on the identity tags, enabling the system to intelligently allocate different customer service resources, response priorities, and service content according to the customer status (such as reservation customers, visitors, temporary experience users), thereby improving the overall resource scheduling efficiency. And when it is found that the user identity tag is not marked as "checked in", the system can appropriately guide the user to complete the check-in registration or registration process, only display basic services (such as consultation, reservation information push), implement service pre-filtering, and improve the user conversion rate and platform experience.

[0230] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for managing the flow of customer service information through the coordination of the front and back ends of a hotel, characterized in that, It includes the following steps: Classify the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset on the hotel terminal; Judge whether the service requests belong to the same service content; If so, identify the source information of the service request, convert the source information into corresponding service mirror information, push the service mirror information to the customer service team preset on the hotel terminal, and generate an autonomous unit of the service request to the hotel terminal in real time according to the service branch preselected by the customer service team for the service mirror information. The autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status; Judge whether the autonomous unit can match the execution unit preset on the hotel terminal, where the execution unit specifically includes a human type and a device type; If not, activate the information recognition engine preset on the hotel terminal. Through the information recognition engine, construct a fuzzy request corresponding to the autonomous unit, divide the service flow of the execution unit according to the fuzzy request, generate a task congestion heat map of the service request based on the collaborative density and time lag of the service flow, and dynamically schedule the execution resources of the hotel terminal according to the task congestion heat map. The service flow specifically includes a request intention structure, a service task decomposition chain, and an execution path plan, and the task congestion heat map specifically includes a status map and a collaborative heat map.

2. The method for managing the flow of customer service information through the collaboration between the front and back ends of a hotel according to claim 1, wherein In the step of identifying the source information of the service request, converting the source information into corresponding service mirror information, and pushing the service mirror information to the customer service team preset on the hotel terminal, it further includes: Construct a clone information object of the service request based on the content structure of the source information, where the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone; Judge whether the statuses among the clone information objects are unified; If so, activate the management mechanism of the clone information object, and according to the management mechanism, allocate the processing cycle of the clone information object, and dynamically generate a view aggregator of the clone information object. The management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking, and the processing cycle specifically includes in generation, dispatched, in processing, to be confirmed, and closed.

3. The customer service information flow management method for hotel front-end and back-end collaboration according to claim 1, wherein After the step of generating an autonomous unit of the service request to the hotel terminal in real time according to the service branch preselected by the customer service team for the service mirror information, it further includes: Generate a multi-channel collaborative distribution instruction for the customer service team based on the task collaboration type of the autonomous unit, where the task collaboration type specifically includes engineering maintenance, cleaning service, and room service; Judge whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team; If not, add the multi-channel collaborative distribution instruction to a preset delay processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delay processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes single-channel tasks, single-channel tasks with subsequent supplementary dispatch, and multi-channel collaborative tasks.

4. The customer service information flow management method for hotel front-end and back-end collaboration according to claim 1, wherein, In the step of activating the preset information recognition engine of the hotel terminal and constructing the fuzzy request corresponding to the autonomous unit through the information recognition engine, it further includes: Based on the confidence mechanism preset in the information recognition engine, obtain the virtual intent data after the fuzzy request is input into the information recognition engine; Determine whether the virtual intent data reaches the confidence threshold preset by the confidence mechanism; If not, convert the fuzzy request into a preset refrigeration request, retain the intent clue of the refrigeration request within a preset time period, collect the information keywords repeatedly entered by the hotel customer into the hotel terminal, and dynamically wake up the refrigeration request according to the same word frequency between the information keywords and the intent clue.

5. The customer service information flow management method for hotel front-end and back-end collaboration according to claim 1, characterized in that, In the step of determining whether the service requests belong to the same service content, it further includes: Based on the space attribute preset in the hotel terminal, obtain the request interval of the service request, where the space attribute specifically includes room location and floor location; Determine whether the request interval is within a preset time window; If so, identify the trigger frequency of the service request, obtain the group similarity behavior corresponding to the space attribute according to the trigger frequency, and place preset prompt content in the public area preset in the hotel terminal according to the group similarity behavior, where the prompt content is specifically operation suggestions and service method recommendations.

6. The customer service information flow management method for hotel front-end and back-end collaboration according to claim 1, wherein In the step of determining whether the autonomous unit can match the execution unit preset in the hotel terminal, it further includes: Based on the feedback processing status of the autonomous unit, construct the processing progress of the service request, where the feedback processing status specifically includes received order, on the way, completed, and customer confirmation; Determine whether the processing progress is stagnant; If so, obtain the regional resource density of the autonomous unit, dynamically adjust the service density of each area of the hotel according to the regional resource density, and adaptively adjust the service cycle of the service request according to the service density.

7. The customer service information flow management method for hotel front-end and back-end collaboration according to claim 1, wherein Before the step of classifying the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset in the hotel terminal, it further includes: Identify the identity label preset by the hotel customer; Determine whether the identity label belongs to a preset check-in identity; If not, limit the service level of the hotel customer on the hotel terminal based on the identity label, and classify the available service content of the hotel customer according to the service level.

8. A customer service information flow management system for hotel front-end and back-end collaboration, characterized in that, It includes: A classification module for classifying the service requests uploaded by hotel customers to the hotel terminal based on the information access channels preset in the hotel terminal; A judgment module for judging whether the service requests belong to the same service content; An execution module, which is used to, if so, identify the source information of the service request, convert the source information into corresponding service mirror information, push the service mirror information to the customer service team preset in the hotel terminal, and generate an autonomous unit of the service request to the hotel terminal in real time according to the service branch pre-selected by the customer service team for the service mirror information, where the autonomous unit specifically includes an event ID, a location attribute, a request type, and a processing status; A second judgment module, which is used to judge whether the autonomous unit can match the execution unit preset in the hotel terminal, where the execution unit specifically includes a human type and a device type; A second execution module, which is used to, if not, activate the information recognition engine preset in the hotel terminal, construct a fuzzy request corresponding to the autonomous unit through the information recognition engine, divide the service flow of the execution unit according to the fuzzy request, generate a task congestion heat map of the service request based on the collaborative density and time lag of the service flow, and dynamically schedule the execution resources of the hotel terminal according to the task congestion heat map, where the service flow specifically includes a request intention structure, a service task disassembly chain, and an execution path planning, and the task congestion heat map specifically includes a status map and a collaborative heat map.

9. The customer service information flow management system for hotel front-end and back-end collaboration according to claim 8, wherein The execution module further includes: A construction sub-module, which is used to construct a clone information object of the service request based on the content structure of the source information, where the content structure specifically includes customer identity information, request original text, trigger time, and request source, and the clone information object specifically includes a customer communication clone, an execution clone, a tracking clone, a management clone, and a multilingual channel adaptation clone; A judgment sub-module, which is used to judge whether the states among the clone information objects are unified; An execution sub-module, which is used to, if so, activate the management mechanism of the clone information object, allocate the processing cycle of the clone information object according to the management mechanism, and dynamically generate a view aggregator of the clone information object, where the management mechanism specifically includes read-only fields, differential updates, permission control, and version tracking, and the processing cycle specifically includes in generation, dispatched, in processing, to be confirmed, and closed.

10. The customer service information flow management system for hotel front-end and back-end collaboration according to claim 8, wherein It further includes: A generation module, which is used to generate a multi-channel collaborative distribution instruction for the customer service team based on the task collaboration type of the autonomous unit, where the task collaboration type specifically includes engineering maintenance, cleaning service, and room service; A third judgment module, which is used to judge whether the multi-channel collaborative distribution instruction can be assigned to the idle personnel of the customer service team; A third execution module, which is used to, if not, list the multi-channel collaborative distribution instruction in a preset delay processing queue, dynamically adjust the service path of the multi-channel collaborative distribution instruction according to the enqueue timestamp of the delay processing queue, and adaptively adjust the task time window of the service request according to the service path, where the service path specifically includes a single-channel task, a single-channel task with subsequent supplementary dispatch, and a multi-channel collaborative task.

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