Order processing method and device in collective ordering scene, equipment and medium

Abstract

The invention relates to the technical field of order processing. The order processing method and device in the collective ordering scene, the equipment and the medium are provided, and the method comprises the following steps: carrying out trigger condition judgment processing on an order data set, and generating a combined order instruction when a preset condition is met; based on the acquired payment mode selection result and the combined order instruction, payment co-processing is performed on the order data set, and a payment state mapping table and an order submission instruction are generated; in response to an order submission instruction, performing food distribution remark matching processing on the order data set, and generating a mapping relationship between personalized remarks and order instances; based on the mapping relationship, generating a meal product with a remark identifier through the catering server; and state tracking processing is carried out on the meals with the remark identifiers, and distribution progress information synchronous to all the staff is generated, so that the technical effects of improving the collective meal ordering management efficiency, ensuring the meal distribution accuracy, enhancing the payment flexibility and optimizing the order combining process are achieved.

Description

Technical Field

[0001] This invention relates to the field of order processing technology, and in particular to order processing methods, apparatus, equipment and media in the context of group meal ordering. Background Technology

[0002] With the increasing demand for digital management in enterprises, group meal ordering is playing an increasingly crucial role in corporate administrative services as an important scenario for improving team collaboration efficiency and cohesion. Efficient group meal ordering management is a core element in ensuring employee satisfaction and optimizing resource allocation, and it is also a key focus and challenge in the field of digital catering technology.

[0003] In traditional technologies, group meal ordering relies on the initiator manually processing member orders, resulting in prolonged occupation of their mobile devices and severely impacting work efficiency and operational security. The food distribution process lacks automated binding mechanisms for identity verification and customized needs, requiring manual checking and matching, leading to significant deficiencies in distribution accuracy and timeliness. Furthermore, existing technologies cannot support independent payments by multiple individuals and real-time settlement collaboration, resulting in low flexibility in fund management. For immediate group order needs within the same office environment, the lack of access control and delivery coordination mechanisms makes rapid initiation and accurate delivery difficult. Summary of the Invention

[0004] Therefore, it is necessary to provide order processing methods, devices, equipment, and media for group meal ordering scenarios to address the aforementioned technical issues, so as to improve the efficiency of group meal ordering management, ensure the accuracy of meal distribution, enhance payment flexibility, and optimize the group ordering process.

[0005] Firstly, this application provides an order processing method for group meal ordering scenarios, the method including:

[0006] The group-buying constraint parameters input by the initiator are processed to generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes.

[0007] The system processes orders by identifying dynamic order channels, performs unique conflict checks on personalized remarks fields and guides users to make modifications, and generates an order dataset that includes unique identifiers, customized food information and verified remarks fields.

[0008] The order dataset is processed to determine trigger conditions, and a merge order instruction is generated when the preset conditions are met.

[0009] Based on the obtained payment mode selection results and order merging instructions, the order dataset is processed for payment coordination to generate a payment status mapping table and order submission instructions;

[0010] In response to the order submission command, perform food distribution note matching on the order dataset to generate a mapping relationship between personalized notes and order instances;

[0011] Based on the mapping relationship, meals with annotation labels are generated through the catering service terminal;

[0012] For meals with annotations, status tracking is performed to generate delivery progress information synchronized across all staff.

[0013] In one embodiment, the order dataset is processed to determine trigger conditions, and a merge order instruction is generated when preset conditions are met, including:

[0014] Perform timeliness calculations on the current time and the generation time of the dynamic ordering channel identifier, and generate a timeout trigger signal;

[0015] The number of valid orders in the order dataset is numerically compared with the participant size threshold to generate a full-amount trigger signal.

[0016] Orders are aggregated based on timeout or full-amount trigger signals to generate merged order instructions.

[0017] In one embodiment, order aggregation processing is performed on timeout trigger signals or full-amount trigger signals to generate merged order instructions, including:

[0018] The system parses and processes timeout or full-amount trigger signals to generate order aggregation execution instructions.

[0019] Based on the order aggregation execution instruction, the stored valid order data is extracted in batches to generate the original order set;

[0020] The original order set is structured and integrated to generate a merge order instruction.

[0021] In one embodiment, the group-buying constraint parameters input by the initiator are processed to generate an identifier, which includes a dynamic ordering channel identifier that includes permission binding and time-limited control, including:

[0022] Receive group-buying constraint parameters input by the initiator. The group-buying constraint parameters include the initiator's unique identifier, participant size threshold, effective duration threshold, and range of selectable meals.

[0023] Perform timestamp generation processing on the real-time data of the system clock to generate the current timestamp;

[0024] The preset random number generator is subjected to random factor generation processing to generate random factors;

[0025] The following formula is used to perform a composite encoding process on the current timestamp and the random factor to generate a globally unique QR code:

[0026]

[0027] in, This indicates a globally unique QR code. This represents a composite coding function. Indicates the current timestamp. This indicates that the 6 characters are a random string. This represents a string concatenation operation;

[0028] Based on the effective duration threshold, the globally unique QR code is time-bound to generate a dynamic ordering channel identifier.

[0029] In one embodiment, unique conflict verification and user modification guidance are performed on the personalized remarks field to generate an order dataset including a unique identifier, menu customization information, and verified remarks fields, including:

[0030] Receive personalized remarks submitted by users who place orders;

[0031] Calculate the hash identifier value of the personalized remarks field;

[0032] When a conflict is detected between the hash identifier value of the personalized remarks field and the existing remarks hash value, a conflict prompt is sent to the user and a graphical interface modification is forced to generate an order dataset that meets the uniqueness condition.

[0033] In one embodiment, based on the obtained payment mode selection result and order merging instruction, payment coordination processing is performed on the order dataset to generate a payment status mapping table and an order submission instruction, including:

[0034] Based on the payment mode selection results and order merging instructions, the order dataset is processed for payment status tracking to generate payment status data.

[0035] The system processes and judges the payment status data and preset payment trigger conditions to generate a payment status mapping table and an order submission instruction.

[0036] In one embodiment, the method further includes:

[0037] The records stored in the historical group-buying database are processed to extract configurations and generate preset delivery addresses and preset meal configurations. The preset delivery addresses and preset meal configurations are used as input values ​​for the delivery address field and meal range field of the group-buying constraint parameters.

[0038] By connecting to the application programming interface of the office software open platform, member scope restriction processing is performed to generate access permission sets for limited group members. These access permission sets are used to control access permissions for dynamic ordering channel identifiers.

[0039] The system performs permission allocation processing on the data of the assignment instructions to the assistant administrators, and generates the management permission configuration for the assistant administrators.

[0040] Secondly, this application also provides an order processing device for group meal ordering scenarios, the device comprising:

[0041] The dynamic QR code generation module is used to process the group-buying constraint parameters input by the initiator and generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes.

[0042] The order receiving and verification module is used to process orders based on the dynamic order channel identifier, perform unique conflict verification of personalized remarks fields and guide users to modify them, and generate an order dataset that includes unique identifiers, customized food information and verified remarks fields.

[0043] The trigger aggregation module is used to perform trigger condition judgment processing on the order dataset, and generate a merge order instruction when the preset conditions are met;

[0044] The payment coordination module is used to perform payment coordination processing on the order dataset based on the obtained payment mode selection results and order merging instructions, and generate a payment status mapping table and order submission instructions;

[0045] The remarks mapping generation module is used to respond to order submission instructions, perform food distribution remarks matching processing on the order dataset, and generate a mapping relationship between personalized remarks and order instances;

[0046] The food label generation module is used to generate food items with annotation labels based on mapping relationships through the catering service terminal;

[0047] The delivery tracking module is used to track the status of meals with annotations and generate delivery progress information synchronized across all staff.

[0048] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods in the first aspect of this application.

[0049] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods in the first aspect of this application.

[0050] This application provides a method, apparatus, equipment, and medium for order processing in a group meal ordering scenario. The method includes: generating a dynamic ordering channel identifier with permission binding and time-limited control based on group order constraint parameters input by the initiator. By limiting the scope of participants and the effective duration, group order initiation becomes more orderly, reducing invalid operations and optimizing the group ordering process. For orders received through the dynamic channel, unique conflict verification and modification guidance are performed on personalized remarks fields. Combined with the mapping relationship between subsequently generated personalized remarks and order instances, the catering service can generate meals with remarks identifiers. This forms a closed loop from data binding to physical identifiers, ensuring accurate matching of user needs during meal distribution and guaranteeing distribution accuracy.

[0051] By applying trigger conditions to the order dataset, a merge order instruction is automatically generated upon timeout or reaching the limit, replacing manual processing by the initiator. This reduces terminal usage and human intervention, improving the management efficiency of group meal orders. Payment coordination is performed based on the payment mode selection result and the merge order instruction, generating a payment status mapping table that supports different modes such as individual payments by multiple people or unified payments, meeting diverse payment needs and enhancing payment flexibility. Furthermore, the status tracking of meals with remarks and the synchronization of delivery progress across all staff further reduce communication costs and contribute to improved management efficiency. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of an order processing method in a group meal ordering scenario according to one embodiment of the present invention;

[0054] Figure 2 This is a flowchart illustrating the process of performing order aggregation on timeout trigger signals or full-amount trigger signals to generate merged order instructions in one embodiment of the present invention.

[0055] Figure 3 This is a structural diagram of an order processing device in a group meal ordering scenario according to one embodiment of the present invention. Detailed Implementation

[0056] To make the above-mentioned objects, features, and advantages of this application more apparent and understandable, the specific embodiments of this application will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0057] First, the application scenarios of the embodiments of this application are described. In the embodiments of this application, order processing methods, devices, equipment and media are provided for collective meal ordering scenarios applicable to, but not limited to, enterprise internal employee group meal ordering, department team afternoon tea group ordering, and multiple people collaboratively ordering catering in the same office area.

[0058] The order processing method, apparatus, equipment and medium provided in the embodiments of this application for group meal ordering can also be applied to application scenarios such as group meal ordering for school classes, centralized catering ordering within public institutions, and group catering ordering at large-scale events. This is only an example and does not limit the specific application scenarios.

[0059] like Figure 1 As shown, this application provides an order processing method for group meal ordering scenarios, the method including:

[0060] S101: Process the group-buying constraint parameters input by the initiator to generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes.

[0061] For example, the system receives group-buying constraint parameters input by the initiator. These parameters include the initiator's unique identifier, a participant size threshold, a valid duration threshold, and a range of selectable dishes. The validity of each parameter is verified. The initiator's unique identifier is associated with the access permission rules of the dynamic ordering channel identifier to achieve permission binding. The availability period of the dynamic ordering channel identifier to be generated is set according to the valid duration threshold to achieve timeliness control. A time factor and a random factor are then introduced for composite coding to ensure identifier uniqueness. Finally, the permission binding result, timeliness control rules, and coding information are integrated to generate a dynamic ordering channel identifier that includes permission binding and timeliness control.

[0062] S102: Process order receipt for dynamic order channel identifiers, perform unique conflict verification of personalized remarks fields and guide users to modify them, and generate an order dataset including unique identifiers, customized meal information and verified remarks fields.

[0063] For example, an order entry point is opened based on a dynamic order channel identifier, receiving initial order information submitted by the user, including a unique identifier, menu customization information, and personalized remarks fields. Then, the historical order remarks repository is retrieved, and the current personalized remarks field is compared with existing personalized remarks fields to perform a unique conflict check. If a conflict exists, a prompt is sent to the user, guiding them to re-enter the information, until the new remarks field passes the verification and becomes a validated remarks field. The unique identifier, menu customization information, and validated remarks fields are then structurally integrated to generate an order dataset including the unique identifier, menu customization information, and validated remarks fields.

[0064] S103: Perform trigger condition judgment processing on the order dataset, and generate a merge order instruction when the preset conditions are met.

[0065] For example, preset conditions are retrieved, including time-related conditions and order quantity-related conditions. The generation time of the dynamic ordering channel identifier, the current system time, and the number of valid orders in the order dataset are obtained. The difference between the current system time and the generation time of the dynamic ordering channel identifier is calculated, and it is determined whether it reaches the time-related condition threshold. Simultaneously, it is determined whether the number of valid orders reaches the order quantity-related condition threshold. If either condition is met, the valid order information in the order dataset is integrated, order aggregation processing is performed, and a merged order instruction is generated.

[0066] S104: Based on the obtained payment mode selection results and order merging instructions, perform payment coordination processing on the order dataset to generate a payment status mapping table and order submission instructions.

[0067] For example, the payment mode selection result and the order aggregation information of the merged order instruction are obtained. The payment mode selection result includes either a unified payment mode by the initiator or an independent payment mode for multiple users. The unique order identifier and corresponding order amount of each order are extracted from the order dataset, and payment association rules are configured according to the payment mode selection result: under the unified payment mode by the initiator, the order amount is aggregated and associated with the initiator's payment account; under the independent payment mode for multiple users, a unique payment identifier is generated for each order identifier and associated with the corresponding ordering user's payment account.

[0068] The system integrates with the payment interface to obtain the payment status of each payment-related object in real time. It associates and stores the unique order identifier or exclusive payment identifier with the corresponding payment status, generating a payment status mapping table. It then determines whether the payment status meets the preset order submission conditions; if the conditions are met, it generates an order submission instruction.

[0069] S105: In response to the order submission command, perform food distribution note matching processing on the order dataset to generate a mapping relationship between personalized notes and order instances.

[0070] For example, upon receiving an order submission instruction, the order dataset association information contained in the order submission instruction is parsed to determine the scope of the order dataset to be processed. From the determined scope of the order dataset, the order instance information and corresponding verified personalized notes for each order instance are extracted one by one, ensuring that the extracted information completely covers the core attributes and personalized notes of the order instance.

[0071] A matching rule for food distribution notes is used, which limits each verified personalized note to only one order instance, avoiding matching conflicts between notes and order instances. Based on this rule, the extracted verified personalized notes are individually associated and bound to the corresponding order instance information to complete the food distribution note matching process. Following a preset mapping data structure, all associated and bound information is structured and organized to generate a mapping relationship between personalized notes and order instances.

[0072] S106: Based on the mapping relationship, generate meals with notes and labels through the catering service terminal.

[0073] For example, the mapping relationship between personalized notes and order instances is transmitted to the food service provider, ensuring that the food service provider fully obtains the associated data in the mapping relationship. After receiving the mapping relationship between personalized notes and order instances, the food service provider parses the mapping relationship, extracts the food information and personalized notes corresponding to each order instance, and establishes a corresponding association between food information, personalized notes, and order instances. The food service provider generates a unique note identifier based on the extracted personalized notes. The note identifier must uniquely match the corresponding personalized note to avoid identifier confusion.

[0074] Next, the food service platform binds the generated remarks to the corresponding order instance's food preparation task, specifying the remarks required for each food preparation task. The food service platform then follows the food preparation process according to the food preparation task with the bound remarks, adding the corresponding remarks to the food or its packaging, thus generating a food with the remarks.

[0075] S107: Track the status of meals with notes and generate synchronized delivery progress information for all staff.

[0076] For example, the system connects to the food status collection unit at the food service end to acquire real-time status data for food items with annotations. This real-time status data includes the food preparation status, pending delivery status, delivery status, and delivered status. The acquired real-time status data is then associated and bound to the corresponding food items with annotations, ensuring that each piece of real-time status data matches a specific food item with an annotation. Next, a preset standard format for delivery progress information is retrieved. This format includes the annotation for the food item, the current real-time status, the status update time, and the estimated subsequent milestone time. Following the standard format for delivery progress information, the associated and bound real-time status data is structured to generate initial delivery progress information.

[0077] Next, a synchronized information push channel is established, which connects to the interactive terminal interface of all participants in the group-buying activity, ensuring that information reaches all participants. The status changes of meals marked with remarks are monitored in real time. If the status is updated, the above data association and structured processing steps are repeated to update the delivery progress information. This updated delivery progress information is then synchronized to all participants through the information push channel, generating synchronized delivery progress information for all participants.

[0078] An embodiment of this application provides an order processing method for group ordering scenarios, comprising: generating a dynamic ordering channel identifier with permission binding and time-limited control based on the group order constraint parameters input by the initiator; by limiting the scope of participants and the effective duration, making group order initiation more orderly, reducing invalid operations, and optimizing the group order process; performing unique conflict verification and modification guidance on personalized remarks fields for orders received through the dynamic channel; and combining the mapping relationship between subsequently generated personalized remarks and order instances, enabling the catering service to generate meals with remarks identifiers, forming a closed loop from data binding to physical identifiers, ensuring that meal distribution accurately matches user needs and guaranteeing distribution accuracy.

[0079] By applying trigger conditions to the order dataset, a merge order instruction is automatically generated upon timeout or reaching the limit, replacing manual processing by the initiator. This reduces terminal usage and human intervention, improving the management efficiency of group meal orders. Payment coordination is performed based on the payment mode selection result and the merge order instruction, generating a payment status mapping table that supports different modes such as individual payments by multiple people or unified payments, meeting diverse payment needs and enhancing payment flexibility. Furthermore, the status tracking of meals with remarks and the synchronization of delivery progress across all staff further reduce communication costs and contribute to improved management efficiency.

[0080] In one embodiment, the order dataset is processed to determine trigger conditions, and a merge order instruction is generated when preset conditions are met, including:

[0081] (1) Perform time-sensitivity calculation on the current time and the time of generation of dynamic ordering channel identifier, and generate timeout trigger signal.

[0082] For example, the clock component is accessed to obtain the current time, and the dynamic ordering channel identifier repository is accessed to extract the generation time of the dynamic ordering channel identifier corresponding to the current group order, ensuring that the two time parameters are consistent with the same time zone and time precision. The obtained current time and dynamic ordering channel identifier generation time are then imported into a preset timeliness calculation component.

[0083] The timeliness calculation component confirms the time calculation unit, which must be consistent with the preset effective duration threshold unit. It then calculates the time difference between the current time value and the time value generated by the dynamic ordering channel identifier, according to the calculation rules. The timeliness calculation component retrieves the preset effective duration threshold corresponding to the group-buying constraint parameter configuration library and compares the calculated time difference with the preset effective duration threshold item by item. If the time difference exceeds the preset effective duration threshold, the timeliness calculation component initiates the signal generation logic, generates a timeout trigger signal according to the preset signal format, and transmits the timeout trigger signal to the signal receiving port of the order aggregation component.

[0084] The clock component provides a real-time and accurate current time, supporting synchronization with standard time to ensure time accuracy. The dynamic ordering channel identifier repository stores all dynamic ordering channel identifiers, their generation times, and associated group-buying information, enabling quick retrieval of specific group-buying identifier generation times. The preset effective duration threshold is a parameter set by the initiator when inputting group-buying constraint parameters, used to limit the effective period of dynamic ordering channel identifiers, with its unit consistent with the time calculation unit of the timeliness calculation component. The timeout trigger signal follows a preset format, including trigger type, associated group-buying identifier, and trigger time, used to inform the order aggregation component to start the order aggregation process.

[0085] (2) Compare the number of valid orders in the order dataset with the participant size threshold to generate a full-amount trigger signal.

[0086] For example, the system retrieves preset order validity criteria from the order validity configuration library, and then extracts the complete order dataset corresponding to the current group purchase from the order data repository. Next, following the rule of verifying each order individually, the system checks the validity of each order in the order dataset: it checks whether the order contains a complete unique order identifier and customized food information (which must include mandatory fields such as category, ingredient requirements, and specifications); it verifies whether the order submission status is submitted and not cancelled, excluding withdrawn or invalid orders; and it confirms whether the personalized remarks field of the order has passed unique conflict validation. Each order that passes all the above checks is marked as a valid order, and the total number of valid orders is counted in real time to generate the number of valid orders in the order dataset.

[0087] Next, the system accesses the group-buying constraint parameter repository to extract the participant size threshold corresponding to the group-buying order. The statistically obtained number of valid orders and the extracted participant size threshold are then imported into a preset numerical comparison component. This component compares the two values ​​according to a rule that the number of valid orders is greater than or equal to the participant size threshold. If the comparison result satisfies this rule, the component generates a full-amount trigger signal according to a preset signal format and sends this signal to the signal receiving port of the order aggregation component.

[0088] The order validity criteria are a set of rules for filtering valid orders, including requirements for order information completeness, order submission status, and personalized remarks verification results. The number of valid orders is the total number of orders in the order dataset that have passed the validity criteria, and it is updated in real time during the statistical process to ensure data accuracy. The participant size threshold is a parameter set by the initiator when inputting group-buying constraint parameters, used to limit the maximum number of participants in the group-buying, and its value is consistent with the statistical dimension of the number of valid orders. The full amount trigger signal is a signal that follows a preset format, including trigger type, associated group-buying identifier, number of valid orders, and participant size threshold, used to notify the order aggregation component to start the order aggregation process.

[0089] (3) Perform order aggregation processing on timeout trigger signals or full amount trigger signals to generate merged order instructions.

[0090] For example, the order aggregation component receives timeout trigger signals or full-amount trigger signals through the signal receiving port, and then performs signal parsing logic: identifying the type of trigger signal, distinguishing between timeout trigger signals and full-amount trigger signals, then extracting the associated group-buying identifier carried in the signal, locating the corresponding group-buying task through the associated group-buying identifier, and confirming the group-buying range to which the orders to be aggregated belong. Based on the trigger signal type, the order aggregation component extracts valid order data for the corresponding range from the order data repository: if it is a timeout trigger signal, it extracts all valid order data for the group-buying task within the validity period of the dynamic ordering channel identifier; if it is a full-amount trigger signal, it extracts all valid order data for the group-buying task that has been completed so far.

[0091] After extraction, the order aggregation component retrieves the preset merged order data format from the data format configuration library. Following a rule of using the order's unique identifier as an index and maintaining a unified field order, it performs structured integration of the extracted valid order data: the order's unique identifier, menu customization information, and verified remarks fields for each valid order are arranged in a fixed order, and the data format is unified, including unified character encoding and field separators. Simultaneously, the amount information of all valid orders is summarized. Based on the integrated order data, a merged order instruction is generated, containing a summary of all valid order information. This summary information includes the order's unique identifier, menu customization information, verified remarks fields, and order amount for each order.

[0092] The order aggregation component is the core component responsible for receiving trigger signals, extracting valid order data, integrating order information, and generating a merge order instruction. The merge order data format is a preset standardized data format that specifies the field order, encoding method, delimiters, and other content of valid order data to ensure the consistency of the integrated data. The merge order instruction is an instruction that contains summary information of all valid orders. The summary information it carries can support the connection between the subsequent payment process and the catering service end, providing a data foundation for subsequent payment processing and food preparation.

[0093] like Figure 2 As shown, the order aggregation process is performed on timeout trigger signals or full-amount trigger signals to generate merged order instructions, including:

[0094] S201: Perform trigger event parsing and processing on timeout trigger signals or full-amount trigger signals to generate order aggregation execution instructions.

[0095] For example, upon receiving a timeout trigger signal or a full-amount trigger signal, the received signal is imported into the trigger event parsing unit. The trigger event parsing unit identifies the signal type, determining whether the received signal is a timeout trigger signal or a full-amount trigger signal. From the identified signal, a related group-buying identifier is extracted, and the target group-buying order requiring order aggregation is located using this identifier.

[0096] Based on the signal type, corresponding aggregation conditions are matched. If the signal is a timeout trigger signal, the aggregation condition of the expiration of the dynamic ordering channel identifier is matched; if the signal is a full-amount trigger signal, the aggregation condition of the number of valid orders reaching the participant scale threshold is matched. According to the preset execution instruction structure, the signal type, associated group-buying identifier, and matched aggregation conditions are integrated into structured data to generate an order aggregation execution instruction.

[0097] The trigger event parsing and processing includes a complete process of receiving timeout trigger signals or full-amount trigger signals, identifying the signal type, extracting associated group-buying identifiers, matching aggregation conditions, and integrating to generate order aggregation execution instructions. The order aggregation execution instructions are structured instructions carrying signal types, associated group-buying identifiers, and aggregation conditions; their function is to provide clear operational guidelines for the subsequent batch extraction of valid order data. The trigger event parsing unit is the functional unit responsible for processing timeout trigger signals or full-amount trigger signals; its core function is to transform the original trigger signals into clear guidelines required for subsequent order aggregation operations.

[0098] S202: Based on the order aggregation execution instruction, perform batch extraction and processing of the stored valid order data to generate the original order set.

[0099] For example, an order aggregation execution instruction is obtained, and the associated group-buying identifier and corresponding aggregation conditions are extracted from the instruction. The valid order data storage unit is accessed, and the valid order data storage directory corresponding to the extracted associated group-buying identifier is located within the valid order data storage unit. Valid order data within the storage directory is filtered according to the extracted aggregation conditions. If the aggregation condition is the expiration of the dynamic ordering channel identifier, all order data marked as valid within that expiration period is filtered. If the aggregation condition is that the number of valid orders reaches the participant scale threshold, all currently counted and marked as valid order data is filtered. Core fields are extracted from the filtered valid order data, including the order unique identifier, food customization information, verified remarks, and order amount. All extracted valid order data are integrated as individual order data units to generate the original order set.

[0100] The batch extraction process includes extracting key information from the order aggregation execution command, locating the valid order data storage directory, filtering valid order data, extracting core fields, and integrating them to generate the original order set. The original order set is a dataset composed of multiple independent and valid order data. Each data unit contains core content such as a unique order identifier, food customization information, verified remarks fields, and order amount, providing basic data support for subsequent structured integration.

[0101] S203: Perform structured integration processing on the original order set to generate a merge order instruction.

[0102] For example, a preset merged order structured integration standard is retrieved, which clearly specifies the order of order fields, data format requirements, and encoding methods. The original order set is imported into the structured integration unit, and each order data in the original order set is formatted uniformly according to the merged order structured integration standard to ensure that the field order of all order data is consistent, the data format meets the requirements, and the encoding method is uniform. All order data after format uniformity are categorized and summarized, and data with the same type of field are centrally statistically analyzed. For example, the order amount of all orders is summarized to obtain the total order amount, and the order quantity is counted to obtain the total order quantity.

[0103] Next, the categorized and summarized data undergoes integrity verification to check for issues such as missing fields or data conflicts. If problems are found, the process returns to the previous step for reprocessing; otherwise, the data is confirmed to be complete and valid. Following the preset order merge instruction format, the order data in a unified format and the categorized and summarized results are integrated into a complete data package, generating a merge order instruction.

[0104] The structured integration process includes retrieving the structured integration standard for merged orders, unifying the order data format, classifying and summarizing order fields, verifying data integrity, and generating merged order instructions. The merged order instruction is a complete data package containing unified format order data, total order quantity, and total order amount. It can be directly used to connect with the subsequent payment process and the catering service end, providing data support for subsequent payment processing and food preparation.

[0105] In one embodiment, the group-buying constraint parameters input by the initiator are processed to generate an identifier, which includes a dynamic ordering channel identifier that includes permission binding and time-limited control, including:

[0106] (1) Receive the group-buying constraint parameters input by the initiator. The group-buying constraint parameters include the initiator's unique identifier, the participant size threshold, the effective duration threshold, and the range of optional meals.

[0107] For example, the system receives group-buying constraint parameters input by the initiator. These parameters include the initiator's unique identifier, a participant size threshold, a valid duration threshold, and a range of selectable dishes. The received group-buying constraint parameters undergo an integrity check to ensure no parameters are missing. A validity check is then performed to confirm that the initiator's unique identifier conforms to the identity verification specifications, the participant size threshold is a valid positive integer, the valid duration threshold is a valid time parameter, and the range of selectable dishes is a set of dishes recognizable by the system. After successful verification, the group-buying constraint parameters are stored in the parameter storage unit.

[0108] Among them, the group-buying constraint parameters are a set of parameters input by the initiator to limit the group-buying rules. The integrity check is to check whether all parameters exist, and the legality check is to confirm that the parameter format and value conform to the system's preset rules.

[0109] (2) Perform timestamp generation processing on the real-time data of the system clock to generate the current timestamp.

[0110] For example, the system clock unit is invoked to obtain real-time data, providing high-precision time information synchronized with standard time. Based on the real-time data from the system clock, a current timestamp containing year, month, day, hour, minute, second, and millisecond information is generated according to preset timestamp generation rules, ensuring the uniqueness and accuracy of the timestamp.

[0111] The system clock unit provides real-time and accurate time data and supports time synchronization to ensure timestamp accuracy. The current timestamp is a string containing detailed time information and is used for subsequent composite encoding processing.

[0112] (3) Perform random factor generation processing on the preset random number generator to generate random factors.

[0113] For example, a preset random number generator is invoked. The generator generates a 6-character random string (i.e., a random factor) consisting of numbers and letters according to preset random character generation rules. After generation, the random factor is uniquely verified to ensure it is not repeated within the system. Once the verification passes, the random factor is stored in a temporary storage unit.

[0114] The preset random number generator is a pre-configured tool for generating random strings, and the random factor is a 6-character random string used to increase the uniqueness of the encoding.

[0115] (4) Use the following formula to perform composite encoding processing on the current timestamp and random factor to generate a globally unique QR code:

[0116]

[0117] in, This indicates a globally unique QR code. This represents a composite coding function. Indicates the current timestamp. This indicates that the 6 characters are a random string. This indicates a string concatenation operation.

[0118] For example, calling the composite coding function The current timestamp and random factor are concatenated according to string concatenation rules to generate the string to be encoded. This string is then input into the composite encoding function. The composite encoding function converts a string into QR code graphic data according to the QR code encoding rules, generating a globally unique QR code. .

[0119] Among them, the composite coding function This function converts a string into a QR code. The string concatenation operation appends the current timestamp and a random factor to form a single, globally unique QR code. It is a QR code graphic data containing timestamp and random factor information.

[0120] (5) Based on the effective duration threshold, the globally unique QR code is time-bound to generate a dynamic ordering channel identifier.

[0121] For example, the effective duration threshold is extracted from the parameter storage unit, and the effective duration threshold is compared with a globally unique QR code. Establish a binding mechanism and set the validity period for the globally unique QR code. Simultaneously, bind the initiator's unique identifier to the globally unique QR code for access control, restricting access to the ordering channel to only the initiator and authorized participants. After completing the time-limited and access control binding, a dynamic ordering channel identifier containing access control information, time-limited information, and the QR code image is generated.

[0122] Among them, the effective duration threshold is a parameter used to limit the effective time of the dynamic ordering channel identifier, the time binding is to set the available time range of the identifier, and the permission binding is to limit the scope of objects that can use the identifier; the dynamic ordering channel identifier is an identifier that includes permissions, time validity and QR code, and is used to open the ordering interaction entry.

[0123] In one embodiment, unique conflict verification and user modification guidance are performed on the personalized remarks field to generate an order dataset including a unique identifier, menu customization information, and verified remarks fields, including:

[0124] (1) Receive personalized remarks fields submitted by users who place orders.

[0125] For example, the system receives personalized remarks submitted by users through the ordering interface, performs a format compliance check on the personalized remarks field, and checks whether the character type meets the requirements and whether the length is within the preset range. After the format compliance check passes, the personalized remarks field is temporarily stored in a temporary data area.

[0126] Among them, the personalized remarks field is a custom text information used by ordering users to distinguish their own meals, the format compliance check is to ensure that it conforms to the specifications that the system can process, and the temporary data area is a storage area used to temporarily store information to be processed.

[0127] (2) Calculate the hash identifier value of the personalized remarks field.

[0128] For example, a preset hash calculation tool is invoked, importing the personalized note fields from the temporary data area as input. The preset hash calculation tool processes the personalized note fields according to a preset hash algorithm, generating a fixed-length hash identifier value for each personalized note field. After generation, the hash identifier value is associated with the corresponding personalized note field and stored in the note hash library.

[0129] Among them, the hash identifier value is a fixed-length string obtained by performing a hash algorithm on the personalized note field, which is used to quickly determine the uniqueness of the note field; the preset hash calculation tool is a pre-configured functional unit for generating hash values; and the note hash library is a database that stores historical note hash values.

[0130] (3) When a conflict is detected between the hash identifier value of the personalized remarks field and the existing remarks hash value, a conflict prompt is sent to the user terminal and a graphical interface modification is forced to generate an order dataset that meets the uniqueness condition.

[0131] For example, the system iterates through all existing memo hash values ​​in the memo hash database and compares the hash identifier value of the currently generated personalized memo field with each existing memo hash value. If a duplicate hash identifier value is found, it is determined that the hash identifier value of the personalized memo field conflicts with an existing memo hash value. At this point, a conflict prompt message is sent to the user's interactive interface, indicating that the personalized memo field is duplicated, and the memo editing unit of the user's graphical interface is forcibly activated, guiding the user to re-enter the personalized memo field.

[0132] After the user re-enters the information, the calculation and comparison of the hash value of the personalized remarks field are repeated until the generated hash value is unique in the remarks hash database, thus obtaining a personalized remarks field that meets the uniqueness requirement. Next, the order's unique identifier and menu customization information corresponding to the user are retrieved, and these elements, along with the verified remarks field, are integrated into a data unit according to a preset data structure. All data units that meet the requirements are then aggregated to generate an order dataset including the unique identifier, menu customization information, and verified remarks field.

[0133] Among them, the stored note hash value is the hash identifier value of the historical personalized note field stored in the note hash library; the conflict prompt message is the prompt content informing the user that the note is duplicated; the graphical interface modification refers to the note editing function area provided in the user interface; the order dataset is a collection of multiple data units containing a unique order identifier, food customization information, and verified note fields, which are used in subsequent order processing.

[0134] In one embodiment, based on the obtained payment mode selection result and order merging instruction, payment coordination processing is performed on the order dataset to generate a payment status mapping table and an order submission instruction, including:

[0135] (1) Based on the payment mode selection result and the order merging instruction, the order dataset is processed for payment status tracking to generate payment status data.

[0136] For example, the payment mode selection result and the order merging instruction are obtained, the current payment mode type is determined from the payment mode selection result, and the associated order dataset range and core order information are extracted from the order merging instruction.

[0137] Based on the determined payment mode type, a corresponding payment tracking identifier is configured for each order in the order dataset. Under the unified payment mode initiated by the initiator, a shared payment tracking identifier is configured for all orders; under the multi-person independent payment mode, an independent payment tracking identifier is configured for each order. The payment interface is integrated to obtain the payment status of each order in real time through the payment tracking identifier, and the update time corresponding to the payment status is recorded. The obtained payment status, update time, and corresponding order unique identifier and payment tracking identifier are associated and stored to generate payment status data.

[0138] The payment mode selection result includes information about the payment method chosen by the order initiator or participants; the order merging instruction contains order aggregation information; the payment tracking identifier is used to link the order and payment status; and the payment status data is structured data containing a unique order identifier, payment tracking identifier, payment status, and update time. Payment mode types include a unified payment mode by the initiator or an independent payment mode for multiple participants.

[0139] (2) The payment status data and preset payment trigger conditions are judged and processed to generate a payment status mapping table and an order submission instruction.

[0140] For example, preset payment trigger conditions are retrieved. These preset payment trigger conditions are differentiated based on the payment mode type. In the unified payment mode initiated by the party, the payment status corresponding to the shared payment tracking identifier changes to "paid." In the independent payment mode for multiple parties, the payment status changes to "paid" when the number of paid orders reaches a preset percentage or when the payment status of all orders changes to "paid." The generated payment status data is compared one by one with the preset payment trigger conditions. In the unified payment mode initiated by the party, the payment status of the shared payment tracking identifier is checked to see if it meets the conditions. In the independent payment mode for multiple parties, the number of paid orders is counted and its compliance with the conditions is determined.

[0141] If the comparison result meets the preset payment trigger conditions, the order unique identifier, payment tracking identifier, payment status, and update time are organized according to a preset format to generate a payment status mapping table. Simultaneously, according to the order submission instruction format, the order unique identifier, food customization information, verified remarks fields, and related information from the payment status mapping table in the order dataset are integrated to generate an order submission instruction. If the comparison result does not meet the preset payment trigger conditions, the payment status data is continuously monitored for updates until the conditions are met, at which point the above generation operations are executed.

[0142] Among them, the preset payment trigger condition is a pre-set standard for determining payment completion, the payment status mapping table is a table that records the correspondence between orders and payment status, and the order submission instruction is an instruction used to trigger subsequent steps such as food preparation and delivery, which includes core order information and payment status association information.

[0143] In one embodiment, the method further includes:

[0144] (1) Extract the configuration of the records stored in the historical group order database to generate the preset delivery address and preset meal configuration. The preset delivery address and preset meal configuration are used as the input values ​​of the delivery address field and meal range field of the group order constraint parameters.

[0145] For example, the system accesses the historical group-buying database and extracts all completed group-buying records. It then filters these records, identifying high-quality records with a group-buying frequency exceeding a preset frequency threshold and user ratings exceeding a preset rating threshold. From these high-quality records, it extracts delivery address and food selection information. The delivery address information is then analyzed for repetition frequency, and the delivery address with the highest repetition frequency is identified as the preset delivery address. Similarly, the food selection information is analyzed for category frequency, and the set of food categories with the highest frequency ranking is identified as the preset food configuration. The preset delivery address and preset food configuration are then used as input values ​​for the delivery address field and food range field of the group-buying constraint parameters, respectively.

[0146] Among them, the historical group-buying database is a database that stores all past group-buying records; the delivery address field of the group-buying constraint parameters is a parameter that limits the delivery location of the group-buying order; the food range field of the group-buying constraint parameters is a parameter that limits the food available in the group-buying order; the preset delivery address is a high-frequency delivery address determined from high-quality group-buying records; and the preset food configuration is a set of high-frequency food categories determined from high-quality group-buying records.

[0147] (2) Connect to the application programming interface of the office software open platform to perform member scope restriction processing and generate a set of access permissions for limited group members. The access permission set is used to control the access permissions of the dynamic ordering channel identifier.

[0148] For example, access permissions are obtained to the application programming interface (API) of the office software open platform, and the member list data of the target group is retrieved through this API. The retrieved member list data is then authenticated to verify whether members are official members of the target group, excluding temporary visitors or members who have left the group. Based on the group's group-buying needs, the permission scope of the access permission set is set, including permissions to view the dynamic ordering channel identifier, order submission permissions, and payment status viewing permissions. Verified official members are associated with the set of access permissions, and each official member is assigned a corresponding permission item. The associations between all members and permission items are integrated to generate a limited access permission set for group members, used to control access permissions to the dynamic ordering channel identifier.

[0149] Among them, the Office Software Open Platform Application Programming Interface is an interface used to retrieve group data within the office software. The target group is the specific office group that initiated the group purchase. The access permission set is a collection containing various permission items. The access permission of the dynamic ordering channel identifier controls whether members can operate the identifier.

[0150] (3) Perform permission allocation processing on the data of the assistant administrator's assignment instructions to generate the assistant administrator's management permission configuration.

[0151] For example, the system receives assignment instructions from assistant administrators and extracts the assigned assistant's identity and the scope of the group-buying task from this data. A pre-defined management permission template is retrieved, which includes basic permissions such as order review permissions, payment exception handling permissions, delivery progress coordination permissions, and conflict arbitration permissions. Based on the group-buying task scope in the assistant administrator assignment instructions, the basic permission items in the management permission template are filtered and adjusted, retaining only those matching the task scope and removing irrelevant ones. The adjusted permission items are then linked to the assigned assistant's identity, clarifying the permissions each assistant can perform. Finally, the association information between the assistant's identity and the corresponding permission items is integrated to generate the assistant administrator management permission configuration.

[0152] Among them, the assistant administrator assignment instruction data is instruction data that includes the identity and task scope of the assigned assistant administrator, the group-buying task scope is the group-buying business scope that limits the assistant administrator's management responsibilities, the management permission template is a preset template that includes basic management permissions, and the assistant administrator management permission configuration is a configuration file that clarifies the scope of assistant administrator permissions.

[0153] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0154] In one embodiment, such as Figure 3 As shown, this application also provides an order processing device 300 for group meal ordering scenarios, the device 300 including:

[0155] The dynamic QR code generation module 301 is used to process the group-buying constraint parameters input by the initiator and generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes.

[0156] The order receiving and verification module 302 is used to process orders based on the dynamic ordering channel identifier, perform unique conflict verification of personalized remarks fields and guide users to modify them, and generate an order dataset including unique identifiers, food customization information and verified remarks fields.

[0157] The aggregation module 303 is used to perform trigger condition judgment processing on the order dataset, and generate a merge order instruction when the preset conditions are met.

[0158] The payment coordination module 304 is used to perform payment coordination processing on the order dataset based on the obtained payment mode selection results and order merging instructions, and generate a payment status mapping table and order submission instructions;

[0159] The remarks mapping generation module 305 is used to respond to the order submission command, perform food distribution remarks matching processing on the order dataset, and generate a mapping relationship between personalized remarks and order instances;

[0160] The food label generation module 306 is used to generate food items with annotation labels through the catering service terminal based on the mapping relationship;

[0161] The delivery tracking module 307 is used to track the status of meals with notes and generate delivery progress information synchronized by all staff.

[0162] Specifically, the dynamic QR code generation module 301 receives group-buying constraint parameters input by the initiator. These parameters include the initiator's unique identifier, participant size threshold, validity period threshold, and range of selectable dishes. The module performs completeness and validity checks on the received constraint parameters to ensure that each parameter is complete and conforms to system specifications. After successful verification, it calls a timestamp generation tool to generate the current timestamp and a random factor generation tool to generate a random factor. A composite encoding function is used to encode the current timestamp and random factor to generate a globally unique QR code. The validity period threshold is time-bound to the globally unique QR code, and the initiator's unique identifier is permission-bound to the globally unique QR code, generating a dynamic ordering channel identifier that includes permission binding and validity period control.

[0163] The order receiving and verification module 302 receives order information submitted by users through a dynamic ordering channel identifier. This order information includes personalized remarks fields and menu customization information. The module performs a format compliance check on the received personalized remarks fields, temporarily storing them in a temporary storage area after passing the check. It then uses a hash calculation tool to calculate the hash identifier value of the personalized remarks field and compares this hash identifier value with existing hash values ​​in the remarks hash database. If a conflict is detected, a conflict warning is sent to the user's client, and the graphical interface modification function is forcibly activated, guiding the user to re-enter the information. This process continues until a conflict-free hash identifier value is generated, confirming the verified remarks field. A unique order identifier is assigned to the order, and the unique order identifier, menu customization information, and verified remarks fields are integrated into a structured data unit. Finally, all structured data units are aggregated to generate an order dataset including the unique identifier, menu customization information, and verified remarks fields.

[0164] The aggregation module 303 extracts the number of valid orders from the order dataset and simultaneously obtains the current time and the generation time of the dynamic ordering channel identifier. It performs timeliness calculations on the current time and the generation time of the dynamic ordering channel identifier; if the time difference exceeds the valid duration threshold, a timeout trigger signal is generated. It also compares the number of valid orders with the participant size threshold; if the number of valid orders reaches the participant size threshold, a full-amount trigger signal is generated. Upon receiving either a timeout trigger signal or a full-amount trigger signal, the order aggregation logic is initiated, extracting all valid order data from the order dataset. The valid order data is then integrated according to a preset aggregation format to generate a merged order instruction.

[0165] The payment collaboration module 304 retrieves the payment mode selection result and the order merging instruction, extracting the association information of the order dataset from the order merging instruction. Based on the payment mode selection result, it configures payment tracking rules: a shared payment tracking identifier is configured under the unified payment mode for the initiator, and an independent payment tracking identifier is configured for each order under the multi-person independent payment mode. It connects to the payment interface to obtain the payment status and update time of each order in real time through the payment tracking identifier, generating payment status data. It retrieves preset payment trigger conditions and compares the payment status data with the preset payment trigger conditions. If the conditions are met, it associates the order's unique identifier with the corresponding payment status and update time to generate a payment status mapping table, and simultaneously integrates the core order information to generate an order submission instruction.

[0166] The remarks mapping generation module 305 receives the order submission instruction and parses the order dataset association information contained in the instruction. It extracts the order instance information for each order instance from the order dataset and retrieves the matching rules for food distribution remarks against the verified remarks fields. These rules restrict each verified remarks field to be bound to only one order instance. Based on the matching rules, it associates and binds each verified remarks field with its corresponding order instance information. Following a preset mapping data structure, it structures and organizes all the associated and bound information to generate a personalized remarks-order instance mapping relationship.

[0167] The food label generation module 306 obtains the mapping relationship between personalized notes and order instances and transmits this mapping relationship to the catering service. The catering service parses the mapping relationship and extracts the food information and verified note fields corresponding to each order instance. It generates a unique note label based on the verified note fields, ensuring that the note label uniquely matches the verified note fields. The note label is then bound to the food preparation task of the corresponding order instance, specifying the note label that must be attached to each food preparation task. The catering service executes the preparation process according to the food preparation tasks bound to the note labels, adding the note labels to the food or packaging, generating food with the note labels.

[0168] The delivery tracking module 307 interfaces with the food status collection module on the catering service side to obtain real-time status data for food items with annotations. It associates and binds the real-time status data with the corresponding food items with annotations to ensure data matching. It retrieves a preset delivery progress information format, which includes annotations, current status, update time, and estimated milestone time. The associated status data is structured according to the format to generate initial delivery progress information. A synchronized information push channel is established for all participants, connecting to their interactive terminals. The module monitors changes in food status in real time; if the status is updated, the delivery progress information is updated and synchronized to all participants via the push channel, generating synchronized delivery progress information for all participants.

[0169] The trigger aggregation module 303 is also used for:

[0170] Perform timeliness calculations on the current time and the generation time of the dynamic ordering channel identifier, and generate a timeout trigger signal;

[0171] The number of valid orders in the order dataset is numerically compared with the participant size threshold to generate a full-amount trigger signal.

[0172] Orders are aggregated based on timeout or full-amount trigger signals to generate merged order instructions.

[0173] The trigger aggregation module 303 is also used for:

[0174] The system parses and processes timeout or full-amount trigger signals to generate order aggregation execution instructions.

[0175] Based on the order aggregation execution instruction, the stored valid order data is extracted in batches to generate the original order set;

[0176] The original order set is structured and integrated to generate a merge order instruction.

[0177] The dynamic QR code generation module 301 is also used for:

[0178] Receive group-buying constraint parameters input by the initiator. The group-buying constraint parameters include the initiator's unique identifier, participant size threshold, effective duration threshold, and range of selectable meals.

[0179] Perform timestamp generation processing on the real-time data of the system clock to generate the current timestamp;

[0180] The preset random number generator is subjected to random factor generation processing to generate random factors;

[0181] The following formula is used to perform a composite encoding process on the current timestamp and the random factor to generate a globally unique QR code:

[0182]

[0183] in, This indicates a globally unique QR code. This represents a composite coding function. Indicates the current timestamp. This indicates that the 6 characters are a random string. This represents a string concatenation operation;

[0184] Based on the effective duration threshold, the globally unique QR code is time-bound to generate a dynamic ordering channel identifier.

[0185] The order receiving and verification module 302 is also used for:

[0186] Receive personalized remarks submitted by users who place orders;

[0187] Calculate the hash identifier value of the personalized remarks field;

[0188] When a conflict is detected between the hash identifier value of the personalized remarks field and the existing remarks hash value, a conflict prompt is sent to the user and a graphical interface modification is forced to generate an order dataset that meets the uniqueness condition.

[0189] The payment coordination module 304 is also used for:

[0190] Based on the payment mode selection results and order merging instructions, the order dataset is processed for payment status tracking to generate payment status data.

[0191] The system processes and judges the payment status data and preset payment trigger conditions to generate a payment status mapping table and an order submission instruction.

[0192] In one embodiment, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0193] In one embodiment, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0194] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0195] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. An order processing method for group meal ordering scenarios, characterized in that, The method includes: The group-buying constraint parameters input by the initiator are processed to generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes. The dynamic ordering channel identifier is processed for order reception, and unique conflict verification of personalized remarks fields and user modification guidance are performed to generate an order dataset including unique identifiers, food customization information and verified remarks fields. The order dataset is processed to determine trigger conditions, and a merge order instruction is generated when preset conditions are met. Based on the obtained payment mode selection result and the merged order instruction, the order dataset is processed for payment coordination to generate a payment status mapping table and an order submission instruction; In response to the order submission instruction, the order dataset is processed for matching food distribution notes to generate a mapping relationship between personalized notes and order instances; Based on the mapping relationship, dishes with annotations are generated through the catering service terminal; The status of the meals marked with remarks is tracked to generate delivery progress information synchronized across all staff.

2. The order processing method for group meal ordering scenarios according to claim 1, characterized in that, The step of performing trigger condition judgment processing on the order dataset, and generating a merge order instruction when a preset condition is met, includes: Perform timeliness calculation processing on the current time and the generation time of the dynamic ordering channel identifier, and generate a timeout trigger signal; The number of valid orders in the order dataset is compared with the participant size threshold to generate a full-amount trigger signal. The timeout trigger signal or the full amount trigger signal is processed to aggregate orders and generate the merged order instruction.

3. The order processing method for group meal ordering scenarios according to claim 2, characterized in that, The step of performing order aggregation processing on the timeout trigger signal or the full amount trigger signal to generate the merged order instruction includes: The timeout trigger signal or the full amount trigger signal is parsed and processed to generate an order aggregation execution instruction; Based on the order aggregation execution instruction, the stored valid order data is extracted in batches to generate the original order set; The original order set is structured and integrated to generate the merged order instruction.

4. The order processing method for group meal ordering scenarios according to claim 1, characterized in that, The step of processing the group-buying constraint parameters input by the initiator to generate a dynamic ordering channel identifier that includes permission binding and time-limited control includes: The system receives group-buying constraint parameters input by the initiator, which include the initiator's unique identifier, participant size threshold, effective duration threshold, and range of selectable meals. Perform timestamp generation processing on the real-time data of the system clock to generate the current timestamp; The preset random number generator is subjected to random factor generation processing to generate random factors; The current timestamp and the random factor are combined and encoded using the following formula to generate a globally unique QR code: in, This indicates a globally unique QR code. This represents a composite coding function. Indicates the current timestamp. This indicates that the 6 characters are a random string. This represents a string concatenation operation; Based on the effective duration threshold, the globally unique QR code is time-bound to generate the dynamic ordering channel identifier.

5. The order processing method for group meal ordering scenarios according to claim 1, characterized in that, The process of performing unique conflict verification and user modification guidance on personalized remarks fields generates an order dataset including unique identifiers, menu customization information, and verified remarks fields, including: Receive personalized remarks submitted by users who place orders; Calculate the hash identifier value of the personalized remarks field; When a conflict is detected between the hash identifier value of the personalized remarks field and the existing remarks hash value, a conflict prompt is sent to the user terminal and a graphical interface modification is forced to generate the order dataset that meets the uniqueness condition.

6. The order processing method for group meal ordering scenarios according to claim 1, characterized in that, Based on the obtained payment mode selection result and the merged order instruction, the order dataset undergoes payment coordination processing to generate a payment status mapping table and an order submission instruction, including: Based on the payment mode selection result and the merged order instruction, the order dataset is processed for payment status tracking to generate payment status data; The payment status data and preset payment trigger conditions are judged and processed to generate the payment status mapping table and order submission instruction.

7. The order processing method for group meal ordering scenarios according to claim 1, characterized in that, The method further includes: The records stored in the historical group-buying database are processed to extract configurations and generate preset delivery addresses and preset meal configurations. The preset delivery addresses and preset meal configurations are used as input values ​​for the delivery address field and meal range field of the group-buying constraint parameters. The application programming interface of the office software open platform is used to limit the scope of members and generate a set of access permissions for limited group members. The access permission set is used to control the access permissions of the dynamic ordering channel identifier. The system performs permission allocation processing on the data of the assignment instructions to the assistant administrators, and generates the management permission configuration for the assistant administrators.

8. An order processing device for group meal ordering scenarios, characterized in that, The device includes: The dynamic QR code generation module is used to process the group-buying constraint parameters input by the initiator and generate a dynamic ordering channel identifier that includes permission binding and time limit control. The group-buying constraint parameters include the initiator's unique identifier, the participant scale threshold, the effective duration threshold, and the range of selectable dishes. The order receiving and verification module is used to process orders based on the dynamic ordering channel identifier, perform unique conflict verification of personalized remarks fields and guide users to modify them, and generate an order dataset including unique identifiers, customized meal information and verified remarks fields. The trigger aggregation module is used to perform trigger condition judgment processing on the order dataset, and generate a merge order instruction when the preset conditions are met; The payment coordination module is used to perform payment coordination processing on the order dataset based on the obtained payment mode selection result and the merged order instruction, and generate a payment status mapping table and an order submission instruction; The remarks mapping generation module is used to respond to the order submission instruction, perform food distribution remarks matching processing on the order dataset, and generate a mapping relationship between personalized remarks and order instances; The food label generation module is used to generate food items with annotation labels through the catering service terminal based on the mapping relationship. The delivery tracking module is used to track the status of the meals marked with remarks and generate delivery progress information synchronized by all staff.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the order processing method in the group meal ordering scenario as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the order processing method in the group meal ordering scenario as described in any one of claims 1 to 7.

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