A store recommendation management method, device, medium and product

Abstract

A store recommendation management method and device, medium and product are provided, which relate to the field of data processing. In the method, a user submits a recommendation officer identity authentication request through a user terminal device; if it is determined that an audit pass instruction of a management terminal of a target store is received, the user is marked as a recommendation officer identity, and the type of the recommendation officer identity and the corresponding commission rules are determined; a user with a recommendation officer identity initiates a guest reservation request for the target store; recommendation room inventory data of the target store is obtained, and the guest reservation request is processed according to the recommendation room inventory data to generate a reservation order; when it is detected that the consumption state of the reservation order is changed to consumption success, the recommendation commission is calculated based on the commission rules and the actual order amount of the reservation order; a payment account bound to the recommendation officer identity is obtained, and the recommendation commission is paid to the payment account. The technical solution provided by the present application improves the store operation efficiency.

Description

Technical Field

[0001] This application relates to the field of data processing, specifically to a store recommendation management method, device, medium, and product. Background Technology

[0002] With the rapid development of mobile internet technology and the increasing diversification of offline consumption scenarios, the marketing model of physical stores is undergoing profound changes. Advertising methods face the problems of high costs and low conversion rates, while word-of-mouth marketing based on social relationships is gradually becoming an important way for stores to acquire customers.

[0003] Currently, although many stores have launched similar referral rebate programs, numerous technical challenges remain in actual management. Firstly, the management of referrer identities is chaotic, often relying on manual records or simple verbal agreements, lacking a systematic identity verification and tiered management mechanism, making it difficult to accurately match promotional benefits. Commission calculation and disbursement are typically delayed and rely on manual verification, making it difficult to correlate with the actual consumption status of orders in real time (e.g., whether they have been redeemed, whether refunds have been issued, etc.). This not only increases the error rate in financial statistics but also prevents automated revenue sharing based on precise rules, severely impacting store operational efficiency. Summary of the Invention

[0004] This application provides a store recommendation management method, device, medium, and product that improves store operational efficiency.

[0005] A first aspect of this application provides a store referral management method, the method comprising: obtaining a referral officer identity authentication request submitted by a user through a user terminal device, the referral officer identity authentication request including user information and a target store to be authenticated; if it is determined that the management terminal of the target store has received an approval instruction for the referral officer identity authentication request, then marking the user as a referral officer, and determining the referral officer type and corresponding commission rule of the referral officer identity; receiving a booking request for the target store initiated by a user with the referral officer identity; obtaining the referral control room inventory data of the target store, and processing the booking request for the booking based on the referral control room inventory data to generate a booking order; when it is detected that the consumption status of the booking order has changed to successful consumption, calculating a referral commission based on the commission rule and the actual order amount of the booking order; obtaining the receiving account bound to the referral officer identity, and disbursing the referral commission to the receiving account.

[0006] By adopting the above technical solution, the system obtains referral agent identity authentication requests submitted by user-end devices, achieving standardized management of referral personnel identities and ensuring that each referral agent is verified by the target store, thus improving the credibility of the referral channel. Marking users as referral agents and determining their types and commission rules establishes a differentiated incentive mechanism, matching corresponding benefits based on the actual capabilities of the referral agents. Receiving and processing referral agent-initiated booking requests and acquiring and controlling store inventory data enables precise management of exclusive inventory for the referral channel, avoiding inventory conflicts with direct sales channels. When a booking order is successfully fulfilled, the referral commission is calculated based on the commission rules, ensuring that the referral agent's labor value is reflected in a timely manner. The referral commission is directly paid to the referral agent's linked payment account, forming a complete business loop and enhancing the referral agent's work enthusiasm and loyalty. The automated processing of the entire process reduces manual intervention and improves the accuracy and timeliness of commission settlement. This technical solution, by constructing a systematic store referral management method, achieves closed-loop management of the entire process from referral agent identity authentication, commission rule determination, booking processing, order consumption status tracking to commission calculation and automatic payment. This solution effectively addresses issues such as chaotic management of referral personnel identities, delayed commission settlement and disbursement, and a lack of automation in the revenue sharing process. It standardizes and automates the referral process, significantly improving store operational efficiency and promotion management accuracy. At the same time, it enhances the enthusiasm and trust of referral officers, thereby driving stores to acquire customers and increase revenue at low cost and high efficiency.

[0007] Optionally, determining the category of the referrer's identity and the corresponding commission rules specifically includes: parsing the user's historical referral data contained in the identity authentication request, wherein the user's historical referral data includes historical referral frequency, historical referral success rate, and historical customer satisfaction score; calculating the referrer's reputation score based on the user's historical referral data using a preset referrer type evaluation model; and determining the referrer type and the commission rules based on the referrer's reputation score.

[0008] By employing the aforementioned technical solution, historical user recommendation data in identity authentication requests is analyzed, including historical recommendation frequency, success rate, and customer satisfaction ratings, enabling a comprehensive quantitative evaluation of the recommender's historical performance. A pre-defined recommender type evaluation model calculates the recommender's reputation score, transforming multi-dimensional historical data into a unified reputation score, making the recommender's capability assessment more scientific and objective. Based on the reputation score, recommender types and commission rules are determined, establishing a dynamic tiered mechanism based on actual performance. Excellent recommenders can obtain higher levels and more generous commissions, creating a healthy competitive and incentive environment. This historical data-based evaluation method effectively filters out truly capable and resourceful recommenders, improving the overall quality of the recommendation channel while also reducing cooperation risks for stores.

[0009] Optionally, determining the referrer type and commission rule based on the referrer's reputation score specifically includes: if the referrer's reputation score is greater than or equal to a preset score threshold, then the referrer type is determined to be an exclusive referrer; if the exclusive referrer is determined to be a peer referrer, then a peer commission rule is assigned to the exclusive referrer, where the peer referrer is a referrer belonging to the same industry as the target store; if the exclusive referrer is determined to be a cross-industry referrer, then a cross-industry commission rule is assigned to the exclusive referrer, where the cross-industry referrer is a referrer not belonging to the same industry as the target store; if the referrer's reputation score is determined to be less than the preset score threshold, then the referrer type is determined to be a regular referrer, and a corresponding regular commission rule is assigned to the regular referrer.

[0010] By adopting the above technical solution, referrers are categorized into dedicated referrers and regular referrers based on preset score thresholds, achieving refined tiered management of the referrer group. Dedicated referrers are further differentiated into those from the same industry and those from different industries, reflecting a differentiated understanding of referrers with different industry backgrounds. Referrers from the same industry receive higher commission rates due to their industry experience and customer resources, while referrers from different industries enjoy corresponding commission rates. Regular referrers are assigned standard commission rates, ensuring that referrers at all levels have corresponding incentive policies. This multi-level, multi-dimensional classification system protects the interests of high-performing referrers, motivating them to continuously bring high-quality customers to stores, while providing growth opportunities and basic guarantees for newly joined or average-performing referrers, forming a complete referrer ecosystem.

[0011] Optionally, the step of processing the booking request on behalf of the customer and generating a booking order based on the recommended room inventory data specifically includes: extracting the booking time period and room type requirements from the booking request on behalf of the customer; determining the remaining available quota corresponding to the booking time period and room type requirements in the recommended room inventory data; comparing the remaining available quota with the booking quantity in the booking request on behalf of the customer; if the remaining available quota is greater than or equal to the booking quantity, then performing an inventory locking operation, deducting the recommended room inventory corresponding to the booking quantity, and generating a booking order with a confirmed status; if the remaining available quota is less than the booking quantity, then generating a booking order with a pending status, and pushing the booking order to the management terminal of the target store.

[0012] By adopting the above technical solution, the system extracts the booking time period and room type requirements from customer booking requests and determines the remaining available quota in the recommended room inventory data, achieving a precise match between booking demand and available resources. Comparing the remaining available quota with the booking quantity ensures the rationality and accuracy of inventory allocation. When the quota is sufficient, an inventory lock operation is performed and inventory is deducted, generating a confirmed booking order. This ensures that both the recommender and the customer receive immediate booking confirmation, improving the service experience. When the quota is insufficient, pending orders are generated and pushed to the store management terminal, giving stores the opportunity to flexibly allocate resources, protecting the interests of recommenders while also considering the operational flexibility of the stores. This intelligent inventory management mechanism effectively avoids overselling while maximizing the utilization efficiency of room resources.

[0013] Optionally, before obtaining the recommended room inventory data of the target store, the method further includes: obtaining all room status information synchronized by the management terminal of the target store; initializing the recommended room inventory data according to the basic recommended room quota of the target store, and assigning a quota number to each recommended quota; creating a quota mapping relationship, mapping each recommended quota to a specific room in the all room status information through the quota number; and updating the availability status of the corresponding recommended quota in the recommended room inventory data when the all room status information changes.

[0014] By adopting the above technical solution, all room status information synchronized with the target store management terminal is obtained, achieving real-time integration between the recommendation system and the actual operational data of the store. Inventory data is initialized based on the basic quota for recommended rooms, and quota numbers are assigned, establishing a structured recommendation-specific inventory management system. A quota mapping relationship is created to associate recommendation quotas with specific rooms, realizing a dynamic association between virtual quotas and physical resources, improving the flexibility of resource allocation. When the room status changes, the availability status of recommendation quotas is automatically updated, ensuring the real-time nature and accuracy of recommendation inventory information. This dynamic mapping mechanism avoids the resource waste caused by fixed reserved rooms, ensuring that the recommendation channel has dedicated protection without affecting the overall operational efficiency of the store, achieving coordinated development between the recommendation business and the store's main business.

[0015] Optionally, after obtaining the payment account bound to the referrer's identity and distributing the referral commission to the payment account, the method further includes: extracting the customer identity identifier from the reservation order, establishing a binding relationship between the customer identity identifier and the referrer's identity, and recording the binding timestamp; monitoring the transaction flow of the target store's POS system, and when a new consumption record containing the customer identity identifier is obtained, determining whether the new consumption record is associated with a new referrer's reservation request; if the new consumption record is not associated with a new referrer's reservation request, determining it as a customer's independent consumption behavior; based on the binding relationship, calling the commission rules, calculating the welfare commission based on the actual payment amount of the new consumption record, and distributing the welfare commission to the payment account of the referrer's identity.

[0016] By employing the aforementioned technical solutions, customer identification is extracted from pre-orders and a binding relationship is established with referrers, enabling long-term tracking and management of customer resources. Recording the binding timestamp provides an accurate basis for subsequent relationship validity assessment. Monitoring the POS system's transaction flow and identifying new customer spending records automates the tracking of subsequent customer spending behavior. Determining whether new spending is associated with new pre-order requests accurately distinguishes between referral spending and self-generated spending. For self-generated spending, bonus commissions are calculated based on the binding relationship and distributed to the referrer's account, creating continuous passive income for the referrer. This mechanism incentivizes referrers not only to acquire new customers but also to maintain good customer relationships and promote repeat purchases, forming a virtuous cycle of win-win for referrers, customers, and stores.

[0017] Optionally, the method further includes: detecting whether the referrer is an exclusive referrer; if the referrer is an exclusive referrer, generating a team invitation QR code for the exclusive referrer, the team invitation QR code including the team identifier information of the target team and the identity information of the inviter; when other referrers are detected scanning the team invitation QR code, establishing a team member relationship mapping and adding the other referrers to the target team; calculating the total order amount of the team members of the target team, and calculating additional team commission for each team member in the target team according to a preset team commission ratio.

[0018] By employing the aforementioned technical solutions, the system verifies whether a referrer is a dedicated referrer, ensuring that only referrers at a certain level can enjoy team management privileges, thus maintaining team quality. A team invitation QR code containing the team identifier and the inviter's identity is generated for each dedicated referrer, facilitating and ensuring traceability in team recruitment. When other referrers scan the QR code, a team member relationship mapping is established, constructing a clear team organizational structure. The total order amount of team members is calculated, and additional team commissions are distributed according to a preset ratio, realizing a profit-sharing mechanism for team collaboration. This team-based operation model expands the coverage of the referral network, improving overall referral efficiency through resource sharing and experience exchange among team members. The additional team commissions incentivize referrers to actively develop and manage their teams, forming a sustainable referral ecosystem.

[0019] Secondly, embodiments of this application provide a store recommendation management device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions, and the one or more processors call the computer instructions to cause the store recommendation management device to perform the method described in the first aspect and any possible implementation thereof.

[0020] Thirdly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a store recommendation management device, cause the store recommendation management device to perform the method described in the first aspect and any possible implementation thereof.

[0021] Fourthly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a store recommendation management device, cause the store recommendation management device to perform the method described in the first aspect and any possible implementation thereof.

[0022] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. Enhance the systematization and precision of referral management: This technical solution achieves systematization and precision in the management of referral personnel through referral officer identity authentication, hierarchical evaluation, and dynamic allocation of commission rules. It solves the problems of chaotic identity management and inaccurate rights allocation in traditional referral business, and effectively improves the scientific nature and fairness of referral management.

[0023] 2. Improve order processing and commission settlement efficiency: By acquiring real-time referral room inventory data, automatically processing customer booking requests, and dynamically tracking order consumption status, this solution can automatically generate booking orders and quickly calculate and distribute referral commissions based on consumption, significantly reducing the need for manual intervention and improving the efficiency and accuracy of order processing and commission settlement.

[0024] 3. Enhance the incentive effect of referral promotion and customer loyalty: By linking referrers with customers, monitoring customers' subsequent consumption behavior, and introducing a team referral mechanism and additional commission rewards, this plan not only incentivizes referrers to promote the business, but also further enhances customer loyalty to the store, thereby improving the store's long-term customer acquisition capabilities and revenue growth potential. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating a store recommendation management method disclosed in an embodiment of this application; Figure 2 This is another schematic diagram of a store recommendation management method disclosed in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a store recommendation management device provided in an embodiment of this application.

[0026] Explanation of reference numerals in the attached drawings: 301, Central Processing Unit; 302, Read-Only Memory; 303, Random Access Memory; 304, Bus; 305, Input / Output Interface; 306, Input Section; 307, Output Section; 308, Storage Section; 309, Communication Section; 310, Driver; 311, Removable Media. Detailed Implementation

[0027] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0028] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0029] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple system devices refer to two or more system devices, and multiple screen terminals refer to two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0030] This application provides a store recommendation management method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a method provided in an embodiment of this application. The method is applied to a server, which can execute a store recommendation management method program. The method includes steps S101 to S106, as follows: Step S101: Obtain the referrer identity authentication request submitted by the user through the user terminal device. The referrer identity authentication request includes user information and the target store to be authenticated.

[0031] In step S101, the user terminal device refers to the smartphone, tablet, or computer terminal device used by the user to initiate interactive operations and data transmission. The referrer identity authentication request refers to the data instruction for a user to apply to become a promoter for a specific store and obtain corresponding permissions. User information refers to basic information including the user's name, contact information, ID number, and past work experience. The target store to be authenticated refers to the specific offline business entity for which the user applies to establish a promotional partnership.

[0032] Specifically, the server provides an application programming interface (API) for user devices to call. After the user fills in the relevant information on the user device's interface and clicks the submit button, the user device generates a referral agent identity verification request containing user information and the target store to be verified, and sends the referral agent identity verification request to the server. The server receives the referral agent identity verification request, performs format validation on the request, ensures that the stored data fields are complete and conform to the preset data interaction specifications, and then temporarily stores the verified referral agent identity verification request in a pending review queue.

[0033] Step S102: If it is confirmed that the management terminal of the target store has received the approval instruction for the referrer identity authentication request, then the user is marked as a referrer, and the referrer type and corresponding commission rules are determined.

[0034] In step S102, the target store's management terminal refers to the terminal device used by the target store's manager to handle business approvals, inventory management, and operational decisions. The approval instruction is a control signal issued by the management terminal confirming agreement to establish a cooperative relationship. The referrer identity is an identifier granting the user the authority to place orders on behalf of customers, view specific inventory, and earn revenue within the system. The referrer type refers to the level or category categorized based on user attributes. The commission rule refers to the specific algorithmic logic or percentage parameters for calculating promotional revenue.

[0035] Specifically, the server forwards the received referral agent authentication request to the target store's management terminal, or sends a to-do notification to the target store's management terminal. The server listens for feedback signals from the target store's management terminal in real time. When the server receives an approval instruction from the target store's management terminal regarding the referral agent authentication request, the server updates the account status field of the user who sent the referral agent authentication request to the referral agent identity in the database. Subsequently, the server determines the referral agent type to which the referral agent identity belongs based on a preset evaluation model or the configuration parameters carried by the administrator in the approval instruction, and retrieves the commission rule matching the referral agent type from the database, associating the commission rule with the referral agent identity for storage, so as to be called later when calculating commissions.

[0036] In one possible implementation, determining the category of the recommender's identity and the corresponding commission rules specifically includes steps S1021-S1026, as follows: Step S1021: Parse the user's historical recommendation data contained in the identity authentication request. The user's historical recommendation data includes the number of historical recommendations, the historical recommendation success rate, and the historical customer satisfaction score.

[0037] Step S1022: Calculate the recommender reputation score based on the user's historical recommendation data using a preset recommender type evaluation model.

[0038] Step S1023: If it is determined that the recommender's reputation score is greater than or equal to the preset score threshold, then the recommender type is determined to be an exclusive recommender.

[0039] Step S1024: If the exclusive referrer is determined to be an industry referrer, then the industry commission rules are assigned to the exclusive referrer. An industry referrer is a referrer who belongs to the same industry as the target store.

[0040] Step S1025: If the exclusive referrer is determined to be a cross-industry referrer, then assign cross-industry commission rules to the exclusive referrer. A cross-industry referrer is a referrer who does not belong to the same industry as the target store.

[0041] Step S1026: If it is determined that the referrer's reputation score is less than the preset score threshold, then the referrer type is determined to be a regular referrer, and the corresponding regular commission rule is assigned to the regular referrer.

[0042] Specifically, in the implementation process of determining the referrer identity category and commission rules on the server, a multi-dimensional data analysis and intelligent evaluation mechanism was adopted. When the server receives a referrer identity authentication request submitted by a user, it first performs in-depth analysis of the request data packet through the data parsing module. The server extracts the user's unique identifier from the request and uses this as an index to retrieve the user's complete historical referral data from the historical database.

[0043] To track historical recommendation frequency, the server queries all booking requests initiated by the user within the past 12 months. For example, if user Zhang initiated 156 booking requests in the past year, of which 132 were successfully converted into actual consumption, the system will store this data in a structured format.

[0044] Calculating historical recommendation success rates is relatively complex. The server not only calculates the simple success rate but also considers time-weighted factors. Recent recommendation success rates are given higher weight; for example, the success rate over the last 3 months has a weight of 0.5, over 3-6 months 0.3, and over 6-12 months 0.2. If Mr. Zhang's success rate is 92% over the last 3 months, 85% over 3-6 months, and 78% over 6-12 months, then the weighted success rate is 92% × 0.5 + 85% × 0.3 + 78% × 0.2 = 87.1%.

[0045] Obtaining historical customer satisfaction ratings involves integrating multiple data sources. The server calls the order rating system's API to retrieve rating data from recommended customers for each purchase experience. Simultaneously, the server analyzes customer repurchase rates and complaint records. If 80% of the customers recommended by a particular referrer make repeat purchases and the complaint rate is below 2%, these positive indicators will improve their satisfaction rating.

[0046] After obtaining complete historical recommendation data for users, the server calls a pre-defined recommendation officer type evaluation model to calculate a reputation score. This evaluation model uses a weighted scoring algorithm, where historical recommendation frequency accounts for 30%, recommendation success rate accounts for 40%, and customer satisfaction accounts for 30%.

[0047] In the specific calculation, the server first standardizes each indicator. For example, the number of recommendations is converted into a score range of 0-100: 0-50 recommendations correspond to 0-60 points, 50-100 recommendations correspond to 60-80 points, and over 100 recommendations correspond to 80-100 points. Zhang's 156 recommendations would earn approximately 88 points. The success rate is directly converted into a percentage score, 87.1% is equivalent to 87.1 points. Customer satisfaction of 4.5 stars (out of 5) is converted to 90 points.

[0048] Final credit score = 88 × 30% + 87.1 × 40% + 90 × 30% = 88.24 points Based on the calculated reputation score of the referrer, the server determines the referrer type using a preset threshold. When the reputation score is greater than or equal to 80, the system automatically marks the user as a dedicated referrer; otherwise, the user is marked as a regular referrer.

[0049] For dedicated referral officers, the server needs to further determine their industry affiliation. The system reads the occupational information filled in by the user during authentication and uses an industry classification algorithm to determine whether they belong to the same industry. If the user fills in the occupation as "bar server" or "KTV manager," the system identifies them as an industry referral officer and assigns them the corresponding industry commission rule (such as 15% of the order amount); if the occupation is "hotel receptionist" or "taxi driver," they are identified as a cross-industry referral officer and assigned a cross-industry commission rule (such as 12% of the order amount). Dedicated referral officers also enjoy benefit commissions, that is, they receive a 3% commission when customers they refer make purchases on their own.

[0050] For regular referral agents, the server assigns standard commission rules, which may be a fixed amount (such as 50 yuan per order) or a lower percentage (such as 8% of the order amount). All these commission rule parameters are stored in the system configuration table, and the store management side can adjust them at any time according to operational needs.

[0051] Step S103: Receive a booking request for the target store initiated by a user with the identity of a referrer.

[0052] In step S103, a booking request on behalf of a customer refers to a data packet initiated by a user with the identity of a referrer on behalf of the end consumer to the target store for a booking service.

[0053] Specifically, the server receives booking requests sent by users with referral officer status via their client devices. These requests include the end consumer's booking time, room type, contact information, and expected number of guests. The server verifies the permissions of the account initiating the booking request, confirming that the account currently holds a valid referral officer status and that this referral officer status is linked to the target store. If verification is successful, the server parses the key fields in the booking request and prepares to perform inventory matching.

[0054] Step S104: Obtain the recommended room inventory data of the target store, process the reservation request on behalf of the customer based on the recommended room inventory data, and generate a reservation order.

[0055] In step S104, the recommended room inventory data refers to the number of bookable rooms, room types, and real-time status information specifically allocated to the referrer channel by the target store. A reservation order refers to a data record generated in the system that includes reservation details, customer information, and transaction status.

[0056] Specifically, based on the target store identifier in the customer's booking request, the server queries the database for the recommended room inventory data of that target store. The server determines whether there are remaining quotas in the recommended room inventory data that meet the time period and room type requirements in the customer's booking request. If the server confirms that there are remaining quotas, it performs an inventory locking operation, that is, deducts the corresponding amount of available inventory from the recommended room inventory data and generates a booking order with a status of pending consumption or pending payment. The booking order records the referrer's identity identifier, customer information, booking details, and order creation time. The server feeds back the details of the booking order to the user's device and the target store's management terminal.

[0057] In one possible implementation, before obtaining the recommended room inventory data of the target store, the method further includes: obtaining all room status information synchronized by the management terminal of the target store; initializing the recommended room inventory data according to the basic recommended room quota of the target store, and assigning a quota number to each recommended quota; creating a quota mapping relationship, mapping each recommended quota to a specific room in the all room status information through the quota number; and updating the availability status of the corresponding recommended quota in the recommended room inventory data when the all room status information changes.

[0058] Specifically, the server first establishes a real-time connection with the target store's management system via a data synchronization interface. The store management system maintains complete status information for all rooms, including room number, room type, current status (available, in use, being cleaned, under maintenance), and reservation details. The server proactively pulls all data every 5 minutes via API, and the store system also proactively pushes incremental updates when room status changes. For example, if a KTV store has 50 private rooms (10 large, 20 medium, and 20 small), the server will obtain the real-time status matrix for each room.

[0059] After obtaining complete room status information, the server initializes the inventory based on the pre-set basic quota for recommended rooms set by the store. The basic quota for recommended rooms is the number of exclusive rooms reserved by the store for the referral channel, usually accounting for 20%-30% of the total number of rooms. For example, the store may allocate a quota of 15 private rooms to the referral channel, including 3 large private rooms, 6 medium private rooms, and 6 small private rooms.

[0060] During initialization, the server generates a unique quota number for each recommended quota. The numbering rule adopts the format of "store code-room type-serial number", such as "KTV001-L-001" representing the first large private room recommended quota of that store. This structured numbering system not only facilitates system management but also benefits subsequent data tracking and statistical analysis. The server creates corresponding records in the recommended room inventory data table, with each record containing fields such as quota number, room type, availability status, and associated room number.

[0061] The creation of quota mapping relationships is the core of the entire mechanism. The server doesn't simply reserve fixed rooms for referral channels; instead, it uses dynamic mapping. The system employs an intelligent allocation algorithm to establish a flexible correspondence between recommended quotas and actual rooms. For example, the quota "KTV001-L-001" might be mapped to different large private rooms at different times—to "Room 101" in the morning and "Room 102" in the afternoon. This dynamic mapping ensures flexibility in room utilization.

[0062] When creating mapping relationships, the server considers several factors: the room's geographical location (prioritizing rooms with good views and convenient locations), the room's equipment condition (prioritizing rooms with newer equipment and better sound systems), and historical usage data (prioritizing rooms with high customer satisfaction). The system maintains a priority queue for each quota, storing a list of mappable rooms.

[0063] When the room status in the store changes, the monitoring module of the server will immediately capture these change events. The status changes include various situations: the room changes from idle to in use, the room enters the maintenance state, the room becomes available again after cleaning, etc. Whenever a status change is captured, the server will perform a series of associated update operations.

[0064] For example, when the "Large Private Room 101" changes from the idle state to in use, and this room is exactly mapped to the quota "KTV001-L-001", the server will immediately update the available status of this quota to "unavailable". At the same time, the system will automatically search for other idle large private rooms for re - mapping. If it is found that the "Large Private Room 105" is in the idle state, the mapping relationship will be updated and the quota will be redirected to "Room 105".

[0065] This dynamic update mechanism ensures the real - time and accuracy of the recommended room inventory control. The server also sets a mapping optimization strategy. During the low - peak period of business (such as weekday afternoons), the system will automatically adjust the mapping relationship and preferentially allocate rooms with better quality to the recommended channels; while during the peak period (such as weekend evenings), it will ensure that the recommended quotas do not overly occupy high - quality resources, maintaining the overall balance of the store's operation.

[0066] The server details each mapping change through transaction logs, including information such as the change time, reason, operation type, etc. These log data are not only used for system auditing but also provide a basis for subsequent big data analysis, helping the store optimize the recommended room inventory control strategy and achieve lean operation.

[0067] In a possible implementation method, the valet reservation request is processed according to the recommended room inventory control data to generate a reservation order, specifically including: extracting the reservation time period and room type requirements in the valet reservation request, determining the remaining available quotas corresponding to the reservation time period and room type requirements in the recommended room inventory control data; comparing the remaining available quotas with the reservation quantity in the valet reservation request; if the remaining available quotas are greater than or equal to the reservation quantity, perform an inventory locking operation, deduct the recommended room inventory corresponding to the reservation quantity, and generate a reservation order with the status of confirmed; if the remaining available quotas are less than the reservation quantity, generate a reservation order with the status of pending and push the reservation order to the management terminal of the target store.

[0068] Specifically, when the server receives a valet reservation request initiated by a recommender through the system, it first starts the request parsing module to perform a detailed analysis of the data packet. The server will extract key reservation information from the request data, mainly including two core elements: the reservation time period and the room type requirements. For example, if recommender Li reserves a "large private room from 20:00 on March 15, 2024 to 02:00 the next day" for a customer, the system will accurately identify that the time span is 6 hours and the room type is a large private room.

[0069] The server then accesses the referral-controlled room inventory database, a dedicated inventory pool for referrals. Distinct from the store's total inventory, the referral-controlled room inventory is a pre-allocated quota from the store to the referral channel. The server uses a dual matching mechanism of time and room type indexes to quickly locate the inventory status of a specific room type within the target time period.

[0070] In determining the remaining available quota, the server executes inventory calculation logic. The system not only checks the static inventory quantity but also considers dynamic factors. For example, if the total recommended quota for large private rooms is 10 rooms from 20:00 on March 15th to 02:00 the next day, with 6 rooms already confirmed and 2 rooms in a 15-minute payment waiting period, then the actual remaining available quota is 2 rooms. The server calculates this dynamic data in real time to ensure inventory accuracy.

[0071] The quota comparison process is a crucial step in determining whether an order will be successful. The server rigorously compares the calculated remaining available quota with the number of pre-orders placed by the referrer. This comparison process uses atomic operations to ensure that overselling does not occur under high concurrency.

[0072] When the remaining available quota meets the booking demand, the server immediately performs an inventory lock operation. This operation involves several sub-steps: First, the system generates a unique lock identifier, marking the corresponding number of rooms as "pre-locked"; second, a deduction operation is performed in the recommended room inventory data table, reducing the available quota by the corresponding quantity; then, an inventory change log is created, recording information such as the operation time, operator, and changed quantity. After the inventory lock is completed, the server generates a booking order with a "confirmed" status, containing complete data such as order number, booking information, referrer information, and confirmation time.

[0073] For example, if Mr. Li books two large private rooms, and the system shows that there are three remaining available rooms, the server will immediately lock two rooms, update the remaining quota to one room, generate a confirmed order with the order number "RM202403150001", and send a booking success notification to the referrer and the customer.

[0074] When the remaining available quota is insufficient, the server adopts a different processing strategy. Instead of directly rejecting the order, the system generates a pre-order with a "pending" status. This design provides stores with flexibility in handling the situation. The server will indicate the specific circumstances of the quota shortage in the order data, such as "3 rooms requested, only 2 remaining."

[0075] Once an order is generated, the server pushes the order information to the target store's management terminal in real time via a push notification service. The push notification includes the complete booking request, referrer information, customer contact information, and system suggestions. Store managers can choose to temporarily allocate rooms from regular inventory to meet the demand, or negotiate with the referrer to adjust the booking plan.

[0076] The server also sets a processing time limit for pending orders, typically 30 minutes. During this period, the system will send a reminder notification to the store management every 10 minutes. If the order is not processed within the time limit, the server will automatically update the order status to "cancelled," release any pre-allocated resources, and notify the referrer of the reason for the booking failure, suggesting that they adjust the booking time or room type and resubmit.

[0077] Throughout the process, the server maintains a high degree of data consistency and transaction integrity, ensuring that every booking is processed accurately and promptly, protecting the interests of the recommenders while also giving stores ample operational flexibility.

[0078] Step S105: When the consumption status of a pre-order is detected to have changed to successful consumption, calculate the referral commission based on the commission rules and the actual order amount of the pre-order.

[0079] In step S105, "consumption status" refers to the current business stage of the pre-order within its lifecycle. "Actual order amount" refers to the monetary value actually paid by the end consumer after completing the transaction at the target store. "Referral commission" refers to the compensation paid to the referrer according to an agreed-upon percentage or fixed amount.

[0080] Specifically, the server continuously polls or receives status change notifications for pre-orders via a message queue. These notifications are typically triggered by the target store's POS or verification system. When the server detects that the pre-order's consumption status has changed to "successful consumption," it indicates that the end consumer has completed their in-store purchase and paid for the order. The server reads the actual order amount recorded in the pre-order and retrieves the commission rules bound to the referrer's identity from step S102. Using the actual order amount as a base, the server calculates the referral commission value corresponding to the transaction according to the percentage or fixed amount formula set in the commission rules.

[0081] Step S106: Obtain the receiving account linked to the referrer's identity and distribute the referral commission to the receiving account.

[0082] In step S106, the receiving account refers to the bank account or e-wallet account that the recommender has pre-linked for receiving funds.

[0083] Specifically, after calculating the referral commission, the server retrieves the payment account information linked to the referrer's identity from the user database. The server generates a transfer instruction or transaction record, transferring the calculated referral commission to the payment account. Simultaneously, the server generates a commission payment record, storing the payment time, amount, and associated order number in the historical billing database, and sends a commission payment notification to the user's device, completing the entire referral management process.

[0084] In one possible implementation, after obtaining the payment account linked to the referrer's identity and disbursing the referral commission to the payment account, the method further includes: extracting the customer identity identifier from the pre-order and establishing a binding relationship between the customer identity identifier and the referrer's identity, and recording the binding timestamp; monitoring the transaction flow of the target store's POS system, and when a new consumption record containing the customer identity identifier is obtained, determining whether the new consumption record is associated with a new referrer's pre-order request; if the new consumption record is not associated with a new referrer's pre-order request, determining it as a customer's independent consumption behavior; based on the binding relationship, invoking the commission rules, calculating the welfare commission based on the actual payment amount of the new consumption record, and disbursing the welfare commission to the payment account of the referrer's identity.

[0085] Specifically, once the server successfully transfers the referral commission to the referrer's account, the system immediately initiates the customer relationship binding process. The server extracts the customer's identification information from the completed booking data. This identification may include a mobile phone number, membership card number, or encrypted ID card number. For example, if referrer Wang successfully refers customer Zhang (mobile phone number 123) and completes a purchase, the server will use that mobile phone number as the customer's unique identifier.

[0086] The server creates a binding record in the customer relationship database, associating the identity of customer Mr. Zhang with the referrer identity of referrer Mr. Wang. The binding uses a many-to-one data structure, meaning a customer may have been referred by multiple referrers at different times, but the system determines the currently valid referral relationship based on the timestamp. The binding timestamp recorded by the server is accurate to the second, such as "2024-03-15 20:15:36". This timestamp not only determines the establishment time of the binding relationship but is also an important basis for subsequently calculating the binding validity period.

[0087] Once the binding relationship is established, the server establishes a real-time monitoring channel with the target store's POS system via a data bus. Each transaction from the POS system is pushed to the server's monitoring module via a message queue. The server deploys a dedicated transaction analysis engine to parse and match each new transaction record in real time.

[0088] When the POS system generates a new transaction record, the server first retrieves the customer's identity information for that transaction. For example, if Mr. Zhang returns to the store on a weekend evening a month later and uses the same mobile phone number to pay, the server's monitoring module immediately identifies this as a transaction from a registered customer.

[0089] The crucial step is determining whether the purchase was self-initiated. The server cross-validates the referrer's booking request database, checking for any new booking requests related to the customer before or after the purchase time. The query range is typically set to 6 hours before the purchase time up to the start of the purchase. If no booking requests initiated by any referrer for the customer are found within this time window, the system determines that this was a self-initiated purchase by the customer.

[0090] For example, Mr. Zhang went directly to the store at 8 pm on April 20 and spent 1,500 yuan. The server query found that no recommender had made a reservation request for Mr. Zhang between 2 pm and 8 pm that day, so it was confirmed that this was a self-initiated consumption.

[0091] Once the purchase is confirmed as self-initiated, the server accesses the customer relationship database to find the customer's currently valid referral relationship. The system verifies whether the relationship is still valid; typically, a referral relationship is valid for 6 months. If Mr. Zhang's relationship with referrer Mr. Wang is still valid, the server proceeds to the commission calculation process.

[0092] The calculation of welfare commissions is based on the store's preset commission rules. For dedicated referral officers, the welfare commission rate is usually 3%-5%. The server uses the customer's actual payment amount as the calculation base, which is the actual payment amount after deducting various coupons and discounts. For example, if Mr. Zhang actually paid 1500 yuan, at a welfare commission rate of 3%, referral officer Wang would receive 45 yuan in welfare commission.

[0093] The server performs multiple checks when calculating benefits commissions: First, it confirms whether the referrer's identity status is normal (not frozen or canceled); second, it verifies whether the purchase has already generated other types of referral commissions to avoid double counting; finally, it checks whether the store's benefits commission budget pool is sufficient.

[0094] After all verifications are completed, the server generates a welfare commission record, including detailed information such as the order number, customer information, referrer information, commission amount, and calculation basis. The system then uses an automated financial processing flow to add the welfare commission to the referrer's pending settlement amount. Depending on the store's settlement cycle settings (daily, weekly, or monthly), the welfare commission will be automatically deposited into the referrer's linked payment account at the next settlement time.

[0095] This incentive mechanism allows referral officers to not only benefit from direct referrals but also to continuously earn income from customers' subsequent independent consumption, forming a long-term bond of mutual benefit and motivating them to recommend high-quality customers and maintain good customer relationships.

[0096] Please refer to Figure 2 In one possible implementation, the method further includes steps S201-S204, as follows: Step S201: Check whether the recommender's identity is that of an exclusive recommender.

[0097] Step S202: If the referrer's identity is a dedicated referrer, then generate a team invitation QR code for the dedicated referrer. The team invitation QR code includes the target team's team identification information and the referrer's identity information.

[0098] Step S203: When it is detected that other referrers have scanned the team invitation QR code, establish a team member relationship mapping and add the other referrers to the target team.

[0099] Step S204: Calculate the total order amount of the team members in the target team, and calculate the additional team commission for each team member in the target team according to the preset team commission ratio.

[0100] Specifically, once a referrer completes an order and receives their commission, the server automatically checks their status level. The system determines whether they qualify as an exclusive referrer by querying the type field in the referrer's data table. Exclusive referrers are senior referrers who have achieved a credit score of 80 or higher and possess special privileges to build teams. For example, referrer Li's credit score reaches 88, thus qualifying him as an exclusive referrer.

[0101] After confirming the identity of the exclusive referrer, the server activates team management functionality for them. The system first creates a unique team identifier for the exclusive referrer, using the format "TM + store code + timestamp", such as "TM-KTV001-202403150001". This team identifier will serve as the core index for all subsequent team operations.

[0102] The server then initiates a QR code generation service, creating a unique team invitation QR code. The QR code embeds encrypted composite information, including team identification information (TM-KTV001-202403150001), the inviter's identity information (Li's referral ID), store identification, and invitation validity period. The system uses dynamic QR code technology, with each QR code having a 24-hour validity period, automatically expiring afterward to prevent malicious dissemination and abuse.

[0103] The generated QR code image is pushed to the dedicated referrer, Mr. Li, via the referrer's app. He can then share it with potential team members through social media. The QR code also embeds an anti-counterfeiting watermark and digital signature to ensure the authenticity and security of the invitation link.

[0104] When other referral officers scan the team's invitation QR code, the server's QR code scanning module is immediately triggered. The system first verifies the referral officer's identity, confirming their registration status in the system and whether they have already joined another team. According to business rules, each referral officer can only join one team. If the system detects that the referral officer already belongs to a team, it will prompt them to leave their original team first.

[0105] For example, if a regular referrer, Zhang, scans Li's team invitation QR code, and the server verifies that Zhang's referrer status is valid and that he has not joined any other teams, then Zhang will enter the team joining process. The system will display team information to Zhang, including basic information about the team creator, Li, the current size of the team, team performance data, etc., allowing him to make an informed choice.

[0106] After Zhang confirms joining, the server creates a new record in the team member relationship mapping table. This mapping relationship contains information in multiple dimensions: team identifier (TM-KTV001-202403150001), team leader ID (Li), member ID (Zhang), joining time, member level (level 1 member), invitation link, etc. The system supports multi-level team structures. If Zhang subsequently becomes a dedicated referrer, the members he invites will become level 2 members.

[0107] Once team relationships are established, the server begins tracking team performance in real time. The system has a dedicated team performance statistics module; whenever a team member generates a new order and successfully completes it, the order amount is added to the team's total performance. The statistics period can be set daily, weekly, or monthly.

[0108] Team commission calculation is the core value of the entire mechanism. The server calculates additional team commissions based on a preset team commission rate, which is an extra reward on top of the individual referral commission. For example, if Mr. Li's team's total performance reaches 100,000 yuan in a certain month, with a 2% team commission rate, the total team commission pool will be 2,000 yuan.

[0109] Commission distribution employs a differentiated strategy. Team leader Li, as the team's creator and manager, typically receives 50% of the team's commission, or 1000 yuan. The remaining 1000 yuan is distributed based on each member's contribution, calculated using a formula that considers factors such as individual performance contribution, length of service, and customer satisfaction. If Zhang contributes 30,000 yuan in sales that month (30% of the team's total sales), he might receive an additional 300-400 yuan in team commission.

[0110] The server also has upper limits and protection rules for the team incentive mechanism. To prevent excessive pyramid-style incentives, the system limits the maximum number of members in each team to 50, and the team hierarchy to no more than 3 levels. Furthermore, if a team member has no performance contributions for two consecutive months, the system will issue a warning, and the team leader has the right to remove them from the team.

[0111] This team-based operation model, through intelligent server management, not only enhances the income potential of referral officers but also fosters a supportive team culture. Referral officers can share experiences and collaboratively develop clients, ultimately achieving a win-win situation for stores, referral officers, and clients.

[0112] The following describes a store recommendation management device according to an embodiment of the present invention from the perspective of hardware processing. Please refer to [link / reference]. Figure 3 This is a schematic diagram of the structure of a store recommendation management device in an embodiment of this application.

[0113] It should be noted that, Figure 3 The structure of the store recommendation management device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0114] like Figure 3 As shown, a store recommendation management device includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in a Read-Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores various programs and data required for device operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0115] The following components are connected to I / O interface 305: input section 306 including audio input devices, push-button switches, etc.; output section 307 including a liquid crystal display (LCD) and audio output devices, indicator lights, etc.; storage section 308 including a hard disk, etc.; and communication section 309 including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0116] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0117] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0119] Specifically, a store recommendation management device according to this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements a store recommendation management method provided in the above embodiment.

[0120] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in a store recommendation management device described in the above embodiments; or it may exist independently and not assembled into the store recommendation management device. The storage medium carries one or more computer programs, which, when executed by a processor of the store recommendation management device, cause the store recommendation management device to implement the store recommendation management method based on encrypted data transmission via the Internet of Things provided in the above embodiments.

Claims

1. A store recommendation management method, characterized in that, The method includes: Obtain the referrer identity authentication request submitted by the user through the user terminal device, wherein the referrer identity authentication request includes user information and the target store to be authenticated; If it is determined that the management terminal of the target store has received an approval instruction for the referrer identity authentication request, then the user is marked as a referrer, and the referrer type and corresponding commission rules of the referrer identity are determined. Receive booking requests on behalf of customers for the target store initiated by users with the aforementioned referrer status; Obtain the recommended room inventory data of the target store, and process the reservation request on behalf of the customer based on the recommended room inventory data to generate a reservation order; When the consumption status of the pre-order is detected to have changed to successful consumption, a referral commission is calculated based on the commission rules and the actual order amount of the pre-order. Obtain the payment account linked to the referrer's identity and distribute the referral commission to the payment account.

2. The method according to claim 1, characterized in that, The determination of the category of the recommender's identity and the corresponding commission rules specifically includes: The user's historical recommendation data contained in the identity authentication request is parsed. The user's historical recommendation data includes the number of historical recommendations, the historical recommendation success rate, and the historical customer satisfaction score. Based on the user's historical recommendation data, the recommendation officer's reputation score is calculated using a preset recommendation officer type evaluation model. The referrer type and commission rules are determined based on the referrer's reputation score.

3. The method according to claim 2, characterized in that, The process of determining the referrer type and commission rules based on the referrer's reputation score specifically includes: If the reputation score of the recommender is determined to be greater than or equal to a preset score threshold, then the recommender type is determined to be an exclusive recommender. If the exclusive referrer is determined to be an industry referrer, then an industry commission rule is assigned to the exclusive referrer. The industry referrer is a referrer who belongs to the same industry as the target store. If the exclusive referrer is determined to be a cross-industry referrer, then cross-industry commission rules are assigned to the exclusive referrer. The cross-industry referrer is a referrer who does not belong to the same industry as the target store. If it is determined that the reputation score of the referrer is less than the preset score threshold, then the referrer type is determined to be a regular referrer, and a corresponding regular commission rule is assigned to the regular referrer.

4. The method according to claim 1, characterized in that, The step of processing the booking request on behalf of the customer based on the recommended room inventory data and generating a booking order specifically includes: Extract the booking time period and room type requirements from the booking request, and determine the remaining available quota corresponding to the booking time period and room type requirements from the recommended room inventory data; Compare the remaining available quota with the reservation quantity in the reservation request; If the remaining available quota is greater than or equal to the reservation quantity, an inventory lock operation is performed, the recommended control room inventory corresponding to the reservation quantity is deducted, and a reservation order with a confirmed status is generated. If the remaining available quota is less than the reservation quantity, a reservation order with a pending status is generated and the reservation order is pushed to the management terminal of the target store.

5. The method according to claim 1, characterized in that, Before obtaining the recommended controlled inventory data of the target store, the method further includes: Obtain all room status information synchronized from the management terminal of the target store; Based on the recommended control room quota of the target store, initialize the recommended control room inventory data and assign a quota number to each recommended quota; Create a quota mapping relationship, and map each of the recommended quotas to a specific room in the total room status information through the quota number; When the status information of all rooms changes, the availability status of the corresponding recommended quota in the recommended room inventory data is updated.

6. The method according to claim 1, characterized in that, After obtaining the receiving account linked to the referrer's identity and disbursing the referral commission to the receiving account, the method further includes: Extract the customer identity identifier from the reservation order, establish a binding relationship between the customer identity identifier and the referrer identity, and record the binding timestamp; Monitor the transaction flow of the POS system of the target store, and when a new consumption record containing the customer's identity is obtained, determine whether the new consumption record is associated with a new referrer's booking request; If the newly added consumption record is not associated with a new referrer booking request, it is determined to be a customer's independent consumption behavior; Based on the binding relationship, the commission rules are invoked to calculate the welfare commission based on the actual payment amount of the newly added consumption record, and the welfare commission is distributed to the receiving account of the referrer.

7. The method according to claim 1, characterized in that, The method further includes: Check whether the recommender's identity is that of an exclusive recommender; If the recommender is a dedicated recommender, then a team invitation QR code is generated for the dedicated recommender. The team invitation QR code includes the team identification information of the target team and the identity information of the inviter. When it is detected that another referrer has scanned the team invitation QR code, a team member relationship mapping is established, and the other referrer is added to the target team; Calculate the total order amount of the target team members, and calculate additional team commission for each team member in the target team according to the preset team commission ratio.

8. A store recommendation management device, characterized in that, The store recommendation management device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the store recommendation management device to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the store recommendation management device, the store recommendation management device performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the store recommendation management device, the store recommendation management device performs the method as described in any one of claims 1-7.

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