Digital management template generation and execution system for hotel service process

Abstract

The invention belongs to the technical field of hotel service process template generation, and particularly relates to a hotel service process digital management template generation and execution system, which comprises a template construction and distribution module, a process execution module and a digital management module, wherein the template construction and distribution module is based on a built-in hotel industry knowledge base, and a structure template is generated by visually dragging a semantic template component. After executing and collecting data strictly according to template steps at an execution end, packaging the data into a structured execution record carrying a semantic tag; the rule analysis module is used for carrying out compliance judgment and anomaly detection according to a preset rule base, calling a hotel domain entity relationship network to carry out root cause reasoning on anomaly and generating a structured analysis result set; and the text generation module is used for filling the analysis result set into a preset text template according to a mapping rule, standardizing a data structure from the source through a semantic template, realizing interpretable intelligent analysis by utilizing a rule base and a knowledge graph, and ensuring the certainty of the report and improving the accuracy of the finally generated report by adopting a template filling mechanism.

Description

Technical Field

[0001] This invention belongs to the field of hotel service process template generation technology, specifically relating to a digital management template generation and execution system for hotel service processes. Background Technology

[0002] With the rise of large language model technology, its powerful text generation capabilities have led to its widespread application in many fields, including report writing. While general-purpose large language models can address fundamental issues such as typos, omissions, and missing subjects to some extent, practical applications still suffer from the following technical limitations: First, general-purpose large-scale language models are essentially probabilistic models trained on massive amounts of internet corpora. Their core optimization goal is to generate the statistically most coherent and probable text sequences. This probability-based "generation" model fundamentally contradicts the requirements of hotel management reports, which demand accurate descriptions based on definite facts, rigorous logical deduction, and standardized formats. In pursuit of text fluency, models often supplement or infer non-existent data relationships, creating factual "illusions" (such as fabricating non-existent business trends) or logical fallacies (such as making unfounded causal attributions). Therefore, their output contains unpredictable randomness, failing to meet the hotel industry's stringent requirements for accuracy, reliability, and compliance in reporting. This results in reports still requiring significant manual effort for fact-checking and comprehensive revision, making automation inefficient and of poor quality.

[0003] In addition, in terms of model understanding, the training data of general models is extensive but discrete, and there is a severe lack of systematic encoding of deep structured knowledge in the hotel vertical industry. Hotel operations involve complex standard operating procedures, specific key performance indicator systems, refined service failure logic trees, and strict industry regulations. General models have difficulty understanding the corresponding professional terms and their relationships, which often leads to problems such as business logic errors, misuse of terminology, and contextual deviations in report generation. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, this invention provides a digital management template generation and execution system for hotel service processes, resolving the issues of unstructured data collection and random, unreliable report content generated from generic large models in existing hotel management report generation systems.

[0005] The objective of this invention can be achieved through the following technical solution: a digital management template generation and execution system for hotel service processes, the system comprising: The template building and distribution module is used to generate and store structural templates based on the built-in hotel industry knowledge base, and distribute the structural templates to terminals of various positions; The structure template includes a template identifier, an executable step sequence, and data acquisition fields associated with each step. The data acquisition fields include predefined field types and semantic tags associated with the field types. The process execution module is used to generate a structured execution record by collecting process execution data corresponding to each data collection field when the structure template is executed. The structured execution record includes template identifier, step identifier, data collection field identifier, input data value, timestamp, and job terminal identifier. The rule analysis module is used to obtain data values ​​from the structured execution record, and perform compliance judgment, anomaly detection, and root cause correlation analysis on semantic tags according to a preset rule base to generate an analysis result set; The text generation module is used to fill the content of the analysis result set into the corresponding variable placeholders of the preset text template according to the preset mapping rules, and generate the final report text.

[0006] Preferably, the template construction and distribution module is configured to: respond to the user's selection operation on the management terminal, filter one or more template components for different service scenarios in the hotel industry knowledge base, combine and configure parameters to generate a structural template.

[0007] Preferably, the process execution module runs on each job terminal and is used to sequentially present interactive interface elements corresponding to each data collection field according to the step sequence when the structure template is executed, and to collect process execution data corresponding to each step sequence through the interactive interface elements.

[0008] Preferably, the form of the interactive interface elements matches the field type, including a time picker, a checkbox, a drop-down list, an image upload control, and a text input box.

[0009] Preferably, the process execution module includes a network detection unit; The network detection unit is used to automatically detect incomplete task instances when the application is closed or the device is restarted during task execution, restore the step state before the interruption, and retain the entered but unsubmitted data at the same time. The process of collecting, temporarily storing, and encapsulating data is performed locally, and the generated structured execution records are saved in the terminal's pending-send queue. When an available network is detected, upload the backlog of data records.

[0010] Preferably, in the rule analysis module, the rule base includes a computational rule base and an entity relationship network; The computational rule base includes a basic rule layer and a semantic mapping layer. The basic rule layer is used to store computational rules, compliance judgment rules, and logical judgment rules associated with semantic tags. The semantic mapping layer is used to automatically associate rules with semantic tags and corresponding data segments. The entity relationship network is a hotel domain entity relationship network represented by a directed graph structure. The hotel domain entity relationship network includes hotel domain entities, entities, and the relationships between entities. The relationships include causal relationships and probabilistic relationships between service nodes.

[0011] Preferably, the semantic mapping layer includes a label-rule mapping table, a scene rule package, and a threshold parameter library.

[0012] Preferably, the rule analysis module generates the analysis result set through the following process: The computational rule base is invoked to perform compliance assessments and anomaly detection on data aggregated based on semantic tags; For the detected anomalies, the entity relationship network is invoked to perform association queries and inferences, generating an analysis result set corresponding to the anomalies. The analysis result set includes data facts, anomaly identifiers, and association relationships.

[0013] Preferably, in the text generation module, the report type is a logical category of reports divided according to management needs, and the text template is a formatted document template corresponding to a specific report type, with a pre-set fixed narrative framework and variable placeholders.

[0014] The beneficial effects of this invention are as follows: This invention achieves a standardized structure and clear business intent (semantic tags) for service process data from its inception through the construction of visual semantic templates and precise data collection at the data source. In data analysis, a set rule base and domain knowledge graph are used. The rule base performs efficient calculations and anomaly detection based on semantic tags, while the knowledge graph provides interpretable reasoning based on causality and probability, avoiding the randomness and black-box problems of general AI models. This enables deterministic and traceable intelligent analysis. In the report generation step, a structured result-filled pre-set template reporting mechanism ensures the absolute accuracy and standardized format of the output content. This ensures that the system can automatically and continuously diagnose problems, trace root causes, and present standardized results based on real business data and embedded domain knowledge. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram illustrating the steps of generating an analysis result set using the rule analysis module of the system of this invention. Detailed Implementation

[0017] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0018] Please see Figures 1-2 This embodiment provides a digital management template generation and execution system for hotel service processes. The system includes a template construction and distribution module, a process execution module, a rule analysis module, and a text generation module that are connected in sequence. The template building and distribution module responds to user selections on the management terminal. Based on the built-in hotel industry knowledge base, it selects one or more template components for different service scenarios, combines them, configures parameters, generates and stores structural templates, and distributes the structural templates to terminals at various workstations. The structure template refers to an executable task list generated based on a hotel industry knowledge base and presented on the job terminal. Its data structure includes predefined template identifiers, step sequences, and associated data collection fields. Each field has a semantic tag that identifies the business dimension, used to standardize service execution and structured data collection. It is transformed into digital executable instructions according to the actual hotel business needs, specifically including the following execution process: S1: Module startup and initialization, including user login and permission verification before entering the template design workbench. This includes: hotel management personnel logging into the management terminal via a web browser or client; the management terminal verifying their role permissions (e.g., department manager, operations director) to ensure they have the operational permissions for template creation and distribution; users selecting the "Create Template" or "Edit Template" function in the management interface; and the system loading the template creation visual workbench. This workbench interface is divided into three main areas: the industry knowledge base component panel on the left, the template canvas in the center, and the attribute configuration panel on the right. S2, component selection and combination based on industry knowledge base, includes the following processes: S21: Select the service scenario and load the industry knowledge base component: Users can select the target scenario in the "Service Scenarios" category at the top of the component panel, such as "Front Office Reception", "Room Service", "Food and Beverage Service", and "Engineering and Maintenance". Based on the selected scenario, the system loads the corresponding template component library dynamically from the built-in hotel industry knowledge base. The template components are predefined and reusable standardized templates, including process step components and data field components. Process step components include "identity verification", "payment processing", "room inspection" and "equipment testing", etc. Data field components include "timestamp field", "photo upload field", "single choice list field" and "numeric input field". The components are displayed in the form of graphical icons or lists, with brief descriptions and applicable business rule prompts. S22: Interactive template assembly, including the following sub-steps: S221: Step Sequence Construction: Users drag and drop the desired "Process Step Components" from the Components panel to the central template canvas. The canvas visually displays the step sequence in flowchart or list format. Users can adjust the step order by dragging and dropping, or set branching conditions (such as "If it is a VIP guest, perform additional step A"). S222: Field Association and Configuration. a) When a user clicks on a step on the canvas (e.g., "Guest Room Cleaning - Bedroom Tidying"), the details of that step will be displayed in the attribute configuration panel on the right; b) From the "Add Field" option in the panel, the user selects the required data collection field type (e.g., "Checkbox", "Time Picker", "Image") and adds it to that step; c) For each added field, the user needs to perform detailed configuration in the attribute panel. The configuration includes defining field labels, selecting field types, configuring business rules, and binding semantic labels. Specifically: Define field labels: Enter the name to be displayed on the front end, such as "Completion Status"; Select field type: Determine the interaction method, including room number selector and facilities checklist; Configure business rules: Set field constraints, such as "required", "numerical range", and "format validation" (e.g., mobile phone number format); Semantic tags are bound to the data. Based on the semantic tags selected or entered by the user from the drop-down list that correspond to the business meaning of the field, and according to the selected step and field type, commonly used tags are recommended from the knowledge base, such as [Task Completion], [Efficiency Indicator - Working Hours], [Quality Evidence - Photo], [Customer Preference], and [Security Compliance]. This tag is key metadata for subsequent data analysis. S3: Configure and generate template parameters: S31: Users can manipulate the top-level canvas to configure the template's global attributes, including the template name and identifier, applicable job roles and terminals, and corresponding execution strategies. Template name and identifier: Enter an easily recognizable name to generate a unique template identifier, such as "Standard Guest Room Cleaning SOP_v2.1"; Applicable positions and terminals, which specifies which departments and job roles this template will be distributed to; The execution strategy includes setting whether offline execution is allowed, the completion time limit, and reminder rules; S32: Simulate the terminal interface of the job position, show the execution flow and interaction effect of the template. At the same time, the system background performs a static verification. The static verification includes checking the completeness of the step logic, confirming that all required fields have been configured with semantic tags, and verifying that there are no conflicts in the business rules. S32: After user confirmation, the system generates and stores the structure template, including: Serialization converts the graphical template on the canvas into a structured machine-readable format (such as JSON or XML). This data structure strictly includes: template identifier, a list of step sequences, a list of data collection fields for each step, and the field type, configuration parameters, and semantic tags for each field. The serialized template data package, along with metadata such as version information, creator, and creation time, is stored in the data storage area of ​​the template library on the server. S4: Distribute the structure template to each job terminal, including pushing the template data packet and distribution instructions to the target job terminal via the enterprise network (Wi-Fi / cellular network). After receiving the data packet, the process execution module on the job terminal parses the data packet, stores the template locally, updates the task list, and records the distribution status (success or failure) of the distribution instructions. If it fails, it is resent. By constructing a visual template design system based on a hotel industry knowledge base, and using pre-built standardized and semantic template components, managers can quickly generate structural templates through drag-and-drop combinations. By standardizing the format and structure of data collection from the source, and injecting clear business context into the raw data through semantic tags, the system changes the unstructured and fragmented state of traditional system data, generating high-quality data sources ready for analysis. By embedding historical data into reusable components, the template generation process itself becomes an application of domain knowledge, realizing the pre-coding of business logic, reducing the knowledge gap between general digital tools and hotel vertical domain knowledge, enabling business personnel to quickly build and iterate processes independently, and replacing cumbersome offline training with one-click distribution, greatly shortening the service standard deployment cycle.

[0019] The process execution module runs on each job terminal and is used to generate a structured execution record by collecting process execution data corresponding to each data collection field when the structure template is executed. The structured execution record includes template identifier, step identifier, data collection field identifier, input data value, timestamp, and job terminal identifier. Specifically, this includes: presenting interactive interface elements corresponding to each data collection field sequentially according to the step sequence, and collecting process execution data corresponding to each step sequence through these interactive interface elements. The form of the interactive interface elements matches the field type, including time pickers, checkboxes, drop-down lists, image upload controls, and text input boxes. The specific execution steps are as follows: Step 1: The execution end verifies the identity of the executor and synchronizes the list of pending tasks corresponding to the executor's position and shift. Each task is associated with a specific structural template identifier; The system retrieves the execution task selected by the executor, loads the corresponding structured template and template data based on the template identifier corresponding to the execution task, and includes the template identifier, the sequence of executable steps, and the data collection fields associated with each step. The data collection fields include predefined field types and semantic tags associated with the field types. Based on the structured template, the system creates an instance for this task execution, initializes the instance, and records the initial state. Step Two: Define the sequence of all steps for the current task through the defined interactive interface, highlight the current step, read all data collection field definitions for the current step, dynamically generate the user interface, obtain information input by the executor through controls on the user interface based on actual work information, and validate the input information format; when the current step is detected as complete, package the input data values ​​of all fields in this step, the corresponding field identifiers, and the step completion timestamp into a data block. The data block is encrypted and temporarily stored in the terminal device's local database (such as SQLite) to prevent data loss due to unexpected application closure or network interruption. Subsequently, the interface automatically jumps to the next step; Step 3: After detecting that the executor has completed all the execution steps specified in the step sequence, retrieve the data blocks of all steps temporarily stored in this task from the local database, and integrate all the cached data blocks of all steps with the template identifier of this task, the employee's job terminal identifier (device unique ID or employee ID), and the global timestamps of task start and submission, and assemble them into a complete JSON format structured execution record. The structured data record contains the specific values ​​entered by the executor and the field identifiers corresponding to the values ​​and the semantic tags inherited from the template. Step 4: If the application is closed or the device is restarted during task execution, the network detection unit of the process execution module will automatically detect incomplete task instances and restore the step state before the interruption after the executor logs in again. At the same time, it will retain the data that has been entered but not submitted. In an environment where the network is completely missing (such as an underground floor), by executing all the above collection, temporary storage and encapsulation processes locally, the generated structured execution records will be saved in the terminal's pending send queue. When an available network is detected, all backlogged records will be automatically and silently uploaded in the background to ensure the final consistency of the data.

[0020] The process execution module achieves automated collection of high-fidelity structured data at the source of business execution through step-by-step mandatory guidance and semantic data encapsulation mechanisms. It dynamically generates strictly matched interactive interfaces (such as time selectors and photo controls) using defined structured templates. The resulting structured execution record integrates the raw data input by the executor with semantic tags inherited from the template and complete contextual information (template, steps, field identifiers, timestamps), ensuring that each piece of data becomes a data block carrying a clear business meaning upon generation. This addresses the technical shortcomings of traditional systems, such as scattered, unstructured data collection that is disconnected from business intent, ensuring 100% standardized execution of service processes and reducing training and supervision costs. It also provides a clear data source for subsequent module calculations.

[0021] The rule analysis module is used to obtain data values ​​from the structured execution record, and perform compliance judgment, anomaly detection, and root cause correlation analysis on semantic tags according to a pre-set rule base to generate an analysis result set. The analysis result set includes data facts, anomaly identifiers, and correlation relationships. The rule base includes a computational rule base and an entity relationship network. The calculation rule base includes a basic rule layer and a semantic mapping layer. The basic rule layer stores calculation rules, compliance judgment rules, and logical judgment rules associated with semantic tags. The semantic mapping layer automatically associates rules with corresponding data segments through semantic tags. Calculation rules are numerical calculation rules for calculating indicator data from raw collected data, including aggregation rules, comparison rules, and statistical rules. Compliance judgment rules are the standards that each operation needs to meet, including threshold rules, range rules, and existence rules. Logical judgment rules are composite judgments based on multiple conditions, including AND / OR rules and sequence rules. For example, a logical AND rule like IF(room type == "suite") AND (cleaning time > 45 minutes) THEN is marked as "abnormal - inefficient". The semantic mapping layer includes a tag-rule mapping table, scenario rule packages, and a threshold parameter library. The tag-rule mapping table defines the rule set that should be triggered for each semantic tag. For example, all data fields carrying the [efficiency indicator] tag are automatically associated with the "duration-type" calculation and compliance rules in the basic rule layer. Scenario rule packages are pre-configured rule sets for different report types or analysis scenarios. These packages include daily report analysis packages, service quality packages, and compliance audit packages. The daily report analysis package includes time-sensitive, execution-oriented rules such as "daily task completion rate" and "emergency repair response timeout." Service quality includes rules focused on customer experience, such as "correlation rules between customer evaluation scores and cleaning quality inspection scores." The compliance audit package includes inspection rules for all mandatory standards and safety regulations. The threshold parameter library stores the dynamic thresholds and parameters required for rule judgment, supporting differentiated configuration by season, hotel brand, and room type.

[0022] The entity relationship network is a directed graph structure representing the entity relationships within the hotel domain. Hotel domain entities are the graph nodes, and the relationships between entities are the edges. Hotel domain entities represent core concepts in hotel operations, including physical entities, logical entities, and event / state entities. Physical entities include "Room 1208" and "Employee ID 007," logical entities include "Room Cleaning SOP" and "VIP Reception Process," and event / state entities include "Room Cleaning SOP." Relationships include causal and probabilistic relationships between service nodes. Causal relationships include ("New employee not completed training") — [leads to] —> ("Cleaning SOP execution omission"), with direction indicating the causal flow. Probabilistic relationships include ("Peak check-in period") — [may increase] —> ("Front-desk error rate"), with a weight set to 0.65. ("Older model vacuum cleaner") — [related to] —> ("Reduced cleaning efficiency"), with a weight set to 0.8.

[0023] The process by which the rule analysis module generates the analysis result set includes: First, receive structured execution records, and then read the semantic tags bound to each data field in each structured execution record, such as [efficiency - working hours], [quality - room inspection results], and [safety - fire inspection], etc. Extract all data values ​​with the same semantic label from scattered records and aggregate them into multiple "semantic datasets". For example, all values ​​under the [efficiency-working hours] label are aggregated into a "working hours dataset". Based on the type of report to be generated (such as "Daily Efficiency Analysis"), the rule set related to the report type and the semantic labels of the current dataset is automatically filtered from the calculation rule base. For example, "Daily Efficiency Analysis" will trigger all calculation and judgment rules related to the [efficiency-*] label. Conduct compliance assessment: First, the calculation rule library is called to perform compliance judgment and anomaly detection on the data aggregated based on semantic tags. The corresponding indicators are calculated first: for the "work hour dataset", the associated calculation rules are executed, such as calculating the average work hours, the standard deviation of work hours, the longest / shortest work hours, and the calculation results are stored as data facts. Next, threshold comparison and anomaly marking are performed. The calculated average working hours are compared with the preset standard working hours in the rule base (e.g., 30 minutes for a luxury room). If the average working hours are greater than 35 minutes, an "exceeding the standard" judgment is triggered. The specific record that exceeds the standard is located, an anomaly identifier (e.g., anomaly code: EFF_OVERTIME_001) is generated, and the severity of the anomaly (judged according to the proportion exceeding the standard value) and the time of occurrence are recorded. Output preliminary facts and anomaly list: After this stage, a subset of preliminary analysis results is generated. The subset includes data facts and an anomaly identification list. The data facts are, for example: {"Indicator": "Average cleaning hours", "Value": "36.2 minutes", "Sample size": 45}. The anomaly identification list is, for example: [{"Anomaly code": "EFF_OVERTIME_001", "Associated record ID": ["REC1208_1", "REC1210_1"], "Description": "Cleaning hours exceeded"}]. For detected anomalies, the entity relationship network is invoked for association queries and inferences to generate an analysis result set corresponding to the anomalies. The analysis result set includes data facts, anomaly identifiers, and association relationships. First, abnormal features are extracted. Taking the abnormal "excessive cleaning hours" as an example, the system extracts the following features from the relevant records: employee ID 007, room number 1208, time period of occurrence 14:00-15:00, and equipment used "vacuum cleaner A". Graph Query and Path Discovery: Starting with anomaly features, traverse the entity relationship network: For example, starting with employee ID007, the possible path is: Employee ID007 — [New employee] —> Training status [Intensive training not completed]; The system integrates all discovered paths, and the paths of new employees and equipment failures both point to "reduced efficiency". In the knowledge graph, the probability weights of the edges are used to calculate the overall confidence of each potential root cause. The root cause analysis results are integrated with the root cause analysis subset to obtain an analysis result set that includes data facts, anomaly indicators and correlations.

[0024] The rule analysis module achieves an automated pipeline from data to insights by coordinating the work between the computational rule base and the entity relationship network. It also automatically binds business rules using semantic tags to achieve accurate indicator calculation and anomaly detection. Furthermore, it uses a structured domain knowledge graph (entity relationship network) to perform interpretable, probability-based root cause reasoning on anomalies, avoiding the "black box" and randomness of general AI models. The generated analysis result set includes anomalies, the causes of anomalies, and their credibility, providing feasible data support for subsequent decision-making.

[0025] The text generation module is used to fill the content of the analysis result set into the corresponding variable placeholders of the preset text template according to the preset mapping rules, and generate the final report text. The text generation module includes a text template library, a mapping rule library and a text synthesis processor. The text synthesizer includes template loading, data mapping, logical judgment, content filling and final text assembly.

[0026] The text template library stores multiple predefined text templates for different report types. Each text template is a structured document framework file. The report type refers to the logical category of the report based on management needs, such as daily or weekly reports based on timeliness, or service quality reports or compliance reports based on content focus, which determines the scope, dimensions, and output objectives of the data analysis. The text template refers to a formatted document template corresponding to a specific report type, with a pre-set fixed narrative framework and variable placeholders, used to organize and present the analysis results according to a predetermined structure. The final report text is the readable document output by the system, synthesized from the text template and the structured analysis results. It presents data-driven insights and conclusions in a coherent narrative language, directly supporting management decisions.

[0027] The text template specifically includes the following components: Static text snippets: Fixed report text, such as titles, chapter names, introductory sentences, concluding statements, compliance statement templates, etc., for example, as of {report_date}, the completion status of key service quality indicators this month is as follows; Dynamic variable placeholders: Blank spaces marked with specific syntax (such as {indicator name}, <anomaly.root cause>) are used to insert specific analysis results. Placeholders have type attributes (such as "numerical", "text description", "list"). Logical control tags: Simple conditional tags used to determine whether to include a certain text based on data values. For example: {#if work hours exceeding the standard rate > 5%}...efficiency training needs to be emphasized...{ / if}; Each template is bound to a unique template ID and a specific report type from the report type configuration library; The mapping rule base stores a one-to-one correspondence between each data item or conclusion item in the analysis result set and the dynamic variable placeholders in the text template. Each rule in the mapping rule base includes a source data path, a target placeholder, a formatting rule, and a default value / fault tolerance logic. Specifically: the source data path is used to point to the identifier of a specific data item in the structured analysis result set, such as result.efficiency.avg_time; the target placeholder is the name of the variable placeholder in the corresponding text template, such as {average_cleaning_time}; the formatting rule is used to specify how to convert the data source into the text at the placeholder's location, such as formatting the value 28.5 as "28.5 minutes" or "about 29 minutes", and the root cause code root_cause_03 can be mapped to the descriptive text "new employees lack operational proficiency"; the default value / fault tolerance logic is used as backup text when the source data is missing or abnormal.

[0028] The specific execution process of the text generation module: Step 1: The module receives a report generation request, which includes parameters such as report type and time range. Based on the report type, it retrieves the associated text template ID and the corresponding mapping rule set ID from the report type configuration library, and loads the specified text template and mapping rule set into memory. Step 2: Data Mapping and Parsing: The text synthesis processor receives the structured analysis result set from the rule analysis module. It then traverses each rule in the mapping rule library, extracting the corresponding numerical values ​​or objects from the structured analysis result set according to the source data path. Based on formatting rules, the extracted data is converted into strings that conform to human reading habits. For example, the boolean value `true` is converted to "yes", and the list `["noise", "air conditioner malfunction"]` is converted to "mainly involves noise and air conditioner malfunction issues". Step 3: Content Population and Logic Execution: The processor scans the text template, identifies all dynamic variable placeholders and logic control tags. For each placeholder, it finds its corresponding formatted data string and replaces it in place. For logic control tags, it determines the truth value of their conditional expressions (based on the populated data). If true, the text fragment within the tag is retained; if false, the fragment is deleted. This achieves dynamic report trimming, ensuring the content closely addresses the key issues. Step 4: Text assembly and post-processing. All static text fragments, filled dynamic content, and text blocks retained by logical judgment are assembled according to the original order of the template to generate a complete original report text. The post-processing process is then executed, including: checking whether all required placeholders have been filled (completeness check); unifying number and date formats; ensuring sentence fluency (such as handling singular and plural numbers). Finally, a report text with standardized format, fluent language, and accurate facts is output. The report text format is set to HTML, PDF, or Word format. The system's workflow includes: The staff executes the "Guest Room Check-out Cleaning" template through the terminal. The system records the "Completed" status of the task and records semantically tagged data such as "Time taken: 22 minutes", "Replenishment Items List A", and "Room Inspection Anomaly: Damaged Light Fixtures". The system generates a report, calculates the "average cleaning time" as 20 minutes, finds a "light fixture damage" record, and links it to the "engineering maintenance response time" indicator and the "customer claims risk" knowledge node through a knowledge graph. The rules analysis module triggered an insight that "cleaning efficiency is normal, but there is an increased risk of customer claims due to asset damage"; Based on the daily report template and the above insights, the text generation module generates: "Today's average room cleaning time (20 minutes) meets the standard (≤22 minutes)." Based on the incident records, one case of asset damage (lighting fixtures) was discovered and linked to the maintenance department. Historical data shows that if such issues are not repaired before guest check-in, the probability of guest claims may increase by 15%. Recommendation: Strengthen the asset inspection checklist during room checks and track the completion of this repair process. This invention achieves this by: […] throughout the entire process of data source input, data analysis, and report generation. At the data source, through the construction of visual semantic templates and precise data collection and execution, service process data is endowed with a standard structure and clear business intent (semantic tags) from its generation, overcoming the technical shortcomings of traditional unstructured data that is difficult to analyze directly. In data analysis, through a set rule base and domain knowledge graph, the rule base performs efficient calculations and anomaly detection based on semantic tags, while the knowledge graph performs interpretable reasoning based on causality and probability for anomalies, avoiding the randomness and black-box problems of general AI models, and achieving deterministic and traceable intelligent analysis. In the report generation step, a report generation mechanism that uses structured results to fill pre-set templates ensures the absolute accuracy and standardized format of the output content, deeply encoding hotel professional knowledge into each module, forming a fully digitally integrated chain from process design, execution monitoring, intelligent diagnosis to report presentation. This ensures that the system can automatically and continuously diagnose problems, trace root causes, and present standardized results based on real business data and embedded domain knowledge.

[0029] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A digital management template generation and execution system for hotel service processes, characterized in that: The system includes: The template building and distribution module is used to generate and store structural templates based on the built-in hotel industry knowledge base, and distribute the structural templates to terminals of various positions; The structure template includes a template identifier, an executable step sequence, and data acquisition fields associated with each step. The data acquisition fields include predefined field types and semantic tags associated with the field types. The process execution module is used to generate a structured execution record by collecting process execution data corresponding to each data collection field when the structure template is executed. The structured execution record includes template identifier, step identifier, data collection field identifier, input data value, timestamp, and job terminal identifier. The rule analysis module is used to obtain data values ​​from the structured execution record, and perform compliance judgment, anomaly detection, and root cause correlation analysis on semantic tags according to a preset rule base to generate an analysis result set; The text generation module is used to fill the content of the analysis result set into the corresponding variable placeholders of the preset text template according to the preset mapping rules, and generate the final report text.

2. The digital management template generation and execution system for hotel service processes according to claim 1, characterized in that: The template building and distribution module is configured to: respond to the user's selection operation on the management terminal, filter one or more template components for different service scenarios in the hotel industry knowledge base, combine and configure parameters to generate a structural template.

3. The digital management template generation and execution system for hotel service processes according to claim 1, characterized in that: The process execution module runs on each job terminal and is used to sequentially present interactive interface elements corresponding to each data collection field according to the step sequence when the structure template is executed, and to collect process execution data corresponding to each step sequence through the interactive interface elements.

4. The system for generating and executing digital management templates for hotel service processes according to claim 3, characterized in that: The forms of the interactive interface elements match the field types, including time pickers, checkboxes, drop-down lists, image upload controls, and text input boxes.

5. The digital management template generation and execution system for hotel service processes according to claim 3, characterized in that: The process execution module includes a network detection unit; The network detection unit is used to automatically detect incomplete task instances when the application is closed or the device is restarted during task execution, restore the step state before the interruption, and retain the entered but unsubmitted data at the same time. The process of collecting, temporarily storing, and encapsulating data is performed locally, and the generated structured execution records are saved in the terminal's pending-send queue. When an available network is detected, upload the backlog of data records.

6. The system for generating and executing digital management templates for hotel service processes according to claim 1, characterized in that: In the rule analysis module, the rule base includes a computational rule base and an entity relationship network; The computational rule base includes a basic rule layer and a semantic mapping layer. The basic rule layer is used to store computational rules, compliance judgment rules, and logical judgment rules associated with semantic tags. The semantic mapping layer is used to automatically associate rules with semantic tags and corresponding data segments. The entity relationship network is a hotel domain entity relationship network represented by a directed graph structure. The hotel domain entity relationship network includes hotel domain entities, entities, and the relationships between entities. The relationships include causal relationships and probabilistic relationships between service nodes.

7. The digital management template generation and execution system for hotel service processes according to claim 6, characterized in that: The semantic mapping layer includes a label-rule mapping table, a scene rule package, and a threshold parameter library.

8. The digital management template generation and execution system for hotel service processes according to claim 6, characterized in that: The rule analysis module generates the analysis result set through the following process: The computational rule base is invoked to perform compliance assessments and anomaly detection on data aggregated based on semantic tags; For the detected anomalies, the entity relationship network is invoked to perform association queries and inferences, generating an analysis result set corresponding to the anomalies. The analysis result set includes data facts, anomaly identifiers, and association relationships.

9. The system for generating and executing digital management templates for hotel service processes according to claim 1, characterized in that: In the text generation module, the report type is a logical category of reports divided according to management needs, and the text template is a formatted document template corresponding to a specific report type, with a pre-set fixed narrative framework and variable placeholders.

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