A full-link intelligent operation system for tea and catering enterprises

Abstract

The application discloses a kind of tea and catering-oriented enterprise full-link intelligent operation system, including data, strategy and business intelligence agent, intelligent asset unit, industry semantic layer, operating scene atlas and evolution module;Among them, industry semantic layer integrates multiple-source data to build operating semantic atlas;Operating scene atlas builds entity association node network with influence intensity, credibility parameter;Intelligent asset unit deposits multiple operating digital assets;Data intelligence agent generates operating state vector relying on semantic atlas and various assets, matches scene path and scores and determines operating scene;Strategy intelligence agent retrieves associated path, calls asset screening, evaluation and outputs optimal operating strategy;Business intelligence agent analyzes optimal operating strategy, determines execution mode and issues landing;Evolution module is used to realize full-link operation evolution.The system of the application comprehensively improves the global operating adaptation, response and standardized replication operation capability of tea and catering enterprises in response to various complex operating fluctuations.

Description

Technical Field

[0001] This invention relates to the field of intelligent tea and catering, and in particular to an intelligent operation system for the entire chain of tea and catering enterprises. Background Technology

[0002] The tea beverage and catering industry is characterized by high-frequency consumption, strong timeliness, strong regionalization, and highly dynamic operational fluctuations. Its operating system is simultaneously affected by multiple factors, including consumer behavior, weather changes, holidays, traffic in commercial areas, traffic distribution through food delivery platforms, and supply chain fulfillment capabilities. Compared to traditional manufacturing or standard retail industries, the tea beverage and catering industry is more complex, changes faster, and has stronger real-time requirements.

[0003] As stores expand, SKUs increase, and online-offline integration deepens, the complexity of business operations grows exponentially. While traditional ERP, POS, WMS, and CRM systems have achieved basic informatization, their underlying structure is still based on fixed processes, static rules, and rigid business architecture, making them unable to adapt to real-time changing business scenarios.

[0004] The industry currently faces the following core contradictions:

[0005] 1. The contradiction between the complexity of store operations and the adaptability of traditional rigid system architecture: Traditional systems usually adopt a fixed process-driven model, and their core logic relies on predefined rules and static business processes; however, the actual operation of tea drinks and catering involves multiple uncertainties such as fluctuations in customer flow, weather effects, random hit products, and changes in takeout traffic; traditional systems cannot perceive environmental changes in real time and dynamically adjust operating strategies, resulting in the system only being able to record operating results and unable to actively participate in operational control.

[0006] 2. Cognitive gap between headquarters operational decisions and store real-time operations: Headquarters decisions rely on outdated operational reports and financial data, while stores need to deal with immediate issues such as real-time customer flow, stockouts, and fulfillment pressures. Headquarters strategies cannot be implemented in real time, and stores lack intelligent decision-making support, resulting in a cognitive and execution gap between headquarters and stores.

[0007] 3. The contradiction between the dynamic business environment and real-time operational decision-making: The tea and catering industry is affected by multiple real-time variables such as weather, business district popularity, and platform ranking. Traditional system statistical analysis is lagging behind and cannot meet the needs of second-level or minute-level response, making it difficult to achieve refined operational actions.

[0008] 4. The contradiction between the store manager's personal experience and the organizational accumulation of management capabilities: The core management experience of excellent store managers is limited to personal cognition and cannot be transformed into digital assets for the enterprise. This makes it difficult to replicate when the store expands and leads to unstable operation. Traditional systems lack the ability to transform experience and pass it on at scale.

[0009] 5. The contradiction between multi-system silo architecture and unified business semantics: Enterprises use multiple systems in parallel, but the data standards, codes and semantics of each system are not consistent. Information such as inventory and membership is not synchronized, and unified business semantics has not been achieved. Only basic informatization has been completed, and the goal of real-time intelligence has not been achieved.

[0010] In summary, the tea beverage and catering industry urgently needs a new type of intelligent operation system covering the entire enterprise chain. This system should possess core capabilities such as real-time perception of business status, dynamic generation of business strategies, automatic execution of operational actions, continuous accumulation of business knowledge, and synergistic evolution between headquarters and stores, in order to resolve the core contradictions of the industry and promote its high-quality development. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings and deficiencies of the existing technology and provide a smart operation system for the entire chain of tea and catering enterprises.

[0012] The objective of this invention is achieved through the following technical solution:

[0013] A smart operation system for the entire supply chain of tea and catering enterprises includes a data intelligence agent, a strategy intelligence agent, a business intelligence agent, an intelligent asset unit, an industry semantic layer, an operational scenario graph, and an evolution module; wherein:

[0014] The industry semantic layer is used to uniformly extract, normalize, and construct semantic relationships for the business semantic objects in the tea and catering industry, forming a business semantic graph.

[0015] The business scenario graph identifies business entities through a business semantic graph and constructs scenario graph nodes. Based on the scenario graph nodes, it constructs the association relationships between the scenario graph nodes and configures structured parameters for the association relationships, including the relationship influence strength and relationship credibility.

[0016] The intelligent asset unit is used to store the data, experience, knowledge, and strategic results generated in the long-term operation of the tea and catering industry, and to convert them into digital assets. Specifically, it includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operating indicator assets, holiday operation strategy assets, and membership operation knowledge assets.

[0017] The data intelligence agent, based on the business semantic graph, calls upon business indicator assets, best-selling product identification model assets, and business district knowledge graph assets to identify the business status vectors of various business objects in the tea and catering industries. The business status vectors are input into the business scenario graph and matched with candidate influence paths in the graph. A scenario identification score is calculated based on node matching degree, path influence strength, and path credibility. When the scenario identification score reaches a threshold, a unified business status result is output. The path influence strength and path credibility are calculated using relation influence strength and relation credibility, respectively.

[0018] The strategy intelligence agent identifies the current business scenario based on the unified business status results, retrieves relevant influence paths of the current business scenario through the business scenario graph, and, based on the relevant influence paths, calls upon merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to screen candidate strategies; and performs a comprehensive evaluation of the strategy effectiveness of the candidate strategies to generate the optimal business strategy.

[0019] The business intelligence agent analyzes the optimal business strategy and determines the strategy type. Based on the strategy type, it calls the corresponding store operation SOP assets, merchandise operation strategy assets, membership operation knowledge assets, or business indicator assets and best-selling product identification model assets to generate a standardized execution workflow. After determining how the business intelligence agent executes the standardized execution workflow based on the path impact strength and path credibility, the business intelligence agent issues the execution.

[0020] The evolution module includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel. It relies on the industry semantic layer to unify data semantics, the intelligent asset unit to accumulate operational capabilities, and the business scenario graph to complete reasoning and the closed-loop linkage of a three-element intelligent agent system consisting of data intelligent agents, strategy intelligent agents, and business intelligent agents.

[0021] The data intelligence agent locates operational semantic objects based on an operational semantic graph. These operational semantic objects include product objects, SKU objects, formula objects, raw material objects, store objects, member objects, business district objects, channel objects, activity objects, order objects, inventory objects, and supplier objects. Subsequently, it calls upon operational indicator assets to complete standardized calculations of operational indicators including average order value, table turnover rate, inventory turnover rate, and member activity. It then calls upon the best-selling product identification model asset to predict the probability of sales growth, product popularity index, and stockout risk probability. Finally, it calls upon the business district knowledge graph asset to capture dynamic changes in the external environment, ultimately obtaining the status of various operational objects. The states of business objects are combined according to a unified structure to form a business state vector. The business state vector is input into the business scenario graph and matched with the candidate influence paths in the business scenario graph. The scenario recognition score is calculated based on the node matching degree, path influence strength, and path credibility. When the scenario recognition score reaches a preset threshold, the business state under the current candidate influence path is taken as a candidate business scenario. When there are at least two candidate business scenarios, they are sorted from high to low according to the scenario recognition score. The candidate business scenario with the highest score is taken as the current main scenario, and the other candidate business scenarios are taken as concurrent auxiliary scenarios. Finally, a unified business state result is output.

[0022] The scene recognition score is calculated as follows:

[0023] Calculate node matching degree, path influence strength, and path credibility separately, as follows:

[0024] Node matching degree = number of matched nodes / number of nodes that should be matched in the candidate affected path;

[0025] Path influence intensity = ;

[0026] Path credibility = ;

[0027] Where n represents the total number of relationships in the candidate influence paths. This represents the relational influence strength of the i-th relation in the candidate influence path. This represents the relation weight of the i-th relation in the candidate influence path. This represents the relation credibility of the i-th relation in the candidate influence path;

[0028] The formula for calculating the scene recognition score is:

[0029] Scene recognition score = node matching degree × α + path influence intensity × β + path credibility × γ;

[0030] Where α, β, and γ are weight parameters, representing the importance of node matching degree, path influence strength, and path credibility in scene recognition, respectively, and α+β+γ=1.

[0031] The strategy agent receives the unified business status result output by the data agent, identifies the current business scenario based on the unified business status result through the industry semantic layer, retrieves relevant influence paths of the current business scenario through the business scenario graph, and prioritizes the influence paths whose influence intensity and credibility exceed the preset threshold for strategy reasoning.

[0032] Based on relevant influence paths, the system invokes merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets. Using a scenario matching engine within the strategy agent, it retrieves historically similar operating scenarios and extracts candidate strategies from the strategy template library of merchandise management strategy assets. These candidate strategies include replenishment strategies, marketing strategies, membership operation strategies, menu optimization strategies, and takeout operation strategies. Then, it invokes a strategy effectiveness library to comprehensively evaluate the candidate strategies in terms of sales increase, inventory health, fulfillment efficiency, user satisfaction, and revenue growth. A multi-objective optimization model is used to select the operating strategy with the best overall strategy effect. The indicators for the comprehensive evaluation of strategy effectiveness include expected sales increase, expected revenue growth, gross profit impact, marketing costs, inventory health, stockout risk, fulfillment pressure, user satisfaction, repeat purchase increase, and store execution capability. The strategy agent sets different indicator weights for the comprehensive evaluation indicators of strategy effectiveness according to different operating scenarios.

[0033] The business intelligence agent receives the optimal business strategy generated by the strategy intelligence agent, parses the business objects in the optimal business strategy through the industry semantic layer, maps the business objects to the business system, and obtains the strategy action object.

[0034] Based on the target of the strategy, the business intelligence agent determines the strategy type and calls the corresponding intelligent assets to generate a standardized execution workflow. When the strategy involves store execution, it calls the store operation SOP asset to determine the store tasks, execution steps, responsible roles, and execution time limits. When the strategy involves merchandise operation, it calls the merchandise management strategy asset to determine the relevant operational actions such as adjusting recommendation positions, menu item removal and removal, package combinations, and pricing strategies. When the strategy involves member outreach, it calls the member operation knowledge asset to determine the target audience, coupon rules, outreach channels, and outreach content. When the strategy involves inventory replenishment, it calls the operational indicator asset and the best-selling product identification model asset to determine the replenishment quantity, safety stock, allocation priority, and stockout risk threshold.

[0035] After generating a standardized execution workflow, the method by which the business intelligence agent executes the standardized execution workflow is determined by the path impact strength and path credibility of the optimal business strategy: when the path impact strength exceeds a first preset threshold, it is considered high impact strength, and vice versa; when the path credibility exceeds a second preset threshold, it is considered high credibility, and vice versa; if both impact strength and credibility are high, the business intelligence agent executes automatically directly; if the impact strength is high but the credibility is low, the business intelligence agent uses small-scale gray-scale execution or submits for manual confirmation; if the impact strength is low but the credibility is high, the current strategy is used as an auxiliary strategy reference; if both impact strength and credibility are low, the business intelligence agent only generates business suggestions and does not directly trigger automatic actions.

[0036] The industry semantic layer construction process includes four stages: semantic object extraction, semantic object normalization, semantic relationship construction, and business semantic graph generation, as detailed below:

[0037] In the semantic object extraction stage, the entire enterprise's internal and external operational data is integrated, covering POS sales data, CRM member data, ERP product data, WMS inventory data, takeaway order data, third-party weather and business district customer flow data, and full-domain marketing activity data. Relying on the object recognition engine of the data intelligence agent, twelve core operational semantic objects are extracted for the tea and catering industry, and a standardized and structured unified attribute set is constructed for each type of operational semantic object. The twelve core operational semantic objects include product objects, SKU objects, formula objects, raw material objects, store objects, member objects, business district objects, channel objects, activity objects, order objects, inventory objects, and supplier objects.

[0038] In the semantic object normalization stage, addressing the common issues of homonymous entities, different codes, and different representations in the tea and catering industries, the business semantic objects are uniformly mapped through attribute matching, code mapping, and association analysis. This completes the semantic alignment and unified identification of cross-system entities, resulting in normalized business semantic objects. Based on the normalized business semantic objects, a unified business semantic object system is constructed, including unified object identifiers, object types, object names, and object attribute sets.

[0039] In the semantic relationship construction phase, based on the normalized business semantic objects, and combined with the business rules of the tea and catering industries, historical operating data, and object extraction results, business relationships between business semantic objects are identified, and business semantic relationships are constructed. These business semantic relationships include source objects, relationship types, target objects, and relationship weights. The source object represents the initiating entity of the business semantic relationship; the target object represents the entity that influences the business semantic relationship; the relationship type describes the business association between two business semantic objects; and the relationship weight represents the influence coefficient of the business semantic relationship on the operating results.

[0040] In the business semantic graph generation stage, a business semantic graph is constructed based on business semantic objects and business semantic relationships; the business semantic graph is constructed with normalized business semantic objects as nodes and business semantic relationships as edges.

[0041] The specific construction of the business scenario map is as follows:

[0042] Business entities are identified based on a business semantic graph, and scene graph nodes are constructed. These scene graph nodes include environment nodes, user nodes, product nodes, store nodes, business status nodes, and business strategy nodes. Based on these scene graph nodes, relationships between them are constructed, and each relationship is configured with six structured parameters: source node, target node, relationship type, relationship influence strength, relationship credibility, and relationship weight. These relationships include influence relationships, dependency relationships, transformation relationships, substitution relationships, and collaborative relationships.

[0043] The credibility of the relationship is obtained by evaluating the credibility of each business relationship based on the sample size of historical business data, the frequency of relationship occurrence, the stability of the relationship, the proportion of negative examples of the relationship, and the verification results of the relationship strategy.

[0044] The strength of the relationship's influence is calculated using historical data, real-time data, and execution result data, as follows:

[0045] Relationship Influence Strength = Base Influence Value × Real-time Correction Value × Feedback Correction Value × Scenario Correction Coefficient;

[0046] Among them, the basic impact value represents the long-term average impact of the relationship in historical operating data; the real-time correction value represents the degree to which the current real-time operating status strengthens or weakens the long-term pattern; the feedback correction value represents the degree to which the historical strategy execution results verify the effectiveness of the relationship; and the scenario correction coefficient is used to represent the corrective effect of relevant scenario conditions on the relationship.

[0047] Event nodes are constructed based on the relationships between scene graph nodes. These event nodes are categorized into external environment events, platform traffic events, marketing campaign events, supply chain events, store operation events, and user feedback events. Each event node is structured with an event identifier, event type, event source, occurrence time, duration, associated objects, event intensity, and event status. The associated objects describe the stores, SKUs, member groups, business districts, channels, or inventory affected by the event. The event status indicates whether the event has occurred, is in progress, or has ended.

[0048] In the scenario graph, event nodes are connected to operational status nodes, product nodes, store nodes, and operational strategy nodes through influence relationships, trigger relationships, and conversion relationships;

[0049] Based on all nodes and relationships in the scenario graph, an influence path is constructed. This influence path includes a source node, intermediate nodes, result nodes, a set of path relationships, path influence strength, path credibility, and path applicability conditions. The source node represents the initial factor influencing the scenario change; intermediate nodes represent changes in business status during the influence transmission process; result nodes represent the final business or risk outcome; the path relationship set records the influence, dependency, transformation, or triggering relationships between adjacent nodes in the influence path; the path influence strength represents the degree of influence of the entire influence path on the final result; the path credibility represents the stability of the influence path in historical samples, real-time verification, and strategy execution feedback; and the path applicability conditions limit the types of stores, business districts, time periods, weather conditions, SKU types, or member groups to which the influence path is applicable.

[0050] The basic impact value is obtained by statistically analyzing the change range of the target node or target operating indicator before and after the change of the source node status from historical data according to the relationship type, and then standardizing the data.

[0051] The real-time correction value is calculated based on the deviation between the current relevant real-time indicator and the historical benchmark value, and the specific calculation is as follows:

[0052] Real-time correction value = 1 + Real-time deviation from overall value;

[0053] Among them, the real-time deviation composite value is obtained by weighting the degree of deviation of the current real-time indicator relative to the historical benchmark indicator;

[0054] The feedback correction value is calculated based on the strategy execution hit rate and effect deviation, as follows:

[0055] Feedback correction value = 1 + execution hit rate correction value - effect deviation penalty value;

[0056] Among them, the execution hit rate adjustment value is the ratio of the number of times the actual business results meet expectations after the relationship generation strategy is implemented to the total number of executions based on the relationship generation strategy;

[0057] The effect deviation penalty value is the average degree of deviation between the actual result and the predicted result, and it is calculated as follows:

[0058] Performance deviation penalty value = |Actual sales growth rate - Predicted sales growth rate| / Predicted sales growth rate;

[0059] The scene correction coefficient is obtained by combining several scene sub-coefficients, and the specific calculation is as follows:

[0060] Scene correction factor = Season correction factor × Business district correction factor × Store type correction factor × Time period correction factor × Holiday correction factor;

[0061] Among them, the seasonal adjustment coefficient is determined by the historical performance of the relationship in different seasons; the business district adjustment coefficient is determined by the historical performance of the relationship in different business district types; the store type adjustment coefficient is determined by different store types, store area, food preparation capacity and SKU structure; the time period adjustment coefficient is determined by the historical performance of the relationship in different time periods; and the holiday adjustment coefficient is determined by the historical change pattern of the relationship before and after holidays.

[0062] The intelligent asset unit includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operating indicator assets, holiday operation strategy assets, and membership operation knowledge assets; among which:

[0063] Store operation SOP assets are continuously recorded by the business intelligence agent, which tracks store operational behavior, including actions such as stocking, scheduling, food preparation, handling negative reviews, peak-hour operations, and inventory management. The data intelligence agent analyzes these operational behavior tracks to identify differences between high-performing and average stores, identifying high-performance difference paths. The strategy intelligence agent abstracts these high-performance difference paths into standardized SOPs, thus forming the store operation SOP assets. High-performing stores are those whose performance evaluation indicators are selected based on the operational indicator assets. Based on these indicators, similar stores are grouped and compared to identify those whose overall performance consistently outperforms the group average. These similar stores include those in the same business district, with the same store size, SKU structure, operating hours, weather conditions, and customer traffic.

[0064] The product management strategy assets are formed by continuously collecting multi-dimensional data, including product sales, sales rate, gross profit margin, user reviews, business district and weather characteristics, and user consumption behavior, through the data intelligence agent. Based on the product lifecycle sample library, the strategy intelligence agent further constructs a scenario feature library, a strategy template library, a strategy effect library, an applicable condition library, and a strategy relationship graph to form the product management strategy assets.

[0065] The hot-selling product identification model asset is constructed by collecting multi-dimensional data, including historical sales, business district traffic, weather, marketing activities, user behavior, and competitors, through a data intelligence agent. Based on the hot-selling product training samples, a sales prediction model, a popularity prediction model, a stockout risk model, and a hot-selling product probability model are generated. The hot-selling product identification model asset is formed by the sales prediction model, popularity prediction model, stockout risk model, and hot-selling product probability model.

[0066] The business district knowledge graph asset is formed by the continuous collection of multi-dimensional data by a data intelligence agent, including weather information, traffic information, business district customer flow, user profiles, competitor distribution, and food delivery platform data. Based on the multi-dimensional data, the business district knowledge graph asset is formed through entity recognition, relationship extraction, knowledge fusion, and graph construction.

[0067] Operational indicator assets are constructed by building a unified quantitative evaluation system for the industry. Based on the industry semantic layer, a unified indicator semantic is defined, which specifically includes average order value, table turnover rate, inventory turnover rate, member activity, marketing conversion rate, fulfillment time, and repurchase rate. Based on the unified indicator semantic, an indicator definition library, an indicator calculation logic library, an indicator dependency relationship library, and an indicator evaluation model library are established simultaneously, thereby forming operational indicator assets.

[0068] Holiday operation strategy assets are formed by continuously accumulating historical festival operation data through data intelligence agents and constructing four major resource databases based on effect attribution analysis, causal analysis, and strategy evaluation analysis techniques: pre-festival inventory, in-festival marketing database, post-festival repurchase database, and regional festival operation database.

[0069] Membership operation knowledge assets are formed by data intelligence agents building user profiles based on user consumption behavior, product preferences, lifecycle changes, and activity participation. These user profiles are then used to further develop a member tag library, a lifecycle model library, a user preference knowledge base, a recall strategy library, a recommendation strategy library, and a growth path model library, thus creating membership operation knowledge assets.

[0070] Based on the collaborative methods of data intelligence agents, strategy intelligence agents, and business intelligence agents at different time periods, enterprise operations are divided into time-scale levels according to response timeliness. These time-scale levels include a real-time response layer, an operational control layer, a strategy optimization layer, and a business evolution layer; wherein:

[0071] The real-time response layer handles operational events ranging from seconds to minutes; the data intelligence agent is responsible for real-time monitoring of abnormal data and identifying immediate scenarios; the strategy intelligence agent is responsible for quickly matching emergency strategies; and the business intelligence agent is responsible for immediately executing actions related to inventory locking, menu removal, replenishment reminders, handling negative reviews, and store alarms.

[0072] The operations control layer handles hourly operational events; the data intelligence agent is responsible for continuously monitoring store operating status, customer flow changes, and fulfillment pressure; the strategy intelligence agent is responsible for generating control strategies, including inventory preparation, scheduling, menu, and takeout operations; and the business intelligence agent is responsible for executing actions such as issuing store tasks, adjusting scheduling, adjusting product recommendation positions, and reminding customers to prepare inventory.

[0073] The strategy optimization layer handles daily to weekly operational issues; the data intelligence agent is responsible for analyzing operational results and strategy effectiveness over a period of time; the strategy intelligence agent is responsible for comparing multiple strategies, attributing effects, and optimizing strategy parameters; and the business intelligence agent is responsible for configuring activities, adjusting product mixes, issuing coupons, and executing member outreach plans.

[0074] The operational evolution layer handles the accumulation of operational patterns at the monthly level and above; the data intelligence agent is responsible for the accumulation of long-term operational data and pattern identification; the strategy intelligence agent is responsible for accumulating strategy templates, strategy effect libraries, and strategy assets; and the business intelligence agent is responsible for accumulating replicable store operation SOPs and standardized execution processes.

[0075] The evolution module includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel; wherein:

[0076] The data flywheel starts with the data generated by the business intelligence agent during real business execution. During execution, the business intelligence agent continuously generates multi-dimensional data. This multi-dimensional data is then normalized and semantically standardized through an industry semantic layer, mapping store, SKU, member, inventory, activity, and order data from different systems into standard operational semantic objects. Based on these standard operational semantic objects, the data intelligence agent performs data cleaning, feature extraction, indicator calculation, anomaly identification, and state modeling. The processed data is then input into the operational scenario graph to identify the current operational scenario and update the status of scenario nodes.

[0077] The strategy flywheel consists of a strategy agent that receives unified operational status results from the data agent, retrieves influencing paths based on the operational scenario graph, and calls upon intelligent assets including merchandise operation strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to generate a candidate strategy set. The candidate strategies are evaluated to obtain the optimal strategy. The business agent breaks down the optimal strategy into specific actions and executes them through various business systems. After execution, the result data is collected and fed back to the strategy agent to update the strategy effect library, strategy template library, applicable condition library, and strategy relationship graph. The result data includes changes in sales volume, inventory health, marketing conversion rate, fulfillment efficiency, user satisfaction, gross profit changes, and repeat purchase changes.

[0078] The SOP flywheel involves the business intelligence agent continuously recording actual operational trajectories during daily store operations. Based on these trajectories, the data intelligence agent evaluates store performance using operational metrics assets, identifying behavioral differences between high-performing and average stores to derive high-performance difference paths. The strategy intelligence agent further abstracts these high-performance difference paths into standardized SOPs, forming store operation SOP assets. Subsequently, when similar operational scenarios occur in other stores, the data intelligence agent identifies the scenario, the strategy intelligence agent matches the SOP assets, and the business intelligence agent automatically generates store tasks and drives their execution.

[0079] The business knowledge flywheel uses an industry semantic layer to uniformly describe business objects and uses a business semantic graph to accumulate the basic relationships between business objects. At the same time, it uses a business scenario graph to accumulate event nodes, business status nodes, business strategy nodes and influence paths, record the scenario-based influence patterns between different business factors, form business knowledge, and feed back into the state recognition of data intelligence agents, the path reasoning of strategy intelligence agents and the action execution of business intelligence agents.

[0080] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0081] 1. This invention constructs a data and strategy intelligence agent and an operational scenario map to collect multi-dimensional operational data inside and outside stores and transform it into an operational status vector, accurately identifying various dynamic operational scenarios. It abandons the traditional fixed static processing mode, and automatically identifies scenarios and dynamically generates operational strategies based on real-time operational status. The business intelligence agent then issues multiple control actions, realizing the transformation of the system from post-event data recording to real-time perception and proactive operation control, greatly improving the adaptability of catering and tea beverage enterprises to cope with various complex operational fluctuations.

[0082] 2. This invention unifies the operational objects and indicator standards of all categories through the industry semantic layer, and uses data, strategy and business three-layer intelligent agents to realize store operation status identification, full-domain operation strategy generation and store implementation task breakdown respectively; it breaks the traditional model of headquarters relying on lagging reports for decision-making and stores relying on personal experience to handle matters, and transforms headquarters strategies into standardized execution processes to be accurately distributed to each operation dimension, opens up the complete link from headquarters strategy to store implementation, reduces information lag and execution deviation, and ensures that headquarters operation plans can be implemented in stores in a timely, accurate and traceable manner.

[0083] 3. This invention, through an operational scenario mapping and a multi-timescale operational cognition mechanism, divides enterprise operations into a real-time response layer, an operational control layer, a strategy optimization layer, and an operational evolution layer. For sudden operational anomalies occurring at the second / minute level, it relies on three types of intelligent agents to quickly identify scenarios, match emergency strategies, and immediately implement action. For cyclical operational needs ranging from hours to months, it completes layered optimization of scheduling and inventory preparation, merchandise and membership strategies, and the accumulation of operational experience and standard processes. This invention can adapt to differentiated perception, decision-making, and execution of operational events with varying timeframes, balancing immediate risk management with medium- to long-term operational iteration, and comprehensively improving the dynamic operational response level of catering and beverage enterprises.

[0084] 4. This invention transforms outstanding stores, outstanding store managers, or high-performing operational actions into standardized and reusable store operation SOP assets through the SOP flywheel in the intelligent asset unit and evolution module; it relies on three types of intelligent agents to record the entire process of store operation behavior, distinguish the differences between high and low performance operation behaviors, extract standardized operation SOPs, and automatically issue and execute them based on scenarios; it transforms store operation methods that rely on the store manager's personal experience into enterprise-level digital assets that can be accumulated, called, replicated, and iterated, reducing the difficulty of experience replication during store expansion and improving the operational stability and consistency between different stores.

[0085] 5. This invention extracts and normalizes multi-system data through an industry semantic layer and builds an operational semantic graph, unifying the standards and relationships of various business objects, eliminating differences in data names, codes, and definitions across multiple systems, and supporting three types of intelligent agents to conduct identification, reasoning, and execution based on unified semantics; it solves the problem of fragmented data across multiple systems, providing a unified cognitive foundation for intelligent asset construction, operational scenario graph reasoning, and the collaboration of three types of intelligent agents, and promoting the upgrading of enterprises from basic informatization to full-link intelligent operation. Attached Figure Description

[0086] Figure 1 This is the overall architecture diagram of the intelligent operation system for the entire enterprise chain of tea drinks and catering described in this invention. Detailed Implementation

[0087] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0088] This invention addresses the industry pain points of heterogeneous data across multiple business systems in the tea and catering industry, fragmented business semantics, fragmented operational cognition, and the lack of a unified support base for intelligent decision-making. It constructs a full-link intelligent operation system based on an industry semantic layer, with intelligent asset units as capability carriers, an operational scenario graph as a reasoning hub, and a three-element intelligent agent as the execution terminal. Through the deep integration and collaborative linkage of multi-layer technical architecture, it realizes closed-loop intelligent operation of catering business from data collection, state perception, scenario reasoning, strategy generation to business execution and iterative optimization, solving the problems of single-point analysis, one-sided cognition, lagging strategies, and fragmented execution in the traditional operation model.

[0089] like Figure 1 A smart operation system for the entire supply chain of tea and catering enterprises, comprising a data intelligence agent, a strategy intelligence agent, a business intelligence agent, an intelligent asset unit, an industry semantic layer, an operational scenario graph, and an evolution module; wherein:

[0090] The industry semantic layer is used to uniformly extract, normalize, and construct semantic relationships for the business semantic objects in the tea and catering industry, forming a business semantic graph.

[0091] The business scenario graph identifies business entities through a business semantic graph and constructs scenario graph nodes. Based on the scenario graph nodes, it constructs the association relationships between the scenario graph nodes and configures structured parameters for the association relationships, including the relationship influence strength and relationship credibility.

[0092] The intelligent asset unit is used to store the data, experience, knowledge, and strategic results generated in the long-term operation of the tea and catering industry, and to convert them into digital assets. Specifically, it includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operating indicator assets, holiday operation strategy assets, and membership operation knowledge assets.

[0093] The data intelligence agent, based on the business semantic graph, calls upon business indicator assets, best-selling product identification model assets, and business district knowledge graph assets to identify the business status vectors of various business objects in the tea and catering industries; inputs the business status vectors into the business scenario graph, matches them with candidate influence paths in the business scenario graph, and calculates the scenario identification score based on node matching degree, path influence strength, and path credibility; when the scenario identification score reaches a threshold, it outputs a unified business status result.

[0094] The strategy intelligence agent identifies the current business scenario based on the unified business status results, retrieves relevant influence paths of the current business scenario through the business scenario graph, and, based on the relevant influence paths, calls upon merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to screen candidate strategies; and performs a comprehensive evaluation of the strategy effectiveness of the candidate strategies to generate the optimal business strategy.

[0095] The business intelligence agent analyzes the optimal business strategy and determines the strategy type. Based on the strategy type, it calls the corresponding store operation SOP assets, merchandise operation strategy assets, membership operation knowledge assets, or business indicator assets and best-selling product identification model assets to generate a standardized execution workflow. After determining how the business intelligence agent executes the standardized execution workflow based on the path impact strength and path credibility, the business intelligence agent issues the execution.

[0096] The evolution module includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel. It relies on the industry semantic layer to unify data semantics, the intelligent asset unit to accumulate operational capabilities, and the business scenario graph to complete reasoning and the closed-loop linkage of a three-element intelligent agent system consisting of data intelligent agents, strategy intelligent agents, and business intelligent agents.

[0097] I. Industry Semantic Layer

[0098] The industry semantic layer serves as the underlying cognitive foundation of the entire system. It addresses the issues of inconsistent object definitions, conflicting business semantics, and fragmented operational cognition among POS systems, CRM systems, ERP systems, WMS systems, food delivery platforms, and third-party operational data. It provides a unified, standardized, and interoperable operational cognitive standard for the collaborative work of all intelligent assets, scenario graphs, and intelligent agents. The construction process of the industry semantic layer includes four stages: semantic object extraction, semantic object normalization, semantic relationship construction, and operational semantic graph generation, as detailed below:

[0099] In the semantic object extraction phase, the system comprehensively integrates internal and external operational data of the enterprise, covering POS sales data, CRM member data, ERP product data, WMS inventory data, takeaway order data, third-party weather and business district customer flow data, and full-domain marketing activity data. Relying on the object recognition engine of the data intelligence agent, it extracts twelve core operational semantic objects for the tea and catering industry, specifically including product objects, SKU objects, recipe objects, raw material objects, store objects, member objects, business district objects, channel objects, activity objects, order objects, inventory objects, and supplier objects. For each type of operational semantic object, a standardized and structured unified attribute set is constructed to unify the field dimensions and description specifications of various heterogeneous data from the source, eliminating the underlying differences between data from multiple systems.

[0100] In the semantic object normalization stage, addressing the common issues of different names, codes, and representations of the same entity in the tea and catering industries, a unified mapping of operational semantic objects is achieved through attribute matching, code mapping, and association analysis. This completes the semantic alignment and unified identification of entities across systems. For example, the same beverage entity referred to by the custom name in the POS system, the code in the ERP system, the ID in the food delivery platform, and the note name in the CRM system are uniformly mapped to a standardized operational semantic object identifier, resulting in a normalized operational semantic object. Finally, a unified operational semantic object system is constructed, including a unified object identifier, object type, object name, and object attribute set. Members, stores, inventory, and activities in different systems are all mapped to the unified operational semantic object system, achieving full-domain semantic unification of all operational entities.

[0101] For example, the same beverage entity is bubble tea in the POS system; SKU-10001 in the ERP system; ID-8866 on the food delivery platform; and product A in the CRM system; and is ultimately mapped to a beverage product object: Drink_Product_001.

[0102] In the semantic relationship construction phase, based on the normalized operational semantic objects, combined with the inherent business rules of the tea and catering industries, massive historical operational data, and object extraction results, business relationships between operational semantic objects are identified, and operational semantic relationships are constructed. These operational semantic relationships include source objects, relationship types, target objects, and relationship weights. The source object represents the initiating entity of the operational semantic relationship; the target object represents the acting entity of the operational semantic relationship; the relationship type describes the business relationship between two operational semantic objects (source object and target object); and the relationship weight represents the influence coefficient of the operational semantic relationship on the operational results. For example, there is a "sales impact relationship" between weather objects and product objects; a "customer flow impact relationship" between business district objects and store objects; a "preference relationship" between member objects and product objects; a "dependency relationship" between product objects and raw material objects; and a "conversion relationship" between marketing activity objects and order objects.

[0103] In the business semantic graph generation stage, a business semantic graph is constructed based on business semantic objects and business semantic relationships. The business semantic graph is constructed with normalized business semantic objects as nodes and business semantic relationships as edges. Among them, the business semantic relationships include influence relationships, dependency relationships, attribution relationships, transformation relationships, substitution relationships, and synergy relationships.

[0104] The operational semantic graph covers the entire operational chain of catering supply chain, store operation, membership marketing, inventory scheduling, and environmental adaptation, providing a unified semantic benchmark and data support for upper-level intelligent asset construction, scenario reasoning, and intelligent agent collaboration. Data intelligent agents, strategy intelligent agents, and business intelligent agents all complete operational status identification, operational strategy reasoning, and operational action execution based on the operational semantic graph, realizing a unified operational cognition system for enterprises.

[0105] II. Business Scenario Map

[0106] The business scenario graph is built on the foundation of the industry semantic layer. By constructing a network of relationships between business objects, business events, business status, and business influence relationships, it enables automatic identification of business scenarios, prediction of scenario evolution, and reasoning of business strategies. It is used to solve the problem that traditional operation systems can only analyze based on single-point data and cannot understand complex business scenarios and the linkage of multiple factors. It is the core reasoning hub connecting the underlying semantic foundation, the middle-layer intelligent assets, and the upper-layer intelligent agent applications.

[0107] Although both the business semantic graph and the business scenario graph adopt a graph structure, they solve different problems, have different construction purposes, and are used in different ways, as detailed below:

[0108] The business semantic graph primarily addresses the issue of "unified understanding of business objects and relationships," used to uniformly describe the basic objects and relationships involved in business operations; for example, what is a store, what is an SKU, what is a member, what is inventory, what is the relationship between SKU and raw materials, what is the relationship between members and products, and what is the relationship between a store and a business district; the business semantic graph leans towards underlying standardization and unified understanding, mainly used to eliminate problems such as inconsistent object definitions, inconsistent field definitions, and fragmented business semantics between different systems.

[0109] The business scenario graph is a dynamic reasoning graph built on the business semantic graph. It mainly solves the problem of "what business scenario is happening now, and what strategy should be adopted in this scenario". It not only includes business objects, but also introduces environment nodes, state nodes, event nodes, strategy nodes and dynamic influence relationships. It is used to identify dynamic business scenarios such as high-temperature cold drink outbreak scenarios, lunch peak fulfillment pressure scenarios, surge in takeaway orders scenarios, inventory risk scenarios, and member churn risk scenarios.

[0110] The business semantic graph answers the question, "What are the business objects, and what are the basic relationships between them?" The business scenario graph answers the question, "What business scenario do these objects and relationships currently form, how does this scenario evolve, and what business strategies should be triggered?" Therefore, the business semantic graph is the underlying cognitive foundation, which is more static, standardized, and uses a unified approach; the business scenario graph is the upper-level reasoning hub, which is more dynamic, scenario-based, and uses strategic reasoning. The business scenario graph relies on the business semantic graph to provide unified objects and relationships, and further performs dynamic calculations for real-time business status and strategy generation.

[0111] The construction of the business scenario map is as follows:

[0112] (1) First, business entities are identified based on the business semantic graph, and scene graph nodes are constructed. The scene graph nodes include six major categories of core nodes: environment nodes (weather, temperature, rainfall, holidays, weekdays, time periods, business district activities, traffic conditions, etc.), user nodes (member groups, new users, old users, high-value members, dormant members, members at risk of churn, etc.), product nodes (SKU, product categories, packages, new products, best-selling products, long-tail products, etc.), store nodes (stores, business districts, warehouses, fulfillment centers, etc.), business status nodes (hot-selling, out of stock, slow-selling, order surge, queue, peak, fulfillment abnormality, etc.), and business strategy nodes (replenishment, allocation, marketing, recommendation, discounts, membership operation, etc.), which fully cover the core variables inside and outside the tea and catering business.

[0113] (2) Based on the scene graph nodes, construct the association relationships between the scene graph nodes. The association relationships include influence relationships, dependency relationships, transformation relationships, substitution relationships and synergy relationships. For example, influence relationships are reflected in: high temperature weather → increased sales of cold drinks; rainstorm weather → increased takeaway orders; holidays → increased customer flow.

[0114] The dependency relationship is reflected in: SKU → raw material inventory; raw material inventory → commodity supply capacity; commodity supply capacity → fulfillment capacity;

[0115] The conversion relationship is reflected in: marketing activities → user orders; user orders → member growth; member growth → increased repeat purchases;

[0116] The substitution relationship is reflected in: bubble tea shortage → taro milk tea sales increase; coffee shortage → fruit tea sales increase.

[0117] The synergistic relationship is reflected in: Package A → Increased sales of Product B; Product C → Increased sales of Product D;

[0118] Simultaneously, six structured parameters are configured for each set of relationships, including source node, target node, relationship type, relationship influence strength, relationship credibility, and relationship weight. Among them, the source node is used to represent the initiating object of the relationship; the target node is used to represent the object affected by the relationship; and the relationship weight is used to represent the influence coefficient of the business semantic relationship on the business results.

[0119] Relationship types are used to represent the association between two business nodes. These relationship types include, but are not limited to, influence relationships, dependency relationships, conversion relationships, substitution relationships, synergistic relationships, competitive relationships, and affiliation relationships. For example, high temperatures and cold drink sales form an influence relationship; goods and raw materials form a dependency relationship; marketing activities and order growth form a conversion relationship; similar products form a substitution relationship; package deals form a synergistic relationship; competitor activities and store sales form a competitive relationship; and stores and business districts form an affiliation relationship.

[0120] The strength of relationship influence is used to represent the magnitude of the impact of one business factor on another business factor or business outcome. The system calculates the strength of relationship influence based on historical data, real-time data, and execution result data. The strength of relationship influence can be adjusted in real time according to seasons, scenarios, and business rhythm. The specific calculation of the strength of relationship influence is as follows:

[0121] Relationship Influence Strength = Base Influence Value × Real-time Correction Value × Feedback Correction Value × Scenario Correction Coefficient;

[0122] Among them, the basic impact value represents the long-term average impact of the relationship in historical operating data; the real-time correction value represents the degree to which the current real-time operating status strengthens or weakens the long-term pattern; the feedback correction value represents the degree to which the historical strategy execution results verify the effectiveness of the relationship; and the scenario correction coefficient is used to represent the corrective effect of scenario conditions such as season, business district, store type, time period, and holidays on the relationship.

[0123] The basic impact value is obtained by statistically analyzing the change range of the target node or target operating indicator before and after the change of the source node status from historical data according to the relationship type, and then standardizing the data.

[0124] Taking the relationship between "high temperatures and increased sales of cold drinks" as an example, the system first extracts historical high-temperature weather samples and statistically analyzes the changes in cold drink sales before and after the occurrence of high-temperature weather; the basic impact value can be calculated as follows:

[0125] Basic impact value = Standardized average sales growth rate × Frequency coefficient × Stability coefficient;

[0126] in:

[0127] Average sales growth rate = (Average sales of cold drinks during high-temperature periods - Average sales of cold drinks during non-high-temperature baseline periods) / Average sales of cold drinks during non-high-temperature baseline periods;

[0128] Frequency coefficient = Number of samples showing increased sales of cold drinks during hot weather / Total number of samples during hot weather;

[0129] The stability coefficient is used to indicate whether the relationship occurs stably in different stores, different business districts, and different time periods. It can be calculated based on the variance or volatility of the relationship in different sample groups. The smaller the volatility, the higher the stability coefficient; the greater the volatility, the lower the stability coefficient.

[0130] For example, if historical data shows that cold drink sales increase by an average of 30% during hot weather, and this increase occurs in 80% of the hot weather samples, and this pattern is relatively stable in most stores and business districts, then the basic impact value is high; if the sales increase during hot weather is not significant, or only occurs in a small number of stores, then the basic impact value is low.

[0131] Regarding the relationship between "food delivery platform activities - food delivery order growth", the basic impact value can be calculated based on the order growth rate before and after the platform activity, the frequency of order growth triggered by the activity, and the stability of the activity effect in different stores.

[0132] The basic impact value for the relationship between "decline in raw material inventory - increase in stockout risk" can be calculated based on the relationship between the historical decline in inventory and the probability of stockouts.

[0133] Therefore, the basic impact value represents the long-term impact level of the business relationship in the historical sample.

[0134] The real-time correction value is calculated based on the deviation between the current relevant real-time indicator and the historical benchmark value; the specific calculation of the real-time correction value is as follows:

[0135] Real-time correction value = 1 + Real-time deviation from overall value;

[0136] Among them, the real-time deviation composite value is obtained by weighting the degree of deviation of the current real-time indicator relative to the historical benchmark indicator;

[0137] Taking the relationship between "high temperatures and increased sales of cold drinks" as an example, the system can select indicators such as current mini-program views, food delivery platform clicks, add-to-cart volume, real-time order volume, store traffic, and inventory availability as real-time correction factors.

[0138] The real-time deviation composite value can be expressed as:

[0139] Real-time deviation composite value = Pageview deviation × First weight + Click deviation × Second weight + Add-to-cart deviation × Third weight + Real-time order deviation × Fourth weight + Customer flow deviation × Fifth weight - Insufficient inventory penalty value × Sixth weight.

[0140] in:

[0141] Pageview Deviation = (Current Pageviews - Historical Average Pageviews for the Same Scenario) / Historical Average Pageviews for the Same Scenario;

[0142] Click deviation = (Current food delivery clicks - Historical average clicks for the same scenario) / Historical average clicks for the same scenario;

[0143] Add-to-cart deviation = (Current add-to-cart volume - Historical average add-to-cart volume for the same scenario) / Historical average add-to-cart volume for the same scenario;

[0144] Real-time order deviation = (Current real-time order volume - Historical average order volume for the same scenario) / Historical average order volume for the same scenario;

[0145] Customer flow deviation = (Current store customer flow - Historical average customer flow in the same scenario) / Historical average customer flow in the same scenario;

[0146] The inventory shortage penalty value is used to represent the inhibitory effect of current inventory shortage on sales growth, and can be calculated based on the gap between current available inventory and forecasted demand.

[0147] The first through sixth weights are adjusted based on the scenario;

[0148] If the current page views, clicks, add-to-cart counts, and real-time order volume are all higher than the historical levels for the same scenario, the real-time correction value is greater than 1, indicating that the current relationship influence has been strengthened; if the current traffic is low, platform exposure has decreased, or inventory is insufficient, the real-time correction value is less than 1, indicating that the current relationship influence has been weakened.

[0149] Therefore, the real-time correction value is not simply a description of "increase" or "decrease", but is calculated based on the degree of deviation of the current real-time operating indicators from the historical benchmark indicators.

[0150] The feedback correction value is calculated based on the strategy execution hit rate and effect deviation, as follows:

[0151] Feedback correction value = 1 + execution hit rate correction value - effect deviation penalty value;

[0152] in:

[0153] The execution hit rate adjustment value is the number of times the actual business results meet expectations after the relationship generation strategy is based on the relationship generation strategy / the total number of executions based on the relationship generation strategy;

[0154] The effect deviation penalty value is the average degree of deviation between the actual result and the predicted result, and it is calculated as follows:

[0155] Performance deviation penalty value = |Actual sales growth rate - Predicted sales growth rate| / Predicted sales growth rate;

[0156] Taking the relationship of "high temperature weather - increased sales of cold drinks - increased raw material consumption" as an example, if the system has generated replenishment and recommendation position enhancement strategies based on this relationship, the system will collect data such as actual cold drink sales, raw material consumption, inventory turnover rate, stockout rate and conversion rate after execution.

[0157] If, after the strategy is implemented, the increase in cold drink sales, the increase in raw material consumption, and the improvement in inventory turnover are all in line with the predicted direction, then the relationship is effective and the feedback correction value is increased.

[0158] If the sales of cold drinks do not increase after the strategy is implemented, or if there is an inventory backlog after restocking, it indicates that the relationship is not effective enough in this store, business district or time period, and the feedback correction value decreases.

[0159] Therefore, the feedback correction value is used to reverse the actual results after the strategy is executed and apply them to the business relationship, so that the business scenario map can continuously correct the strength of the relationship influence.

[0160] The scene correction coefficient is obtained by combining multiple sub-coefficients, and the specific calculation is as follows:

[0161] Scene correction factor = Season correction factor × Business district correction factor × Store type correction factor × Time period correction factor × Holiday correction factor;

[0162] in:

[0163] The seasonal adjustment factor is determined by the historical performance of this relationship in different seasons; for example, the impact of hot weather on cold drink sales is stronger in summer and weaker in winter.

[0164] The business district adjustment coefficient is determined by the historical performance of this relationship under different business district types; for example, the increase in orders during the lunch peak is more obvious in office building business districts, while the increase in evening orders is more obvious in community business districts.

[0165] The store type correction factor is determined by different store types, store area, food preparation capacity, and SKU structure;

[0166] The time period correction factor is determined by the historical performance of this relationship during different time periods such as breakfast, lunch peak, afternoon tea, and evening peak.

[0167] The holiday correction factor is determined by the historical variation of this relationship before and after the holiday.

[0168] Each correction coefficient can be obtained from historical sample statistics, or it can be preset according to the company's operating rules and then dynamically corrected through execution feedback.

[0169] Example of calculating the strength of a relationship: Taking the relationship of "high temperatures - increased sales of cold drinks" as an example:

[0170] The system calculates the baseline impact value based on historical data; if the average sales of cold drinks increase by 30% in the historical high-temperature samples, and this occurs frequently and is stable across stores, then a higher baseline impact value is obtained.

[0171] The system then calculates the real-time correction value based on the current real-time data; if the current mini-program views, food delivery clicks, add-to-cart counts, and real-time order counts are all higher than the historical average for the same scenario, the real-time correction value is greater than 1; if the current inventory is insufficient or the platform exposure has decreased, the real-time correction value is less than 1.

[0172] The system then calculates the feedback correction value based on the historical strategy execution results. If, in most cases, the sales growth and inventory turnover improvement are in line with the forecast after the recommendation position enhancement and replenishment strategies were executed based on this relationship, the feedback correction value is greater than 1. If the execution effect frequently deviates from the forecast, the feedback correction value is less than 1.

[0173] Finally, the system calculates the scenario correction coefficient based on the current scenario conditions such as summer, office building business district, and lunchtime peak hours; if these scenario conditions historically increase the demand for cold drinks, the scenario correction coefficient is greater than 1.

[0174] Ultimately, the system calculates the strength of the relationship influence:

[0175] Relationship Influence Strength = Base Influence Value × Real-time Correction Value × Feedback Correction Value × Scenario Correction Coefficient;

[0176] To obtain the specific impact strength of this relationship in the current scenario;

[0177] The impact intensity is further used for scene identification by data agents, path ranking by strategy agents, and execution control by business agents. For example, when the impact intensity is high, the system is more likely to identify "high-temperature cold drink outbreak scenarios," and the strategy agent will prioritize generating replenishment, recommendation ranking, and SKU replacement strategies. The business agent can increase the replenishment volume, expand the recommendation range, or improve the recommendation ranking. When the impact intensity is low, the system will only generate reminders or make minor adjustments to avoid over-execution.

[0178] Relationship credibility guides the priority of different relationships in subsequent scenario reasoning. The system assesses the credibility of each business relationship based on the size of historical business data samples, the frequency of relationship occurrence, the stability of the relationship, the proportion of counterexamples, and the results of relationship strategy verification. Regarding the size of the historical business data samples, if a relationship appears only in a small amount of data, its credibility is low; if it appears repeatedly in a large number of historical samples, its credibility is high. Regarding the frequency of relationship occurrence, if a relationship appears repeatedly in similar business scenarios, its credibility increases; for example, multiple periods of high temperatures are accompanied by increased sales of cold drinks. Regarding the stability of the relationship, if the relationship remains stable across different stores, different business districts, and different time periods, its credibility is high; if it only appears occasionally in a few stores, its credibility is low. Regarding the proportion of counterexamples, if there are many counterexamples corresponding to the relationship in a scenario, the credibility of the relationship decreases; for example, if there are many counterexamples where cold drink sales do not increase after high temperatures, the credibility of the relationship decreases. Regarding the results of relationship strategy verification, if the actual business results meet expectations after the system generates and executes a strategy based on the relationship, the credibility of the relationship increases; if the execution results deviate from expectations for a long period, the credibility decreases.

[0179] Relationship credibility is used to prioritize system reasoning and execution. In data intelligence agents, high-credibility relationships serve as important criteria for scene recognition, while low-credibility relationships only serve as auxiliary signals. In policy intelligence agents, high-credibility relationship paths are prioritized for policy reasoning, while low-credibility paths can enter the candidate policy pool but are ranked lower. In business intelligence agents, policies corresponding to high-credibility relationships can be executed automatically; policies corresponding to medium-credibility relationships can be implemented using gray-scale testing or small-scale trials; policies corresponding to low-credibility relationships require manual confirmation or are only presented to operations personnel as business suggestions.

[0180] For example, if the relationship between "high temperature weather - increased sales of cold drinks" has a high impact and high credibility, the system can automatically trigger the replenishment of cold drinks and the adjustment of the recommendation position; if the sample size of the hot-selling relationship between a new SKU and a certain business district is small and the credibility is low, the system will only generate pilot suggestions and will not directly implement them on a large scale.

[0181] (3) Construct event nodes based on the relationship between scene graph nodes and the scene graph nodes; the event nodes are used to describe business events or external events that occur at a specific time point or time window and can trigger changes in business status; the event nodes are divided into external environment events, platform traffic events, marketing activity events, supply chain events, store operation events and user feedback events, specifically including changes in the ranking of the takeaway platform, the start of platform activities, the arrival of holidays, large-scale events in the business district, sudden rainfall, competitor promotions, new product launches, coupon distribution, inventory arrival, raw material supply disruptions, negative reviews, abnormal queuing at stores, and order surges, etc.

[0182] For example, the arrival of hot weather is an external environmental event node; the start of a food delivery platform's promotion is a platform traffic event node; the distribution of coupons is a marketing event node; the arrival of inventory or shortage of raw materials is a supply chain event node; abnormal queuing during the lunch rush is a store operation event node; and the appearance of negative reviews or member complaints is a user feedback event node.

[0183] Each event node is structured and described by event identifier, event type, event source, occurrence time, duration, associated objects, event intensity, and event status. Among them, associated objects are used to describe the stores, SKUs, member groups, business districts, channels, or inventory objects affected by the event; the event status is used to describe whether the event has occurred, is in progress, or has ended.

[0184] In the scenario graph, event nodes are connected to operational status nodes, product nodes, store nodes, and operational strategy nodes through influence relationships, trigger relationships, and conversion relationships; for example, "high temperature weather event" is connected to "cold drink sales growth status"; "takeaway platform activity start event" is connected to "takeaway order growth status"; "inventory arrival event" is connected to "out-of-stock risk reduction status"; and "negative review occurrence event" is connected to "negative review handling strategy node".

[0185] (4) Construct the influence path based on all nodes and relationships in the scene graph:

[0186] The influence path is a directed inference link formed by combining multiple scenario graph nodes and multiple relationships according to the direction of business influence transmission. It is used to describe how a certain business factor gradually affects the final business result through multiple intermediate states.

[0187] A single relationship can only express the direct impact between two nodes, such as "high temperatures affect cold drink sales"; while an impact path can express the continuous transmission relationship between multiple business elements, such as "high temperatures—increased demand for cold drinks—increased raw material consumption—decreased inventory—increased stockout risk—triggered replenishment strategy". Therefore, impact paths are an important basis for business scenario identification, scenario evolution prediction, and strategy generation.

[0188] The impact path includes the origin node, intermediate nodes, result node, path relationship set, path impact strength, path credibility, and path applicability conditions; among which:

[0189] Origin nodes are used to represent the initiating factors of scene changes, such as high temperatures, food delivery platform activities, increased customer traffic in shopping districts, competitor promotions, and inventory arrivals.

[0190] Intermediate nodes are used to represent changes in business status during the transmission of influence, such as increased demand for cold drinks, increased raw material consumption, decreased inventory, and increased pressure to fulfill contracts.

[0191] Result nodes are used to represent the final operating results or risk outcomes, such as increased stockout risk, sales growth, fulfillment anomalies, and increased risk of member churn.

[0192] The path relationship set is used to record the influence, dependency, transformation, or triggering relationships between adjacent nodes in a path;

[0193] Path influence intensity is used to represent the degree of influence of the entire path on the final result;

[0194] Path credibility is used to represent the stability of a path across historical samples, real-time verification, and policy execution feedback.

[0195] The route application conditions are used to limit the types of stores, business districts, time periods, weather conditions, SKU types, or member groups to which the route applies.

[0196] For example, in the "high-temperature cold drink outbreak scenario," the impact path can be represented as:

[0197] High temperature weather event → Increased demand for cold drinks → Increased sales of cold drink SKUs → Increased consumption of key raw materials → Decreased raw material inventory → Increased risk of stockout → Replenishment strategy node.

[0198] This path indicates that high temperatures do not directly trigger replenishment strategies. Instead, they gradually lead to increased stockout risks through multiple intermediate states, such as increased demand for cold drinks, increased SKU sales, increased raw material consumption, and decreased inventory, ultimately triggering replenishment strategies.

[0199] For example, in the "midday peak performance pressure scenario," the impact path can be represented as follows:

[0200] During the lunch peak period → Increased customer traffic at the store → Increased dine-in orders → Increased pressure on the kitchen → Increased fulfillment time → Increased risk of negative reviews → Nodes for adjusting scheduling strategies or prioritizing food preparation strategies.

[0201] When performing scene recognition, the system does not only determine whether a single node appears, but matches the real-time business status vector with multiple influence paths in the business scene graph. When multiple nodes on a certain influence path are matched by the real-time status, and the influence strength and confidence of the path reach a preset threshold, the system can determine the corresponding business scene that the current business status matches.

[0202] The system has a robust dynamic update mechanism for business relationships: the data intelligence agent continuously receives real-time business data such as orders, inventory, members, business districts, weather, and marketing activities, while also receiving business result data generated after the business intelligence agent executes business strategies; the system identifies the current business status based on real-time business data, evaluates the actual effectiveness of business relationships under the current business status based on business result data, and dynamically adjusts the influence intensity, relationship weight, and relationship credibility of business relationships according to their actual effectiveness; through the dynamic update mechanism for business relationships, the scenario graph can reflect the actual operating patterns of tea and catering enterprises in real time.

[0203] Based on a complete scenario graph, the system forms a standardized scenario reasoning mechanism. Through business scenario positioning, related influence path retrieval, intelligent asset matching, candidate strategy generation, strategy effect deduction, and optimal strategy selection, it achieves intelligent decision-making reasoning in complex scenarios. At the same time, it constructs a scenario-driven intelligent agent collaborative logic, in which the three intelligent agents all work collaboratively around a unified business scenario object. Among them, the data intelligent agent is responsible for business scenario identification, business scenario prediction, and scenario change monitoring; the strategy intelligent agent is responsible for scenario understanding, scenario reasoning, strategy generation, and strategy optimization; and the business intelligent agent is responsible for scenario execution, action orchestration, and result feedback. This forms a full-link scenario-driven operation closed loop of "environmental change → scenario identification → scenario reasoning → strategy generation → action execution → effect feedback → scenario update".

[0204] III. Smart Asset Unit

[0205] The intelligent asset unit is not a static rule base, but a dynamic operational capability asset system continuously built upon data, experience, knowledge, and strategy results generated during the long-term operation of an enterprise. It serves as the core capability carrier for the three-element intelligent agent to conduct intelligent perception, strategy reasoning, and business execution. It comprehensively covers seven core asset systems: store operation, merchandise management, best-selling product identification, business district awareness, indicator control, festival operation, and membership operation. All types of assets are uniformly described and organized through an industry semantic layer, and they are interconnected and synergistically empowering each other to form a complete intelligent asset network for catering operations. The intelligent asset unit includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operational indicator assets, holiday operation strategy assets, and membership operation knowledge assets. Among them:

[0206] Store operation SOP assets are continuously recorded by a business intelligence agent, tracking store operational behavior. This behavior includes actions such as inventory preparation, scheduling, food preparation, handling negative reviews, peak-hour operations, and inventory management. A data intelligence agent analyzes these operational behavior tracks to identify differences between high-performing and average stores, revealing the pathways to high performance. High-performing stores are defined by selecting store performance evaluation indicators based on operational metrics assets, such as sales growth rate, average order value, food preparation efficiency, fulfillment time, negative review rate, inventory loss rate, repurchase rate, marketing conversion rate, and labor efficiency. Based on these performance evaluation indicators, stores of the same type are grouped and compared, with stores consistently outperforming the group average being selected. These similar stores include those in the same business district, with the same store size and SKU structure. The data analysis includes behavioral clustering analysis, trajectory analysis, critical path identification, and effect evaluation analysis for stores operating under the same time, weather, and customer flow conditions. Furthermore, the strategy agent abstracts high-performance difference paths into standardized SOPs, thus forming store operation SOP assets, enabling standardized, refined, and intelligent replication of store operation actions. Examples include "completing the preparation of semi-finished cold drinks 30 minutes before the lunch peak," "increasing the safety stock of cold drink ingredients in advance during hot weather," "automatically switching the priority of food preparation when takeout orders surge," and "triggering the store manager's handling task within 10 minutes of a negative review." Subsequently, when other stores experience similar operating scenarios, the business agent can call upon this SOP asset to automatically generate execution tasks or recommend them to the store manager for execution, thereby achieving the replication and large-scale application of excellent store experiences.

[0207] The product management strategy assets are formed by continuously collecting multi-dimensional data such as product sales volume, sales rate, gross profit margin, user reviews, business district and weather characteristics, and user consumption behavior through the data intelligence agent, thus creating a product lifecycle sample library. Based on the product lifecycle sample library, the strategy intelligence agent further constructs a scenario feature library, strategy template library, strategy effect library, applicable condition library, and strategy relationship graph to form product management strategy assets, enabling intelligent management strategy capabilities that adapt to different scenarios and product states.

[0208] The "Hot-Selling Product Recognition Model Asset" utilizes a data intelligence agent to collect multi-dimensional data, including historical sales figures, customer traffic in commercial areas, weather conditions, marketing campaigns, user behavior, and competitor data, to construct training samples for hot-selling products. Based on these training samples, it generates sales prediction models, popularity prediction models, stockout risk models, and hot-selling product probability models. These models collectively form the hot-selling product recognition model asset, providing model support for product hot-selling incubation, pre-emptive inventory management, and precise marketing efforts. Specifically:

[0209] The sales forecasting model is used to predict the sales changes of a certain SKU within a future time window. The system takes historical sales, store type, business district type, weather, holidays, time period, marketing activities, member preferences, and exposure on food delivery platforms as input features and outputs the predicted sales for a future period of time. For example, the system can predict the sales of a certain cold drink SKU in the next 2 hours, on the same day, or in the next 3 days.

[0210] The popularity prediction model is used to determine changes in market attention and consumer willingness for a particular SKU. The system can collect data such as mini-program views, search volume, add-to-cart volume, favorites volume, food delivery platform clicks, review growth, social media content popularity, and activity participation to calculate the product popularity index. If a new product experiences a simultaneous increase in views, add-to-cart volume, and food delivery clicks within a short period of time, the popularity prediction model will increase the popularity score of that SKU.

[0211] The stockout risk model is used to determine whether a certain SKU or its corresponding raw material is at risk of stockout in the future operating cycle. The system takes current inventory, in-transit inventory, raw material consumption rate, predicted sales, replenishment cycle, supplier fulfillment time, and store safety stock as inputs and outputs the probability of stockout risk. For example, if a certain cold drink SKU is expected to sell 300 cups in the next 3 hours, but the store's current key raw material can only support 180 cups, and replenishment will take 4 hours, then the stockout risk model will output a high risk.

[0212] The best-selling product probability model is used to comprehensively determine whether a certain SKU has the potential to become a best-selling product. The best-selling product probability model integrates multi-dimensional data such as sales forecast results, popularity forecast results, stockout risk results, gross profit margin, repurchase rate, user reviews, business district suitability, and marketing pull effect to output the best-selling product probability of the SKU.

[0213] The aforementioned best-selling product probability model does not rely solely on experience to determine whether a particular SKU will become a best-seller. Instead, it is a comprehensive probability judgment model trained using historical operational data, real-time popularity data, product operation data, user feedback data, business district environment data, and marketing execution data. Its construction process includes sample construction, feature construction, best-selling product tag definition, model training, probability calibration, and dynamic updates, as detailed below:

[0214] Sample Construction: The sample unit is "SKU + store + business district + time window". Multidimensional data of SKU is collected to form a training sample of best-selling products. The multidimensional data includes the sales volume, sales growth rate, sales speed, page views, search volume, add-to-cart volume, favorites volume, click volume on food delivery platforms, review growth, repurchase rate, gross profit margin, participation in marketing activities, weather conditions, business district traffic, competitor activities, and inventory guarantee capabilities of the SKU during the historical operating cycle.

[0215] Feature Construction: Based on the training samples of top-selling products, input features for the probability model of top-selling products are constructed, including sales trend features, popularity change features, user feedback features, operating revenue features, scenario adaptation features, channel exposure features, inventory guarantee features, and marketing pull features. Among these, sales trend features include sales growth rate, sales acceleration, and number of consecutive days of growth; popularity change features include browsing growth rate, search growth rate, add-to-cart growth rate, and platform click growth rate; user feedback features include positive review rate, repurchase rate, negative review rate, and user review keywords; operating revenue features include gross profit margin, average order value contribution, and package deal performance; scenario adaptation features include weather adaptability, business district adaptability, time of day adaptability, and holiday adaptability; channel exposure features include traffic coverage and search volatility; inventory guarantee features include safety stock coverage period, out-of-stock rate, and pre-order conversion rate; and marketing pull features include marketing return on investment and content popularity.

[0216] The definition of a "hot-selling product" label: If a SKU meets one or more of the following conditions within the prediction window: sales growth exceeding a preset threshold, sales ranking entering the top of the same category by a preset percentage, user popularity continuously rising, repurchase rate or positive review rate reaching preset conditions, then the sample is marked as a hot-selling product sample; otherwise, it is marked as a non-hot-selling product sample. According to the company's business objectives, it can also be further divided into different labels such as sales explosion hot-selling products, high-margin hot-selling products, new product potential hot-selling products, and regional hot-selling products.

[0217] Model Training: Based on the above-mentioned best-selling product training samples and best-selling product tags, train a best-selling product probability model; the model can be trained using logistic regression, random forest, gradient boosting tree, neural network, or multi-model fusion; the predicted sales output of the sales prediction model, the popularity index output of the popularity prediction model, and the risk probability output of the stockout risk model are used as secondary input features, and together with the product's own features, user behavior features, and business district environment features, are input into the best-selling product probability model, and finally output the probability value of a certain SKU becoming a best-selling product in the future time window;

[0218] Probability calibration and dynamic updates: The probability model for best-selling products is continuously updated based on actual business results. After the business intelligence agent executes strategies such as replenishment, marketing, and adjustment of recommendation positions, the system collects data on actual sales, conversion rates, inventory consumption, user reviews, and changes in gross profit, and feeds this data back to the data intelligence agent and strategy intelligence agent to update training samples, correct feature weights, and optimize model parameters. This enables the probability model for best-selling products to continuously adapt to the formation patterns of best-selling products under different seasons, business districts, stores, and consumption trends.

[0219] If a certain cold drink SKU shows a significant increase in predicted sales, a continuous rise in platform click-through rate, good user reviews, and high gross profit during hot weather, the system will determine that it has a high probability of becoming a best-selling product and trigger strategies such as advance inventory preparation, marketing amplification, and improved recommendation positions.

[0220] For example, during hot weather, a store's "lemon tea" sales are projected to increase by 60%, click-through rate on food delivery platforms by 35%, and add-to-cart volume on mini-programs by 40%. However, if the inventory of key raw materials can only support the product for 3 hours, the system will determine that the SKU has a high probability of becoming a bestseller, but also faces the risk of stockout. Based on this, the strategy agent generates a combination strategy of "replenishing stock in advance + increasing recommendation position + controlling discount intensity + setting alternative SKUs".

[0221] The business district knowledge graph asset is continuously collected by a data intelligence agent from multiple dimensions, including weather information, traffic information, business district customer flow, user profiles, competitor distribution, and food delivery platform data. Based on this multidimensional data, the system forms a business district knowledge graph asset through entity recognition, relationship extraction, knowledge fusion, and graph construction. The graph entities include business districts, stores, user groups, weather, products, activities, and competitors. Through the business district knowledge graph asset, a comprehensive, dynamic, and accurate understanding of the external operating environment of stores can be achieved.

[0222] Operational indicator assets are used to construct a unified quantitative evaluation system for the industry. Based on the industry semantic layer, a unified indicator semantic is defined, which includes core indicators such as average order value, table turnover rate, inventory turnover rate, member activity, marketing conversion rate, fulfillment time, and repurchase rate. Based on the unified indicator semantic, an indicator definition library, indicator calculation logic library, indicator dependency relationship library, and indicator evaluation model library are established simultaneously, thereby forming operational indicator assets. Through operational indicator assets, the calculation, interpretation, and evaluation of operational indicators are unified and intelligently analyzed across the entire domain.

[0223] Holiday operation strategy assets are formed by continuously accumulating historical holiday operation data through data intelligence. Based on effect attribution analysis, causal analysis, and strategy evaluation analysis technologies, four major resource libraries are constructed: pre-holiday inventory, in-holiday marketing library, post-holiday repeat purchase library, and regional holiday operation library. This forms holiday operation strategy assets that are adapted to intelligent operation strategies for different holidays and regions.

[0224] Membership operation knowledge assets are formed by data intelligence agents that build user profiles based on user consumption behavior, product preferences, lifecycle changes, and activity participation. These user profiles are then used to further develop a member tag library, a lifecycle model library, a user preference knowledge library, a recall strategy library, a recommendation strategy library, and a growth path model library. This forms membership operation knowledge assets that support refined and intelligent operations of members throughout their entire lifecycle.

[0225] All intelligent assets are uniformly organized and managed based on the industry semantic layer, forming a global asset network that can be invoked, iterated, and collaborated, providing core capability support for the operation of the ternary intelligent agent.

[0226] IV. Triadic Intelligent Agent

[0227] The data intelligence agent, strategy intelligence agent, and business intelligence agent collaborative system constructed by this invention abandons the traditional working mode of intelligence agents directly processing raw heterogeneous data. It relies on the unified cognition of the industry semantic layer, the supply capacity of intelligent asset units, and the business scenario map to complete reasoning, and realizes the professional division of labor and closed-loop collaboration of business perception, strategy generation, and business execution.

[0228] As a comprehensive operational perception terminal, the data intelligent agent relies on the industry semantic layer to locate operational objects, calls upon various intelligent assets to quantify and analyze operational status, and uses scene graphs to complete scene recognition and dynamic monitoring. The data intelligent agent first locates operational semantic objects based on the operational semantic graph, such as store A, cold drink SKUs, office building business districts, and lunch peak scenarios; then it calls upon operational indicator assets to complete standardized calculations of core indicators such as average order value, table turnover rate, inventory turnover rate, and member activity; it calls upon the best-selling product identification model asset to predict the probability of sales growth, product popularity index, and stockout risk probability; it calls upon the business district knowledge graph asset to capture dynamic changes in the external environment such as weather, customer flow, and competitor activities to obtain the status of various operational objects; it combines the statuses of various operational objects according to a unified structure to form an operational status vector; and it inputs the operational status vector... The system inputs the business scenario graph and matches it with candidate influence paths (selected from among the influence paths). It then calculates a scenario identification score based on node matching degree, path influence strength, and path credibility. When the scenario identification score reaches a preset threshold, the business status under the current candidate influence path is taken as the candidate business scenario. When multiple candidate business scenarios appear (i.e., when the scenario identification scores of multiple candidate influence paths all reach the preset threshold), they are sorted from highest to lowest score. The scenario with the highest score is taken as the current primary scenario, and the other scenarios are taken as concurrent auxiliary scenarios. Finally, a unified business status result is output.

[0229] The scene recognition score is calculated as follows:

[0230] Calculate the node matching degree, path influence strength, and path credibility separately, as follows;

[0231] Node matching degree = number of matched nodes / number of nodes that should be matched in the candidate affected path;

[0232] Path influence intensity = ;

[0233] Path credibility = ;

[0234] Where n represents the total number of relationships in the candidate influence paths. This represents the relational influence strength of the i-th relation in the candidate influence path. This represents the relation weight of the i-th relation in the candidate influence path. This represents the relation credibility of the i-th relation in the candidate influence path;

[0235] Node matching degree is the probability that a state in the real-time business state vector matches a node in the candidate influence path. For example, if the current state vector contains states such as high temperature, increased sales of cold drinks, decreased raw material inventory, and increased risk of stockouts, and the candidate path also contains these nodes, then the node matching degree is high. The path influence strength is obtained by the weighted average of the influence strength of each relation in the path and its corresponding relation weight, which is used to represent the degree of influence of each relation in the path on the final business result. The path credibility is obtained by the weighted average of the credibility of each relation in the path and its corresponding relation weight, which is used to represent the stability of the path in historical samples, real-time verification, and strategy execution feedback.

[0236] The formula for calculating the scene recognition score is:

[0237] Scene recognition score = node matching degree × α + path influence intensity × β + path credibility × γ;

[0238] Where α, β, and γ are weight parameters, representing the importance of node matching degree, path influence strength, and path credibility in scene recognition, respectively, and α+β+γ=1;

[0239] For example, the data intelligence agent calls upon operational indicator assets, best-selling product identification model assets, and business district knowledge graph assets to identify the status of various business objects. It identifies the weather status as high temperature, the business district status as midday peak, the product status as rapid growth in cold drink sales, the inventory status as accelerated raw material consumption, and the fulfillment status as growth in takeout orders. The business object statuses are then combined according to a unified structure to obtain the operational status vector: {Store: A001, Business District: Office Building Business District, Time Period: Midday Peak, Weather: High Temperature, Product Status: Rapid Growth in Cold Drink Sales, Inventory Status: Rapid Decrease in Raw Material Inventory, Channel...} Status: Increased takeout orders; Risk status: Increased risk of stockouts. The operational status vector is input into the operational scenario graph and matched with it. The path "high temperature weather—increased sales of cold drinks—increased raw material consumption—decreased inventory—increased risk of stockouts" is identified. If the scenario identification score calculated based on the path's impact strength and reliability reaches a threshold, a unified operational status result is output as a high-temperature cold drink outbreak scenario. The data intelligence agent identifies complex operational scenarios such as high-temperature sales, peak fulfillment, and inventory alarms, completing real-time perception, scenario prediction, and dynamic monitoring of the entire operational status, providing scenario input for strategy generation.

[0240] As the core hub of intelligent decision-making, the strategy agent is based on the operational status results output by the data agent. It leverages the industry semantic layer to perform scenario semantic analysis, matches the operational scenario graph with associated paths, and invokes multiple intelligent assets to complete strategy reasoning and optimization. Specifically, the strategy agent receives unified operational status results from the data agent. Based on these results, it identifies the current operational scenario through the industry semantic layer and retrieves relevant impact paths from the operational scenario graph. The strategy agent prioritizes impact paths with high intensity and credibility for strategy reasoning. For example, if the current scenario is identified as a high-temperature cold drink outbreak, and the scenario graph reveals relevant impact paths such as high temperature → increased cold drink demand → increased raw material consumption → decreased inventory → increased stockout risk, and these paths have high intensity and credibility, then the system prioritizes strategy reasoning to generate replenishment, allocation, and inventory warning strategies.

[0241] Based on relevant impact paths, the system invokes merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets. Using the scenario matching engine within the strategy agent, it retrieves historically similar operating scenarios and extracts candidate strategies such as replenishment, marketing, membership operation, menu optimization, and delivery operation from the strategy template library of merchandise management strategy assets, forming a candidate strategy set. Then, it invokes the strategy effectiveness library to comprehensively evaluate the candidate strategies from multiple dimensions, including sales increase, inventory health, fulfillment efficiency, user satisfaction, and revenue growth. Finally, a multi-objective optimization model selects the operating strategy with the optimal overall strategy effect (return). The system generates and optimizes intelligent, scenario-based, and precise strategies. The comprehensive evaluation indicators for these strategies include expected sales growth, expected revenue growth, impact on gross profit, marketing costs, inventory health, stockout risk, fulfillment pressure, user satisfaction, repeat purchase increase, and store execution capabilities. The strategy intelligence agent sets different indicator weights according to different operating scenarios. For example, in a high-temperature cold drink surge scenario, the system focuses more on sales growth, inventory health, and stockout risk; in a member reactivation scenario, the system focuses more on repeat purchase rate, discount costs, and user response rate; and in a lunch peak fulfillment scenario, the system focuses more on meal preparation efficiency, fulfillment time, and negative review risk.

[0242] Taking the "high-temperature cold drink outbreak scenario" as an example, the policy agent can generate the following candidate policies:

[0243] Strategy A: Increase the distribution of cold drink coupons.

[0244] Strategy B: Replenish the inventory of cold drink ingredients in advance.

[0245] Strategy C: Increase the recommended placement of cold beverage SKUs on mini-programs and food delivery platforms.

[0246] Strategy D: Temporarily increase peak-hour shifts at stores.

[0247] Strategy E: Set up alternative product recommendations for SKUs with insufficient inventory.

[0248] The strategy agent evaluates the expected effects of candidate strategies. For example, strategy A may lead to higher sales growth, but it will also increase marketing costs and inventory pressure; strategy B can reduce stockout risk, but has limited short-term sales improvement; strategy C can increase exposure and conversion at a lower cost; strategy D can alleviate fulfillment pressure, but increases labor costs; strategy E can reduce order loss caused by stockouts.

[0249] The strategic agent conducts a comprehensive evaluation and, through a multi-objective optimization model, determines the optimal business strategy as follows:

[0250] Replenish cold drink ingredient inventory in advance + increase the recommended position of cold drink SKUs + set up alternative SKU contingency plans;

[0251] This optimal business strategy can capitalize on the sales growth opportunities brought by high temperatures while mitigating the risk of stockouts, and avoid excessive order surges and fulfillment pressures caused by simply increasing discounts. Therefore, the core of the comprehensive evaluation is not optimizing a single indicator, but rather balancing multiple objectives among revenue growth, inventory safety, fulfillment capabilities, user experience, and cost control to generate the most suitable strategy combination for the current business scenario.

[0252] The business intelligence agent, acting as the execution terminal, is responsible for implementing the optimal business strategy generated by the strategy intelligence agent into specific business actions. It relies on the industry semantic layer to parse the business objects corresponding to the strategy and matches them with corresponding intelligent assets to complete standardized execution. The business intelligence agent first receives the optimal business strategy generated by the strategy intelligence agent. Through the industry semantic layer, it parses the business objects within the optimal business strategy and maps these business objects to the business systems to obtain the policy's target objects. For example, the strategy may contain business objects such as target stores, target SKUs, target raw materials, target member groups, target channels, and target activities. The business intelligence agent maps these objects to specific codes and data objects in business systems such as ERP, WMS, CRM, POS, and food delivery platforms.

[0253] Based on the target of the strategy, the business intelligence agent determines the strategy type and calls the corresponding intelligent assets according to the strategy type: when the strategy involves store execution, it calls the store operation SOP asset to determine the store task, execution steps, responsible roles, and execution time limit; when the strategy involves merchandise operation, it calls the merchandise management strategy asset to determine actions such as adjusting recommendation positions, menu item removal and removal, package combinations, and pricing strategies; when the strategy involves member outreach, it calls the member operation knowledge asset to determine the target audience, coupon rules, outreach channels, and outreach content; when the strategy involves inventory replenishment, it calls the operating indicator asset and best-selling product identification model asset to determine the replenishment quantity, safety stock, allocation priority, and stockout risk threshold.

[0254] Standardized execution workflows are generated based on each intelligent asset; for example, in the "high-temperature cold drink outbreak scenario," the business intelligent agent generates the following standardized execution workflow:

[0255] The first step is to check the target store's cold drink SKUs and key ingredient inventory;

[0256] The second step is to calculate the replenishment quantity based on the predicted sales volume and safety stock rules.

[0257] The third step is to create a replenishment or transfer task in the WMS or ERP system.

[0258] The fourth step is to adjust the recommended placement of cold drink SKUs on the mini-program and food delivery platforms;

[0259] The fifth step is to distribute cold drink coupons or push recommended content to the target membership group;

[0260] Step 6: Issue tasks to store managers regarding peak-hour inventory preparation and food preparation priority adjustments;

[0261] Step 7: Continuously collect execution results such as sales volume, inventory, fulfillment time, conversion rate, and user reviews.

[0262] After generating a standardized execution workflow, the method by which the business intelligence agent executes the standardized execution workflow is determined by the path influence strength and path credibility: when the path influence strength exceeds a first preset threshold, it is considered high influence strength, and vice versa; when the path credibility exceeds a second preset threshold, it is considered high credibility, and vice versa; if the optimal business strategy is generated based on a high-influence, high-credibility path, the business intelligence agent will execute it automatically, automatically connecting with various business systems such as ERP, CRM, WMS, and food delivery platforms, and autonomously completing the entire process of business operations such as product replenishment, inventory allocation, marketing activity configuration, coupon distribution, product recommendation, menu adjustment, and personnel scheduling; if the influence strength is high but the credibility is low, the business intelligence agent will implement it on a small scale or submit it for manual confirmation; if the influence strength is low but the credibility is high, the strategy will be used as an auxiliary strategy reference; if both the influence strength and credibility are low, the business intelligence agent will only generate business suggestions and will not directly trigger automatic actions; thus, the strategy is implemented automatically and precisely.

[0263] For example: High impact strength + high credibility: Automatically execute replenishment, menu adjustment, and recommendation promotion.

[0264] High impact intensity + low credibility: Enter gray-scale pilot or manual confirmation.

[0265] Low impact intensity + high credibility: as a reference for auxiliary strategies.

[0266] Low impact strength + low credibility: Only enters observation, does not trigger automatic execution.

[0267] All execution results of the business intelligence agent are fed back to the data intelligence agent and the strategy intelligence agent in real time for subsequent scenario identification, strategy effect evaluation, and asset updates. Therefore, the business intelligence agent does not simply "execute strategies," but breaks down strategies into a complete closed-loop workflow of "object identification—asset invocation—action generation—system integration—task execution—result feedback." The data intelligence agent updates scenario data based on execution results, the strategy intelligence agent iteratively optimizes the strategy effect library and strategy templates, and the business intelligence agent continuously accumulates high-quality execution experience to update SOP assets. Through continuous knowledge extraction, pattern discovery, and experience summarization, the system continuously generates and iterates new intelligent assets, ultimately forming a full-link intelligent operation closed loop of "operational data collection → status perception → scenario identification → strategy generation → automatic execution → effect feedback → asset update → capability enhancement." As the system continues to operate, the semantic system of the industry semantic layer continues to improve, the operational capabilities of intelligent assets continue to accumulate, the inference accuracy of the scenario graph continues to improve, and the collaborative intelligence level of the three-element intelligence agent continues to enhance, ultimately realizing the digital accumulation of operating experience, the normalized evolution of operating capabilities, and the intelligent upgrade of operational decisions for tea and catering enterprises.

[0268] V. Time Scale

[0269] Catering operations are simultaneously affected by multiple factors, including weather changes, fluctuations in customer traffic in commercial areas, traffic distribution from food delivery platforms, changes in user consumption behavior, holiday activities, and supply chain fulfillment status. The change cycles and impact cycles of these different factors vary significantly. Traditional systems primarily rely on daily, weekly, or monthly reports for operational analysis, which cannot meet the rapid response needs of real-time operational scenarios. To resolve the compatibility contradiction between the dynamic operating environment of the catering industry and real-time operational decision-making, this invention's enterprise end-to-end intelligent operation system introduces a multi-timescale operational cognition mechanism. It divides enterprise operations into multiple timescale levels according to response timeliness to address the issue of varying response cycles for different operational events in tea and catering businesses. Each timescale is not an independent module but rather corresponds to the collaborative methods of data intelligence, strategy intelligence, and business intelligence at different time periods. The timescale levels include a real-time response layer, an operational control layer, a strategy optimization layer, and an operational evolution layer.

[0270] In the real-time response layer, the system primarily handles operational events at the second to minute level, such as surges in takeout orders, inventory alerts, abnormal store queues, and negative reviews. At this stage, the data intelligence agent is responsible for real-time monitoring of abnormal data and identifying immediate scenarios; the strategy intelligence agent is responsible for quickly matching emergency strategies; and the business intelligence agent is responsible for immediately executing actions such as inventory locking, menu removal, restocking reminders, handling negative reviews, and store alerts.

[0271] In the operations control layer, the system primarily handles hourly operational events, such as peak-hour inventory adjustments, store staff scheduling, dynamic menu adjustments, and adjustments to the delivery platform's operations. At this stage, the data intelligence agent continuously monitors store operational status, customer traffic changes, and fulfillment pressure; the strategy intelligence agent generates control strategies for inventory, scheduling, menus, and delivery operations; and the business intelligence agent executes actions such as issuing store tasks, adjusting scheduling, adjusting product recommendation placements, and providing inventory reminders.

[0272] In the strategy optimization layer, the system primarily handles daily to weekly operational issues, such as marketing campaign optimization, product mix optimization, business district operation optimization, and membership operation strategy optimization. At this stage, the data intelligence agent is responsible for analyzing operational results and strategy effectiveness over a given period; the strategy intelligence agent is responsible for comparing multiple strategies, attributing effects, and optimizing strategy parameters; and the business intelligence agent is responsible for configuring activities, adjusting product mixes, issuing coupons, and executing membership outreach plans.

[0273] At the operational evolution layer, the system primarily handles the accumulation of operational patterns at the monthly level and above, such as member lifecycle management, store capability growth, regional market pattern accumulation, and SOP optimization. At this stage, the data intelligence agent is responsible for long-term operational data accumulation and pattern identification; the strategy intelligence agent is responsible for accumulating strategy templates, strategy effectiveness libraries, and strategy assets; and the business intelligence agent is responsible for accumulating replicable store operation SOPs and standardized execution processes.

[0274] Therefore, the multi-timescale mechanism essentially coordinates the perception, decision-making, and execution capabilities of the ternary intelligent agent in a hierarchical manner according to different operational response cycles, so that the system can not only handle immediate anomalies, but also support daily operational control, periodic strategy optimization, and long-term operational capability evolution.

[0275] VI. Evolution Module

[0276] The system evolution module of this invention includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel. These flywheels are not independently operating functional modules, but rather form a four-layer capability evolution closed loop deeply coupled with the system's built-in industry semantic layer, intelligent asset units, business scenario graph, and three-element intelligent agent collaborative system. The four flywheels respectively correspond to the iterative upgrades of an enterprise's data perception capability, strategic decision-making capability, business execution capability, and business cognition capability. Each flywheel relies on a unified system architecture to complete data standardization, capability accumulation, scenario reasoning, and closed-loop execution, achieving a full-link, self-driven, and sustainable system evolution effect. Among them:

[0277] The data flywheel serves as the foundational closed loop for iterative development of comprehensive perception capabilities. Starting with data generated by the business intelligence agent during real-world business execution, the agent continuously generates multi-dimensional data such as order data, inventory change data, fulfillment data, member behavior data, marketing conversion data, store execution data, and user feedback data when performing actions like replenishment, allocation, marketing outreach, recommendation adjustments, menu adjustments, store task assignment, and negative review handling. This data is then normalized and semantically standardized through an industry semantic layer, mapping data from different systems such as stores, SKUs, members, inventory, activities, and orders to standardized operational semantic objects. Subsequently, the data intelligence agent performs data cleaning, feature extraction, indicator calculation, anomaly identification, and state modeling based on these standardized operational semantic objects. The processed state data is then input into the operational scenario graph to identify the current operational scenario and update the status of scenario nodes. This forms a data flywheel consisting of "business execution data generation—industry semantic layer unification—data intelligence agent governance and analysis—operational scenario graph status update—more accurate scenario identification."

[0278] The strategy flywheel centers on strategy generation by the strategy intelligence agent and strategy execution feedback by the business intelligence agent. After receiving unified operational status results from the data intelligence agent, the strategy intelligence agent retrieves influence paths based on the operational scenario graph and calls upon intelligent assets such as product operation strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to generate a candidate strategy set. The candidate strategies are evaluated to obtain the optimal strategy. The business intelligence agent breaks down the optimal strategy into specific actions and executes them through business systems such as ERP, WMS, CRM, POS, and food delivery platforms. After execution, the system collects data on sales changes, inventory health, marketing conversion rate, fulfillment efficiency, user satisfaction, gross profit changes, and repeat purchase changes, and feeds this data back to the strategy intelligence agent to update the strategy effect library, strategy template library, applicable condition library, and strategy relationship graph. This forms the strategy flywheel of "scenario identification—influence path retrieval—strategy generation—business execution—effect feedback—strategy asset update."

[0279] The SOP flywheel, directly corresponding to store operation SOP assets, is at its core to transforming excellent stores, excellent store managers, or high-performing operational actions into reusable standard execution assets. The business intelligence agent continuously records the actual operational trajectories of daily store operations, such as inventory preparation, scheduling, food preparation, handling negative reviews, inventory management, takeout order taking, and adjustment of recommended positions. Based on these operational trajectories, the data intelligence agent evaluates store performance according to operational indicator assets, identifies behavioral differences between high-performing stores and ordinary stores, and obtains the high-performance difference paths. The strategy intelligence agent further abstracts these high-performance difference paths into standardized SOPs, forming store operation SOP assets. Subsequently, when other stores encounter similar operational scenarios, the data intelligence agent identifies the scenario, the strategy intelligence agent matches the SOP assets, and the business intelligence agent automatically generates store tasks and drives their execution. This forms the SOP flywheel of "store execution behavior recording—performance difference identification—high-performance path abstraction—SOP asset accumulation—similar scenario reuse—execution effect feedback—continuous SOP optimization."

[0280] The Business Knowledge Flywheel, with its core carriers of an industry semantic layer and a business scenario graph, is used to accumulate object knowledge, relationship knowledge, scenario knowledge, and strategy knowledge formed during the long-term operation of an enterprise. The system uses the industry semantic layer to uniformly describe business objects such as stores, SKUs, members, business districts, channels, activities, inventory, and suppliers, and uses the business semantic graph to accumulate the basic relationships between these objects. Simultaneously, the business scenario graph accumulates event nodes, state nodes, strategy nodes, and dynamic impact paths, recording the scenario-based impact patterns between different business factors, thus forming business knowledge. For example, the system can gradually accumulate business knowledge such as "high temperatures easily drive sales growth of cold drink SKUs," "office building business districts experience higher fulfillment pressure during lunchtime," "heavy rain increases takeout orders but decreases in-store traffic," and "when a certain SKU is out of stock, users are more likely to choose alternative SKUs." This experiential knowledge feeds back into the state recognition of the data intelligence agent, the path reasoning of the strategy intelligence agent, and the action execution of the business intelligence agent, thereby forming a business knowledge flywheel of "unified business objects—accumulated business relationships—formed scenario paths—validated strategy execution—knowledge updates—enhanced intelligence agent capabilities."

[0281] Therefore, the four types of flywheels jointly achieve unified cognition through the industry semantic layer, accumulate capabilities through intelligent asset units, achieve dynamic reasoning through the business scenario map, and achieve closed-loop operation through data intelligence, strategy intelligence, and business intelligence, ultimately forming an enterprise-wide intelligent operation evolution mechanism of "continuous data enhancement, continuous scenario correction, continuous strategy optimization, continuous execution accumulation, and continuous knowledge evolution".

[0282] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A smart operation system for the entire supply chain of tea beverage and catering enterprises, characterized in that, It includes data intelligence agents, strategy intelligence agents, business intelligence agents, intelligent asset units, industry semantic layers, business scenario graphs, and evolutionary modules; among which: The industry semantic layer is used to uniformly extract, normalize, and construct semantic relationships for the business semantic objects in the tea and catering industry, forming a business semantic graph. The business scenario graph identifies business entities through a business semantic graph and constructs scenario graph nodes. Based on the scenario graph nodes, it constructs the association relationships between the scenario graph nodes and configures structured parameters for the association relationships, including the relationship influence strength and relationship credibility. The intelligent asset unit is used to store the data, experience, knowledge, and strategic results generated in the long-term operation of the tea and catering industry, and to convert them into digital assets. Specifically, it includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operating indicator assets, holiday operation strategy assets, and membership operation knowledge assets. The data intelligence agent, based on the business semantic graph, calls upon business indicator assets, best-selling product identification model assets, and business district knowledge graph assets to identify the business status vectors of various business objects in the tea and catering industries. The business status vectors are input into the business scenario graph and matched with candidate influence paths in the graph. A scenario identification score is calculated based on node matching degree, path influence strength, and path credibility. When the scenario identification score reaches a threshold, a unified business status result is output. The path influence strength and path credibility are calculated using relation influence strength and relation credibility, respectively. The strategy intelligence agent identifies the current business scenario based on the unified business status results, retrieves relevant influence paths of the current business scenario through the business scenario graph, and, based on the relevant influence paths, calls upon merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to screen candidate strategies; and performs a comprehensive evaluation of the strategy effectiveness of the candidate strategies to generate the optimal business strategy. The business intelligence agent analyzes the optimal business strategy and determines the strategy type. Based on the strategy type, it calls the corresponding store operation SOP assets, merchandise operation strategy assets, membership operation knowledge assets, or business indicator assets and best-selling product identification model assets to generate a standardized execution workflow. After determining how the business intelligence agent executes the standardized execution workflow based on the path impact strength and path credibility, the business intelligence agent issues the execution. The evolution module includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel. It relies on the industry semantic layer to unify data semantics, the intelligent asset unit to accumulate operational capabilities, and the business scenario graph to complete reasoning and the closed-loop linkage of a three-element intelligent agent system consisting of data intelligent agents, strategy intelligent agents, and business intelligent agents.

2. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The data intelligence agent locates operational semantic objects based on an operational semantic graph. These operational semantic objects include product objects, SKU objects, formula objects, raw material objects, store objects, member objects, business district objects, channel objects, activity objects, order objects, inventory objects, and supplier objects. Subsequently, it calls operational indicator assets to complete standardized calculations of operational indicators, including average order value, table turnover rate, inventory turnover rate, and member activity. It also calls popular product identification model assets to predict the probability of sales growth, product popularity index, and probability of stockout risk. The system utilizes business district knowledge graph assets to capture dynamic changes in the external environment, ultimately obtaining the status of various business objects. These statuses are then combined according to a unified structure to form a business status vector. This vector is input into the business scenario graph and matched with candidate influence paths within the graph. A scenario identification score is calculated based on node matching degree, path influence strength, and path credibility. When the scenario identification score reaches a preset threshold, the business status under the current candidate influence path is selected as a candidate business scenario. When at least two candidate business scenarios appear, they are sorted from high to low according to their scenario recognition scores. The candidate business scenario with the highest score is taken as the current main scenario, and the other candidate business scenarios are taken as concurrent auxiliary scenarios. Finally, a unified business status result is output. The scene recognition score is calculated as follows: Calculate node matching degree, path influence strength, and path credibility separately, as follows: Node matching degree = number of matched nodes / number of nodes that should be matched in the candidate affected path; Path influence intensity = ; Path credibility = ; Where n represents the total number of relationships in the candidate influence paths. This represents the relational influence strength of the i-th relation in the candidate influence path. This represents the relation weight of the i-th relation in the candidate influence path. This represents the relation credibility of the i-th relation in the candidate influence path; The formula for calculating the scene recognition score is: Scene recognition score = node matching degree × α + path influence intensity × β + path credibility × γ; Where α, β, and γ are weight parameters, representing the importance of node matching degree, path influence strength, and path credibility in scene recognition, respectively, and α+β+γ=1.

3. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The strategy agent receives the unified business status result output by the data agent, and identifies the current business scenario based on the unified business status result through the industry semantic layer; The business scenario graph is used to retrieve relevant impact paths for the current business scenario; the policy agent prioritizes impact paths whose impact strength and credibility exceed a preset threshold for policy reasoning. Based on relevant influence paths, the system invokes merchandise management strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets. Using a scenario matching engine within the strategy agent, it retrieves historically similar operating scenarios and extracts candidate strategies from the strategy template library of merchandise management strategy assets. These candidate strategies include replenishment strategies, marketing strategies, membership operation strategies, menu optimization strategies, and takeout operation strategies. Then, it invokes a strategy effectiveness library to comprehensively evaluate the candidate strategies in terms of sales increase, inventory health, fulfillment efficiency, user satisfaction, and revenue growth. A multi-objective optimization model is used to select the operating strategy with the best overall strategy effect. The indicators for the comprehensive evaluation of strategy effectiveness include expected sales increase, expected revenue growth, gross profit impact, marketing costs, inventory health, stockout risk, fulfillment pressure, user satisfaction, repeat purchase increase, and store execution capability. The strategy agent sets different indicator weights for the comprehensive evaluation indicators of strategy effectiveness according to different operating scenarios.

4. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The business intelligence agent receives the optimal business strategy generated by the strategy intelligence agent, parses the business objects in the optimal business strategy through the industry semantic layer, maps the business objects to the business system, and obtains the strategy action object. Based on the target of the strategy, the business intelligence agent determines the strategy type and calls the corresponding intelligent assets to generate a standardized execution workflow. When the strategy involves store execution, it calls the store operation SOP asset to determine the store tasks, execution steps, responsible roles, and execution time limits. When the strategy involves merchandise operation, it calls the merchandise management strategy asset to determine the relevant operational actions such as adjusting recommendation positions, menu item removal and removal, package combinations, and pricing strategies. When the strategy involves member outreach, it calls the member operation knowledge asset to determine the target audience, coupon rules, outreach channels, and outreach content. When the strategy involves inventory replenishment, it calls the operational indicator asset and the best-selling product identification model asset to determine the replenishment quantity, safety stock, allocation priority, and stockout risk threshold. After generating a standardized execution workflow, the method by which the business intelligence agent executes the standardized execution workflow is determined by the path impact strength and path credibility of the optimal business strategy: when the path impact strength exceeds a first preset threshold, it is considered high impact strength, and vice versa; when the path credibility exceeds a second preset threshold, it is considered high credibility, and vice versa; if both impact strength and credibility are high, the business intelligence agent executes automatically directly; if the impact strength is high but the credibility is low, the business intelligence agent uses small-scale gray-scale execution or submits for manual confirmation; if the impact strength is low but the credibility is high, the current strategy is used as an auxiliary strategy reference; if both impact strength and credibility are low, the business intelligence agent only generates business suggestions and does not directly trigger automatic actions.

5. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The industry semantic layer construction process includes four stages: semantic object extraction, semantic object normalization, semantic relationship construction, and business semantic graph generation, as detailed below: In the semantic object extraction stage, the entire enterprise's internal and external operational data is integrated, covering POS sales data, CRM member data, ERP product data, WMS inventory data, takeaway order data, third-party weather and business district customer flow data, and full-domain marketing activity data. Relying on the object recognition engine of the data intelligence agent, twelve core operational semantic objects are extracted for the tea and catering industry, and a standardized and structured unified attribute set is constructed for each type of operational semantic object. The twelve core operational semantic objects include product objects, SKU objects, formula objects, raw material objects, store objects, member objects, business district objects, channel objects, activity objects, order objects, inventory objects, and supplier objects. In the semantic object normalization stage, addressing the common issues of homonymous entities, different codes, and different representations in the tea and catering industries, the business semantic objects are uniformly mapped through attribute matching, code mapping, and association analysis. This completes the semantic alignment and unified identification of cross-system entities, resulting in normalized business semantic objects. Based on the normalized business semantic objects, a unified business semantic object system is constructed, including unified object identifiers, object types, object names, and object attribute sets. In the semantic relationship construction phase, based on the normalized business semantic objects, and combined with the business rules of the tea and catering industries, historical operating data, and object extraction results, business relationships between business semantic objects are identified, and business semantic relationships are constructed. These business semantic relationships include source objects, relationship types, target objects, and relationship weights. The source object represents the initiating entity of the business semantic relationship; the target object represents the entity that influences the business semantic relationship; the relationship type describes the business association between two business semantic objects; and the relationship weight represents the influence coefficient of the business semantic relationship on the operating results. In the business semantic graph generation stage, a business semantic graph is constructed based on business semantic objects and business semantic relationships; the business semantic graph is constructed with normalized business semantic objects as nodes and business semantic relationships as edges.

6. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The specific construction of the business scenario map is as follows: Business entities are identified based on the business semantic graph, and scene graph nodes are constructed; the scene graph nodes include environment nodes, user nodes, product nodes, store nodes, business status nodes, and business strategy nodes; Based on scene graph nodes, the association relationships between scene graph nodes are constructed, and six structured parameters are configured for each association relationship, including source node, target node, relationship type, relationship influence strength, relationship credibility, and relationship weight; the association relationships include influence relationships, dependency relationships, transformation relationships, substitution relationships, and collaborative relationships. The credibility of the relationship is obtained by evaluating the credibility of each business relationship based on the sample size of historical business data, the frequency of relationship occurrence, the stability of the relationship, the proportion of negative examples of the relationship, and the verification results of the relationship strategy. The strength of the relationship's influence is calculated using historical data, real-time data, and execution result data, as follows: Relationship Influence Strength = Base Influence Value × Real-time Correction Value × Feedback Correction Value × Scenario Correction Coefficient; Among them, the basic impact value represents the long-term average impact of the relationship in historical operating data; the real-time correction value represents the degree to which the current real-time operating status strengthens or weakens the long-term pattern; the feedback correction value represents the degree to which the historical strategy execution results verify the effectiveness of the relationship; and the scenario correction coefficient is used to represent the corrective effect of relevant scenario conditions on the relationship. Event nodes are constructed based on the relationships between scene graph nodes. These event nodes are categorized into external environment events, platform traffic events, marketing campaign events, supply chain events, store operation events, and user feedback events. Each event node is structured with an event identifier, event type, event source, occurrence time, duration, associated objects, event intensity, and event status. The associated objects describe the stores, SKUs, member groups, business districts, channels, or inventory affected by the event. The event status indicates whether the event has occurred, is in progress, or has ended. In the scenario graph, event nodes are connected to operational status nodes, product nodes, store nodes, and operational strategy nodes through influence relationships, trigger relationships, and conversion relationships; Based on all nodes and relationships in the scenario graph, an influence path is constructed. This influence path includes a source node, intermediate nodes, result nodes, a set of path relationships, path influence strength, path credibility, and path applicability conditions. The source node represents the initial factor influencing the scenario change; intermediate nodes represent changes in business status during the influence transmission process; result nodes represent the final business or risk outcome; the path relationship set records the influence, dependency, transformation, or triggering relationships between adjacent nodes in the influence path; the path influence strength represents the degree of influence of the entire influence path on the final result; the path credibility represents the stability of the influence path in historical samples, real-time verification, and strategy execution feedback; and the path applicability conditions limit the types of stores, business districts, time periods, weather conditions, SKU types, or member groups to which the influence path is applicable.

7. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 6, characterized in that, The basic impact value is obtained by statistically analyzing the change range of the target node or target operating indicator before and after the change of the source node status from historical data according to the relationship type, and then standardizing the data. The real-time correction value is calculated based on the deviation between the current relevant real-time indicator and the historical benchmark value, and the specific calculation is as follows: Real-time correction value = 1 + Real-time deviation from overall value; Among them, the real-time deviation composite value is obtained by weighting the degree of deviation of the current real-time indicator relative to the historical benchmark indicator; The feedback correction value is calculated based on the strategy execution hit rate and effect deviation, as follows: Feedback correction value = 1 + execution hit rate correction value - effect deviation penalty value; Among them, the execution hit rate adjustment value is the ratio of the number of times the actual business results meet expectations after the relationship generation strategy is implemented to the total number of executions based on the relationship generation strategy; The effect deviation penalty value is the average degree of deviation between the actual result and the predicted result, and it is calculated as follows: Performance deviation penalty value = |Actual sales growth rate - Predicted sales growth rate| / Predicted sales growth rate; The scene correction coefficient is obtained by combining several scene sub-coefficients, and the specific calculation is as follows: Scene correction factor = Season correction factor × Business district correction factor × Store type correction factor × Time period correction factor × Holiday correction factor; Among them, the seasonal adjustment coefficient is determined by the historical performance of the relationship in different seasons; the business district adjustment coefficient is determined by the historical performance of the relationship in different business district types; the store type adjustment coefficient is determined by different store types, store area, food preparation capacity and SKU structure; the time period adjustment coefficient is determined by the historical performance of the relationship in different time periods; and the holiday adjustment coefficient is determined by the historical change pattern of the relationship before and after holidays.

8. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, The intelligent asset unit includes store operation SOP assets, merchandise management strategy assets, best-selling product identification model assets, business district knowledge graph assets, operating indicator assets, holiday operation strategy assets, and membership operation knowledge assets; among which: Store operation SOP assets are continuously recorded by the business intelligence agent, which tracks store operational behavior, including actions such as stocking, scheduling, food preparation, handling negative reviews, peak-hour operations, and inventory management. The data intelligence agent analyzes these operational behavior tracks to identify differences between high-performing and average stores, identifying high-performance difference paths. The strategy intelligence agent abstracts these high-performance difference paths into standardized SOPs, thus forming the store operation SOP assets. High-performing stores are those whose performance evaluation indicators are selected based on the operational indicator assets. Based on these indicators, similar stores are grouped and compared to identify those whose overall performance consistently outperforms the group average. These similar stores include those in the same business district, with the same store size, SKU structure, operating hours, weather conditions, and customer traffic. The product management strategy assets are formed by continuously collecting multi-dimensional data, including product sales, sales rate, gross profit margin, user reviews, business district and weather characteristics, and user consumption behavior, through the data intelligence agent. Based on the product lifecycle sample library, the strategy intelligence agent further constructs a scenario feature library, a strategy template library, a strategy effect library, an applicable condition library, and a strategy relationship graph to form the product management strategy assets. The hot-selling product identification model asset is constructed by collecting multi-dimensional data, including historical sales, business district traffic, weather, marketing activities, user behavior, and competitors, through a data intelligence agent. Based on the hot-selling product training samples, a sales prediction model, a popularity prediction model, a stockout risk model, and a hot-selling product probability model are generated. The hot-selling product identification model asset is formed by the sales prediction model, popularity prediction model, stockout risk model, and hot-selling product probability model. The business district knowledge graph asset is formed by the continuous collection of multi-dimensional data by a data intelligence agent, including weather information, traffic information, business district customer flow, user profiles, competitor distribution, and food delivery platform data. Based on the multi-dimensional data, the business district knowledge graph asset is formed through entity recognition, relationship extraction, knowledge fusion, and graph construction. Operational indicator assets are constructed by building a unified quantitative evaluation system for the industry. Based on the industry semantic layer, a unified indicator semantic is defined, which specifically includes average order value, table turnover rate, inventory turnover rate, member activity, marketing conversion rate, fulfillment time, and repurchase rate. Based on the unified indicator semantic, an indicator definition library, an indicator calculation logic library, an indicator dependency relationship library, and an indicator evaluation model library are established simultaneously, thereby forming operational indicator assets. Holiday operation strategy assets are formed by continuously accumulating historical festival operation data through data intelligence agents and constructing four major resource databases based on effect attribution analysis, causal analysis, and strategy evaluation analysis techniques: pre-festival inventory, in-festival marketing database, post-festival repurchase database, and regional festival operation database. Membership operation knowledge assets are formed by data intelligence agents building user profiles based on user consumption behavior, product preferences, lifecycle changes, and activity participation. These user profiles are then used to further develop a member tag library, a lifecycle model library, a user preference knowledge base, a recall strategy library, a recommendation strategy library, and a growth path model library, thus creating membership operation knowledge assets.

9. The intelligent operation system for the entire supply chain of tea beverage and catering enterprises according to claim 1, characterized in that, Based on the collaborative methods of data intelligence agents, strategy intelligence agents, and business intelligence agents at different time periods, enterprise operations are divided into time-scale levels according to response timeliness. These time-scale levels include a real-time response layer, an operational control layer, a strategy optimization layer, and a business evolution layer; wherein: The real-time response layer handles operational events ranging from seconds to minutes; the data intelligence agent is responsible for real-time monitoring of abnormal data and identifying immediate scenarios; the strategy intelligence agent is responsible for quickly matching emergency strategies; and the business intelligence agent is responsible for immediately executing actions related to inventory locking, menu removal, replenishment reminders, handling negative reviews, and store alarms. The operations control layer handles hourly operational events; the data intelligence agent is responsible for continuously monitoring store operating status, customer flow changes, and fulfillment pressure; the strategy intelligence agent is responsible for generating control strategies, including inventory preparation, scheduling, menu, and takeout operations; and the business intelligence agent is responsible for executing actions such as issuing store tasks, adjusting scheduling, adjusting product recommendation positions, and reminding customers to prepare inventory. The strategy optimization layer handles daily to weekly operational issues; the data intelligence agent is responsible for analyzing operational results and strategy effectiveness over a period of time; the strategy intelligence agent is responsible for comparing multiple strategies, attributing effects, and optimizing strategy parameters; and the business intelligence agent is responsible for configuring activities, adjusting product mixes, issuing coupons, and executing member outreach plans. The operational evolution layer handles the accumulation of operational patterns at the monthly level and above; the data intelligence agent is responsible for the accumulation of long-term operational data and pattern identification; the strategy intelligence agent is responsible for accumulating strategy templates, strategy effect libraries, and strategy assets; and the business intelligence agent is responsible for accumulating replicable store operation SOPs and standardized execution processes.

10. The intelligent operation system for the entire supply chain of tea beverages and catering enterprises according to claim 1, characterized in that, The evolution module includes a data flywheel, a strategy flywheel, a SOP flywheel, and a business knowledge flywheel; wherein: The data flywheel starts with the data generated by the business intelligence agent during real business execution. During execution, the business intelligence agent continuously generates multi-dimensional data. This multi-dimensional data is then normalized and semantically standardized through an industry semantic layer, mapping store, SKU, member, inventory, activity, and order data from different systems into standard operational semantic objects. Based on these standard operational semantic objects, the data intelligence agent performs data cleaning, feature extraction, indicator calculation, anomaly identification, and state modeling. The processed data is then input into the operational scenario graph to identify the current operational scenario and update the status of scenario nodes. The strategy flywheel consists of a strategy agent that receives unified operational status results from the data agent, retrieves influencing paths based on the operational scenario graph, and calls upon intelligent assets including merchandise operation strategy assets, membership operation knowledge assets, holiday operation strategy assets, and store operation SOP assets to generate a candidate strategy set. The candidate strategies are evaluated to obtain the optimal strategy. The business agent breaks down the optimal strategy into specific actions and executes them through various business systems. After execution, the result data is collected and fed back to the strategy agent to update the strategy effect library, strategy template library, applicable condition library, and strategy relationship graph. The result data includes changes in sales volume, inventory health, marketing conversion rate, fulfillment efficiency, user satisfaction, gross profit changes, and repeat purchase changes. The SOP flywheel involves the business intelligence agent continuously recording actual operational trajectories during daily store operations. Based on these trajectories, the data intelligence agent evaluates store performance using operational metrics assets, identifying behavioral differences between high-performing and average stores to derive high-performance difference paths. The strategy intelligence agent further abstracts these high-performance difference paths into standardized SOPs, forming store operation SOP assets. Subsequently, when similar operational scenarios occur in other stores, the data intelligence agent identifies the scenario, the strategy intelligence agent matches the SOP assets, and the business intelligence agent automatically generates store tasks and drives their execution. The business knowledge flywheel uses an industry semantic layer to uniformly describe business objects and uses a business semantic graph to accumulate the basic relationships between business objects. At the same time, it uses a business scenario graph to accumulate event nodes, business status nodes, business strategy nodes and influence paths, record the scenario-based influence patterns between different business factors, form business knowledge, and feed back into the state recognition of data intelligence agents, the path reasoning of strategy intelligence agents and the action execution of business intelligence agents.

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