Product order sequence generation method and device and computer equipment

Abstract

The invention relates to a product order sequence generation method and device and computer equipment. The method comprises the steps of obtaining product order information of each user, and splitting each piece of product order information into order demand information of each user, an order time type of each user and an order time point of each user; based on the order demand information of each user, identifying product feature information of each product demand type of each user through an order feature analysis strategy, and based on the order time point of each user, the order time type of each user and the product feature information of each product demand type of each user, identifying the product feature information of each product demand type of each user; generating an order generation sequence; and identifying sequence abnormal information of the order generation sequence, and adjusting the sequence abnormal information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence. By adopting the method, the processing efficiency of all orders in order of a large number of products can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of order management, and in particular to a method, apparatus, and computer device for generating a product order sequence. Background Art

[0002] In modern society, with the advancement of technology and the accelerated pace of life, technologies such as intelligent manufacturing systems, order management systems, and liquid dispensing devices are gaining widespread application in the catering industry. Intelligent manufacturing systems can automate, intelligentize, and inform the production process, improving production efficiency and product quality. Order management systems can rapidly process and track customer orders, enhancing customer satisfaction. Therefore, improving efficient order management and thereby enhancing the user experience is a current research focus.

[0003] Traditional order management methods are implemented through order classification and sorting. When generating actual orders, the order placement time and order quantity are often the primary considerations for staff. Furthermore, when sorting and managing orders, each order is manually analyzed and adjusted to achieve sorting and management of each user's orders. However, this solution has poor control effectiveness, resulting in uneven order delivery rates, affecting customer wait times. This is especially true when processing large orders, which can easily lead to confusion and errors, resulting in poor order processing efficiency for large orders. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for generating a product order sequence in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for generating a product order sequence, comprising:

[0006] Obtain product order information of each user, and split each product order information into order demand information of each user, order time type of each user, and order time point of each user;

[0007] Based on the order demand information of each user, identifying the product feature information of each product demand type of each user through an order feature analysis strategy, and generating an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user;

[0008] Identify sequence anomaly information of the order generation sequence, and adjust the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

[0009] Optionally, identifying product feature information of each product demand type of each user based on the order demand information of each user through an order feature analysis strategy includes:

[0010] For each user, split the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, identify the order source type of the product demand type, product quantity information of the product demand type, and material demand information of the product demand type based on the sub-order demand information of the product demand type;

[0011] Based on the order source type of the product demand type, identifying the urgency of the product of the product demand type, and based on the product quantity information of the product demand type and the material requirement information of the product demand type, calculating the predicted product production time of each sub-order of the product demand type through a product generation algorithm;

[0012] The product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type are used as product feature information of the product demand type.

[0013] Optionally, generating an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user includes:

[0014] generating an initial user sequence based on the order time points of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence;

[0015] For each user, based on the predicted product production time of each sub-order of each product demand type of the user, according to the order optimization sorting strategy, a product production sequence for each user is generated, and the product production sequence of each user is sequenced according to the user sequence to obtain an initial order generation sequence;

[0016] Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the order time adjustment analysis strategy is used to identify the order time adjustment range corresponding to each user, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, the order generation position information of each product demand type of each user in the initial order generation sequence is sequence adjusted to obtain an order generation sequence.

[0017] Optionally, the identifying sequence anomaly information of the order generation sequence includes:

[0018] The order generation sequence is divided into sub-order generation sequences for each generation period using a time period division strategy. Based on the predicted product production durations of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence, the product generation frequency and the average product production duration of each sub-order generation sequence are calculated.

[0019] In each of the sub-order generation sequences, the sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time are screened as abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are used as sequence abnormality information of the order generation sequence.

[0020] Optionally, adjusting the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence includes:

[0021] For each abnormal sub-order generation sequence, extract the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence and the abnormal user corresponding to each abnormal sub-order;

[0022] Based on the predicted product production time of each abnormal sub-order and the abnormal user corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network;

[0023] The order generation sequence is replaced by the new order generation sequence, and the process returns to executing the time period division strategy to divide the order generation sequence into sub-order generation sequence steps for each generation period, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time. The new order generation sequence obtained from the last iteration is used as the target order generation sequence.

[0024] Optionally, the method further includes:

[0025] Based on the target order generation sequence, the intelligent liquid dispensing machine is controlled to execute the product generation task corresponding to each user's sub-order, and user feedback information of each user and order generation status information of each sub-order are collected;

[0026] Based on the order generation status of each child order and the user feedback information of each user, a sequence adjustment process is performed on the unexecuted current order generation sequence in the target order generation sequence to obtain a current new order generation sequence, and the current new order generation sequence replaces the target order generation sequence;

[0027] Return to execute based on the target order generation sequence, control the intelligent liquid dispensing machine, and execute the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

[0028] In a second aspect, the present application further provides a device for generating a product order sequence, comprising:

[0029] An acquisition module is used to acquire product order information of each user, and split each product order information into order demand information of each user, order time type of each user, and order time point of each user;

[0030] a generation module configured to identify, based on the order demand information of each user and using an order feature analysis strategy, product feature information of each product demand type of each user, and generate an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user;

[0031] The identification module is used to identify sequence anomaly information of the order generation sequence, and adjust the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

[0032] Optionally, the generating module is specifically configured to:

[0033] For each user, split the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, identify the order source type of the product demand type, product quantity information of the product demand type, and material demand information of the product demand type based on the sub-order demand information of the product demand type;

[0034] Based on the order source type of the product demand type, identifying the urgency of the product of the product demand type, and based on the product quantity information of the product demand type and the material requirement information of the product demand type, calculating the predicted product production time of each sub-order of the product demand type through a product generation algorithm;

[0035] The product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type are used as product feature information of the product demand type.

[0036] Optionally, the generating module is specifically configured to:

[0037] generating an initial user sequence based on the order time points of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence;

[0038] For each user, based on the predicted product production time of each sub-order of each product demand type of the user, according to the order optimization sorting strategy, a product production sequence for each user is generated, and the product production sequence of each user is sequenced according to the user sequence to obtain an initial order generation sequence;

[0039] Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the order time adjustment analysis strategy is used to identify the order time adjustment range corresponding to each user, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, the order generation position information of each product demand type of each user in the initial order generation sequence is sequence adjusted to obtain an order generation sequence.

[0040] Optionally, the identification module is specifically configured to:

[0041] The order generation sequence is divided into sub-order generation sequences for each generation period using a time period division strategy. Based on the predicted product production durations of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence, the product generation frequency and the average product production duration of each sub-order generation sequence are calculated.

[0042] In each of the sub-order generation sequences, the sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time are screened as abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are used as sequence abnormality information of the order generation sequence.

[0043] Optionally, the identification module is specifically configured to:

[0044] For each abnormal sub-order generation sequence, extract the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence and the abnormal user corresponding to each abnormal sub-order;

[0045] Based on the predicted product production time of each abnormal sub-order and the abnormal user corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network;

[0046] The order generation sequence is replaced by the new order generation sequence, and the process returns to executing the time period division strategy to divide the order generation sequence into sub-order generation sequence steps for each generation period, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time. The new order generation sequence obtained from the last iteration is used as the target order generation sequence.

[0047] Optionally, the device further includes:

[0048] A collection module is used to control the intelligent liquid dispensing machine based on the target order generation sequence to execute the product generation task corresponding to each user's sub-order, and collect user feedback information of each user and order generation status information of each sub-order;

[0049] an adjustment module configured to perform sequence adjustment processing on the unexecuted current order generation sequence in the target order generation sequence based on the order generation status of each sub-order and user feedback information of each user, to obtain a current new order generation sequence, and replace the target order generation sequence with the current new order generation sequence;

[0050] The iteration module is used to return to execute based on the target order generation sequence, control the intelligent liquid dispensing machine, and execute the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

[0051] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0052] In a fourth aspect, the present application 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 one of the methods in the first aspect.

[0053] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0054] The above-mentioned method, device and computer equipment for generating a product order sequence obtain the product order information of each user and split the product order information into the order demand information of each user, the order time type of each user, and the order time point of each user; based on the order demand information of each user, the product feature information of each product demand type of each user is identified through an order feature analysis strategy, and based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user, an order generation sequence is generated; the sequence abnormality information of the order generation sequence is identified, and the sequence abnormality information of the order generation sequence is adjusted through an order sequence optimization adjustment strategy to obtain a target order generation sequence. When generating the order generation sequence of a product, this solution starts from three aspects, namely, the order time type, order time point, and order demand information of each user, and comprehensively considers the product feature information of each product demand type of different users to generate an order generation sequence, so that the order requirements of different orders are classified and sorted, which can improve the efficiency of order processing, reduce waiting time, and improve customer satisfaction. Secondly, this solution identifies the sequence anomaly information in the order generation sequence generated by the above solution, and optimizes and adjusts the order generation sequence based on different anomaly information, so that the optimized order generation sequence can not only meet the waiting time requirements of a large number of users, but also control the equipment to deliver meals in a balanced manner, thereby effectively improving the order processing efficiency of a large number of orders, reducing the actual waiting time of each user, and improving the user experience, thereby effectively improving the efficiency of all order processing for a large number of product orders. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 1 is a flow chart of a method for generating a product order sequence in one embodiment;

[0057] Figure 2 A schematic diagram of a process for generating a product order sequence in one embodiment;

[0058] Figure 3 is a structural block diagram of a device for generating a product order sequence in one embodiment;

[0059] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0061] The method for generating a product order sequence provided in the embodiment of the present application can be applied to the control module of an intelligent liquid dispenser. The control module can be a terminal, which can be, but is not limited to, various personal computers, laptops, mid-range computers, etc. When generating the product order generation sequence, the terminal starts from three aspects, namely, the order time type, order time point, and order demand information of each user, and comprehensively considers the product feature information of each product demand type of different users to generate an order generation sequence, so that the order demand of different orders can be classified and sorted, which can improve the efficiency of order processing, reduce waiting time, and improve customer satisfaction. Secondly, this solution identifies the sequence abnormality information in the order generation sequence generated by the above solution, and optimizes and adjusts the order generation sequence based on different abnormal information, so that the optimized order generation sequence can not only meet the waiting time requirements of a large number of users, but also control the equipment to evenly serve meals, thereby effectively improving the efficiency of order processing for a large number of orders, reducing the actual waiting time of each user, and improving the user experience, thereby effectively improving the efficiency of all order processing when placing a large number of product orders.

[0062] In an exemplary embodiment, Figure 1 As shown, a method for generating a product order sequence is provided, which is described by taking the method applied to a terminal as an example, and includes the following steps S101 to S103.

[0063] Step S101 , obtaining product order information of each user, and dividing each product order information into order demand information of each user, order time type of each user, and order time point of each user.

[0064] In this embodiment, the terminal receives the order content sent by each user through multiple channels and obtains the product order information of each user. Among them, the product order information includes the sub-order information of each product required by the user, wherein each sub-order information includes the order time point, the order time type, and the order requirement information, wherein the order time type includes but is not limited to the instant order type and the reservation order type, and the order requirement information includes the material requirement information (product material ratio, product additional material addition and other requirement information, temperature status, ice cube ratio status, product mixing status, i.e., whether it is shaken), etc. Among them, each sub-order corresponds to a product requirement type, and each product requirement type ordered by each user can correspond to one or more sub-orders, and the product requirement type is the product type of each product that the smart liquid dispenser can produce. And the multiple channels correspond to the order source type of each user's order, wherein the order source type is but is not limited to the POS order type (i.e., on-site scan code payment order), the mini program / APP order type (i.e., internal link order), and the takeaway order type (delivery order of a third party or internal platform).

[0065] Step S102: Based on the order demand information of each user, the product feature information of each product demand type of each user is identified through the order feature analysis strategy, and an order generation sequence is generated based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user.

[0066] In this embodiment, the terminal identifies the product feature information of each product demand type of each user based on the order demand information of each user through an order feature analysis strategy, and generates an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user. Among them, the product feature information of each product demand type includes the product urgency of each product demand type and the predicted product production time of each sub-order of each product demand type. The specific identification process will be described in detail later, and the order generation sequence includes the generation sequence between all sub-orders of each user. Among them, the sub-orders of the same user may be adjacent or non-adjacent in the order generation sequence. The specific generation process will be described in detail later.

[0067] Step S103 , identifying sequence anomaly information of the order generation sequence, and adjusting the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

[0068] In this embodiment, the terminal identifies sequence anomalies within an order generation sequence and, through an order sequence optimization and adjustment strategy, adjusts the anomaly information within the order generation sequence to obtain a target order generation sequence. The anomaly information refers to sub-order generation sequences corresponding to time periods with low order delivery rates and / or long customer wait times. The order sequence optimization and adjustment strategy optimizes and adjusts each sub-order within the order generation sequence to eliminate issues such as uneven delivery rates and long customer wait times within the order generation sequence. The specific adjustment process will be described in detail later.

[0069] Based on the above scheme, when generating the order generation sequence of the product, starting from the three aspects of each user's order time type, order time point, and order demand information, the product feature information of each product demand type of different users is comprehensively considered to generate the order generation sequence, so that the order requirements of different orders can be classified and sorted, which can improve the efficiency of order processing, reduce waiting time, and improve customer satisfaction. Secondly, this scheme identifies the sequence abnormality information in the order generation sequence generated by the above scheme, and optimizes and adjusts the order generation sequence based on different abnormal information, so that the optimized order generation sequence can not only meet the waiting time requirements of a large number of users, but also control the equipment to evenly deliver meals, thereby effectively improving the efficiency of order processing for a large number of orders, reducing the actual waiting time of each user, and improving the user experience, thereby effectively improving the efficiency of all order processing for a large number of product orders.

[0070] Optionally, based on the order demand information of each user, the product feature information of each product demand type of each user is identified through an order feature analysis strategy, including: for each user, splitting the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, based on the sub-order demand information of the product demand type, identifying the order source type of the product demand type, the product quantity information of the product demand type, and the material demand information of the product demand type; based on the order source type of the product demand type, identifying the product urgency of the product demand type, and based on the product quantity information of the product demand type and the material demand information of the product demand type, calculating the predicted product production time of each sub-order of the product demand type through a product generation algorithm; using the product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type as product feature information of the product demand type.

[0071] In this embodiment, for each user, the terminal splits the user's order demand information into sub-order demand information for each product demand type. For each product demand type, based on the sub-order demand information for the product demand type, the terminal identifies the order source type, product quantity information, and material demand information for the product demand type. The order source type for all product demand types for each customer is the same, while the product quantity information for different product demand types (i.e., the number of all sub-orders for that product demand type) is different. The material demand information for each product demand type includes the sub-material demand information for each sub-order of that product demand type.

[0072] The terminal identifies the urgency of the product demand type based on the order source type. The urgency of POS orders is high, the urgency of mini program / app orders is medium, and the urgency of takeout orders is low.

[0073] Based on the product quantity information and material requirement information of the product requirement type, the terminal calculates the predicted product production time for each sub-order of the product requirement type using a product generation algorithm. The product generation algorithm for the predicted product production time is as follows: predicted product production time = time to add sub-order material requirement information + product production time of the sub-order.

[0074] In another embodiment, the terminal can also identify the actual production difficulty of the product demand type based on the product quantity information and material demand information of the product demand type through a production difficulty evaluation strategy preset in the terminal, and query the production difficulty database based on the actual production difficulty of the product demand type. The product production time range corresponding to the product demand type is queried in the production difficulty database, and the product production time range is used as the predicted product production time of the product demand type. The production difficulty evaluation strategy includes each product quantity information range and the production difficulty level corresponding to each material demand information range. The production difficulty database includes the product production time range corresponding to each production difficulty level.

[0075] Finally, the terminal uses the product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type as product feature information of the product demand type.

[0076] Based on the above solution, by performing a categorized analysis of each user's sub-orders according to product demand type, the product urgency of each product demand type and the predicted product production time of each sub-order of the product demand type are identified, thereby improving the accuracy and comprehensiveness of the analysis of the production time and urgency of each sub-order.

[0077] Optionally, an order generation sequence is generated based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user, including: generating an initial user sequence based on the order time point of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence; for each user, based on the predicted product production time of each sub-order of each product demand type of the user, generating a product production sequence for each user according to the order optimization sorting strategy, and performing sequence adjustment processing on the product production sequence of each user according to the user sequence to obtain an initial order generation sequence; based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, identifying the order time adjustment range corresponding to each user through the order time adjustment analysis strategy, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, performing sequence adjustment processing on the order generation position information of each product demand type of each user in the initial order generation sequence to obtain an order generation sequence.

[0078] In this embodiment, the terminal generates an initial user sequence based on each user's order time and adjusts the initial user sequence based on each user's order time type to obtain a user sequence. Specifically, after sorting each user by their order time, the terminal calculates each user's product completion time based on the predicted product production duration of all sub-orders for each user and the current time point. Then, based on the scheduled pickup time of users with the reservation order type and the product completion time of each user, the terminal adjusts the sort position of users of each reservation order type according to the respective reservation pickup time points to obtain a user sequence. This ensures that users of each reservation order type can normally pick up their products at the scheduled pickup time points.

[0079] For each user, the terminal generates a product production sequence for each user based on the predicted product production time for each sub-order of each product demand type of the user, in accordance with the order optimization sorting strategy. The terminal then adjusts the product production sequence for each user according to the user sequence to obtain an initial order generation sequence. Specifically, the terminal calculates the total predicted product production time for all sub-orders of each product demand type and sorts them in order from shortest to longest based on the total predicted product production time to obtain a first order generation sequence. Then, for each product demand type, the terminal sorts each sub-order of that product demand type in order from shortest to longest based on the predicted product production time of that sub-order to obtain a second order generation sequence.

[0080] Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the terminal identifies the order time adjustment range corresponding to each user through the order time adjustment analysis strategy. Specifically, based on the order time type of each user, the terminal queries the first order adjustment time corresponding to each user in the order management database. Then, based on the sum of the predicted product production time of all sub-orders of each user, the terminal adds the first order adjustment time to obtain the target order adjustment time. Thereafter, based on the order time point of each user, the terminal determines the order time adjustment range corresponding to each user according to the target order adjustment time.

[0081] Based on the product urgency of each user's product demand type and the order duration adjustment range corresponding to each user, the terminal performs sequence adjustment processing on the order generation position information of each user's product demand type in the initial order generation sequence to obtain an order generation sequence. In the event that the order duration adjustment ranges of two users overlap or partially overlap, the terminal adjusts the order positions of the two users in the initial order generation sequence based on the product urgency to obtain an order generation sequence, wherein the higher the product urgency, the higher the order position, while the lower the product urgency, the lower the order position.

[0082] Based on the above solution, by combining the user's order time point, order duration adjustment range, and product urgency, we can comprehensively sort orders, improve the rationality of order sorting, and effectively avoid the problem of user satisfaction being affected by long order production time.

[0083] Optionally, identifying sequence anomaly information of an order generation sequence includes: dividing the order generation sequence into sub-order generation sequences for each generation period through a time period division strategy, and calculating the product generation frequency of each sub-order generation sequence and the average product generation time of each sub-order generation sequence based on the predicted product production time of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence; in each sub-order generation sequence, screening sub-order generation sequences whose product generation frequency is lower than a preset product generation frequency threshold and whose average product generation time is lower than a preset product average generation time as abnormal sub-order generation sequences, and treating all abnormal sub-order generation sequences as sequence anomaly information of the order generation sequence.

[0084] In this embodiment, the terminal divides the order generation sequence into sub-order generation sequences for each generation time period using a time period division strategy, and calculates the product generation frequency and the average product generation time for each sub-order generation sequence based on the predicted product production time of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence. The time period division strategy divides the order generation sequence according to a preset time period to obtain each sub-order generation sequence, wherein the order generation sequence includes the predicted product production time for each sub-order, and the preset time period is, for example, 10 minutes, 20 minutes, 30 minutes, 40 minutes, etc. Based on the sorting position of each sub-order and the predicted product production time of each sub-order, the terminal divides all sub-orders into sub-order groups in the order of the order generation sequence from front to back, thereby obtaining each sub-order generation sequence.

[0085] The terminal calculates the product generation frequency and average product generation time for each sub-order generation sequence. Then, within each sub-order generation sequence, the terminal selects sub-order generation sequences whose product generation frequency falls below a preset product generation frequency threshold and whose average product generation time falls below a preset average product generation time. These sequences are considered abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are recorded as sequence anomaly information for the order generation sequence.

[0086] Based on the above solution, abnormal sub-order generation sequences in the sub-order generation sequence are identified by dividing the time periods, thereby improving the accuracy of abnormal analysis of the sub-order generation sequence.

[0087] Optionally, the order sequence optimization and adjustment strategy is used to adjust the sequence anomaly information of the order generation sequence to obtain a target order generation sequence, including: for each abnormal sub-order generation sequence, extracting the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence, and the abnormal users corresponding to each abnormal sub-order; based on the predicted product production time of each abnormal sub-order and the abnormal users corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network; the new order generation sequence is replaced by the order generation sequence, and the execution is returned to the step of dividing the order generation sequence into sub-order generation sequences for each generation time period through the time period division strategy, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time, and the new order generation sequence obtained from the last iteration is used as the target order generation sequence.

[0088] In this embodiment, the terminal extracts the predicted product production time for each abnormal sub-order in each abnormal sub-order generation sequence, as well as the abnormal users corresponding to each abnormal sub-order. Then, based on the predicted product production time for each abnormal sub-order and the abnormal users corresponding to each abnormal sub-order, the terminal generates a new order generation sequence through an abnormal sequence adjustment network. The abnormal sequence adjustment network is a reinforcement learning-based neural network that includes a reward function, and the order duration adjustment range for each user serves as a constraint for generating the new order generation sequence. This ensures that the total production time for all sub-orders for each user in the generated new order generation sequence does not exceed the order duration adjustment range for that user.

[0089] Replace the order generation sequence with the new order generation sequence, and return to execute the time period division strategy to divide the order generation sequence into sub-order generation sequence steps for each generation period. This continues until there are no sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time. The new order generation sequence obtained from the last iteration is used as the target order generation sequence.

[0090] In another embodiment, the terminal sorts the predicted product production times of each abnormal sub-order in ascending order based on the predicted product production times, obtaining a new sub-order generation sequence. Each new sub-order generation sequence replaces the abnormal sub-order generation sequence to obtain a first order generation sequence. The terminal then calculates the total predicted order production time for each user in the first order generation sequence based on the sequence position information of each sub-order in the first order generation sequence and the predicted product production time of each sub-order. The terminal also calculates the order generation deviation time for each user based on the total predicted order production time for each user and the order time adjustment range corresponding to each user. If an order generation deviation time exceeds a preset order generation deviation time threshold, the terminal identifies the user with an order generation deviation time greater than the preset order generation deviation time threshold as an abnormal user. Based on the sequence position information of each sub-order of each abnormal user in the first order generation sequence and the order time adjustment range corresponding to each abnormal user, the terminal adjusts the sequence position information of each sub-order of each abnormal user in the first order generation sequence via a sequence position adjustment network to obtain a second order generation sequence. The terminal replaces the order generation sequence with the second order generation sequence and returns to execute the time period division strategy, dividing the order generation sequence into sub-order generation sequence steps for each generation period. This continues until there is no order generation deviation duration greater than a preset order generation deviation duration threshold. The second order generation sequence obtained from the last iteration is then used as the target order generation sequence. The sequence position adjustment network is a convolutional neural network based on a self-attention mechanism.

[0091] Based on the above solution, by predicting and optimizing the time points of order delivery, we can achieve balanced delivery of orders, reduce customer waiting time, and improve user experience.

[0092] Optionally, the method also includes: based on the target order generation sequence, controlling the intelligent liquid dispensing machine to execute the product generation task corresponding to each user's sub-order, and collecting user feedback information of each user, and order generation status information of each sub-order; based on the order generation status of each sub-order and the user feedback information of each user, performing sequence adjustment processing on the current order generation sequence that has not been executed in the target order generation sequence to obtain the current new order generation sequence, and replacing the target order generation sequence with the current new order generation sequence; returning to execute based on the target order generation sequence, controlling the intelligent liquid dispensing machine, and executing the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

[0093] In this embodiment, the terminal controls the intelligent liquid dispensing machine based on the target order generation sequence, executes the product generation task corresponding to each user's sub-order, and collects user feedback information from each user, as well as the order generation status information of each sub-order. Among them, the user's feedback information includes the user's feedback information on the product delivery efficiency and delivery quality, and the order generation status information includes the normal delivery status of the order and the abnormal delivery status of the order. Among them, when the delivery quality is abnormal or the delivery status is abnormal, the terminal will determine that the sub-order generation is abnormal and the sub-order needs to be regenerated. In the case of low product delivery efficiency, the terminal will process the sequence position of each ungenerated sub-order of the user in advance.

[0094] Based on the order generation status of each sub-order and the user feedback information of each user, the terminal performs sequence adjustment processing on the unexecuted current order generation sequence in the target order generation sequence to obtain the current new order generation sequence, and replaces the target order generation sequence with the current new order generation sequence. The adjustment process is to adjust the order adjustment time of each user corresponding to the sub-order that needs to be processed in advance, using the current time point as the time starting point and the expedited time preset in the terminal as the time period to obtain the new order adjustment time of the user, and to adjust the order adjustment time of each user corresponding to the sub-order that needs to be regenerated, using the current time point as the time starting point and the pending order generation time preset in the terminal as the time period to obtain the new order adjustment time of the user. Finally, the terminal replaces the product order information of each user with the sub-orders in the current order generation sequence that have not been executed, together with the orders of the above-mentioned users that need to be regenerated or expedited, and returns to execute the steps of splitting the product order information into the order demand information of each user, the order time type of each user, and the order time point of each user, thereby obtaining the current new order generation sequence.

[0095] Then, the terminal returns to execute the target order generation sequence, controls the intelligent liquid dispensing machine, and executes the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

[0096] Based on the above solution, each order sequence is intelligently adjusted based on user feedback and product status, thereby improving the flexibility and intelligence of order management while meeting the user's continued needs, and improving the user experience while ensuring the efficiency of order generation.

[0097] The application also provides an example of generating a product order sequence, such as Figure 2 As shown, the specific processing process includes the following steps:

[0098] Step S201 : obtaining product order information of each user, and dividing each product order information into order demand information of each user, order time type of each user, and order time point of each user.

[0099] Step S202: For each user, split the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, identify the order source type of the product demand type, the product quantity information of the product demand type, and the material demand information of the product demand type based on the sub-order demand information of the product demand type.

[0100] Step S203, based on the order source type of the product demand type, identify the product urgency of the product demand type, and based on the product quantity information of the product demand type and the material demand information of the product demand type, calculate the predicted product production time of each sub-order of the product demand type through the product generation algorithm.

[0101] Step S204 : The product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type are used as product feature information of the product demand type.

[0102] Step S205 : generating an initial user sequence based on the order time point of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence.

[0103] In step S206, for each user, based on the predicted product production time of each sub-order of each product demand type of the user, a product production sequence for each user is generated according to the order optimization sorting strategy, and the product production sequence of each user is sequence-adjusted according to the user sequence to obtain the initial order generation sequence.

[0104] Step S207: Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the order time adjustment analysis strategy is used to identify the order time adjustment range corresponding to each user, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, the order generation position information of each product demand type of each user in the initial order generation sequence is sequence adjusted to obtain an order generation sequence.

[0105] Step S208: Divide the order generation sequence into sub-order generation sequences for each generation period through a time period division strategy, and calculate the product generation frequency of each sub-order generation sequence and the average product generation time of each sub-order generation sequence based on the predicted product production time of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence.

[0106] Step S209: In each sub-order generation sequence, the sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time are screened as abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are used as sequence abnormality information of the order generation sequence.

[0107] Step S210 , for each abnormal sub-order generation sequence, extract the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence and the abnormal user corresponding to each abnormal sub-order.

[0108] Step S211 : Based on the predicted product production time of each abnormal sub-order and the abnormal user corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network.

[0109] Step S212, replace the order generation sequence with the new order generation sequence, return to execute the time period division strategy, divide the order generation sequence into sub-order generation sequence steps for each generation period, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and the average product generation time is lower than the preset product average generation time, and the new order generation sequence obtained in the last iteration is used as the target order generation sequence.

[0110] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0111] Based on the same inventive concept, embodiments of the present application also provide a device for generating a product order sequence for implementing the aforementioned method for generating a product order sequence. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for generating a product order sequence provided below can be found in the aforementioned limitations of the method for generating a product order sequence, and will not be further elaborated here.

[0112] In an exemplary embodiment, Figure 3 As shown, a device for generating a product order sequence is provided, comprising: an acquisition module 310, a generation module 320 and an identification module 330, wherein:

[0113] The acquisition module 310 is used to acquire product order information of each user and split the product order information into order demand information of each user, order time type of each user, and order time point of each user;

[0114] A generation module 320 is configured to identify, based on the order demand information of each user, product feature information of each product demand type of each user using an order feature analysis strategy, and generate an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user;

[0115] The identification module 330 is configured to identify sequence anomaly information of the order generation sequence, and adjust the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

[0116] Optionally, the generating module 320 is specifically configured to:

[0117] For each user, split the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, identify the order source type of the product demand type, product quantity information of the product demand type, and material demand information of the product demand type based on the sub-order demand information of the product demand type;

[0118] Based on the order source type of the product demand type, identifying the urgency of the product of the product demand type, and based on the product quantity information of the product demand type and the material requirement information of the product demand type, calculating the predicted product production time of each sub-order of the product demand type through a product generation algorithm;

[0119] The product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type are used as product feature information of the product demand type.

[0120] Optionally, the generating module 320 is specifically configured to:

[0121] generating an initial user sequence based on the order time points of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence;

[0122] For each user, based on the predicted product production time of each sub-order of each product demand type of the user, according to the order optimization sorting strategy, a product production sequence for each user is generated, and the product production sequence of each user is sequenced according to the user sequence to obtain an initial order generation sequence;

[0123] Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the order time adjustment analysis strategy is used to identify the order time adjustment range corresponding to each user, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, the order generation position information of each product demand type of each user in the initial order generation sequence is sequence adjusted to obtain an order generation sequence.

[0124] Optionally, the identification module 330 is specifically configured to:

[0125] The order generation sequence is divided into sub-order generation sequences for each generation period using a time period division strategy. Based on the predicted product production durations of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence, the product generation frequency and the average product production duration of each sub-order generation sequence are calculated.

[0126] In each of the sub-order generation sequences, the sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time are screened as abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are used as sequence abnormality information of the order generation sequence.

[0127] Optionally, the identification module 330 is specifically configured to:

[0128] For each abnormal sub-order generation sequence, extract the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence and the abnormal user corresponding to each abnormal sub-order;

[0129] Based on the predicted product production time of each abnormal sub-order and the abnormal user corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network;

[0130] The order generation sequence is replaced by the new order generation sequence, and the process returns to executing the time period division strategy to divide the order generation sequence into sub-order generation sequence steps for each generation period, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time. The new order generation sequence obtained from the last iteration is used as the target order generation sequence.

[0131] Optionally, the device further includes:

[0132] A collection module is used to control the intelligent liquid dispensing machine based on the target order generation sequence to execute the product generation task corresponding to each user's sub-order, and collect user feedback information of each user and order generation status information of each sub-order;

[0133] an adjustment module configured to perform sequence adjustment processing on the unexecuted current order generation sequence in the target order generation sequence based on the order generation status of each sub-order and user feedback information of each user, to obtain a current new order generation sequence, and replace the target order generation sequence with the current new order generation sequence;

[0134] The iteration module is used to return to execute based on the target order generation sequence, control the intelligent liquid dispensing machine, and execute the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

[0135] Each module in the aforementioned product order sequence generation device may be implemented in whole or in part via software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0136] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for generating a product order sequence is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0137] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0138] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements steps corresponding to the method for generating a product order sequence when executing the computer program.

[0139] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the computer program implements the steps corresponding to the method for generating a product order sequence.

[0140] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements steps corresponding to a method for generating a product order sequence.

[0141] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0143] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0144] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for generating a product order sequence, characterized in that: The method comprises: Obtain product order information of each user, and split each product order information into order demand information of each user, order time type of each user, and order time point of each user; Based on the order demand information of each user, identifying the product feature information of each product demand type of each user through an order feature analysis strategy, and generating an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user; Identify sequence anomaly information of the order generation sequence, and adjust the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

2. The method according to claim 1, characterized in that The method of identifying product feature information of each product demand type of each user based on the order demand information of each user through an order feature analysis strategy includes: For each user, split the user's order demand information into sub-order demand information of each product demand type, and for each product demand type, identify the order source type of the product demand type, product quantity information of the product demand type, and material demand information of the product demand type based on the sub-order demand information of the product demand type; Based on the order source type of the product demand type, identifying the urgency of the product of the product demand type, and based on the product quantity information of the product demand type and the material requirement information of the product demand type, calculating the predicted product production time of each sub-order of the product demand type through a product generation algorithm; The product urgency of the product demand type and the predicted product production time of each sub-order of the product demand type are used as product feature information of the product demand type.

3. The method according to claim 2, characterized in that Generating an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user includes: generating an initial user sequence based on the order time points of each user, and performing sequence adjustment processing on the initial user sequence based on the order time type of each user to obtain a user sequence; For each user, based on the predicted product production time of each sub-order of each product demand type of the user, according to the order optimization sorting strategy, a product production sequence for each user is generated, and the product production sequence of each user is sequenced according to the user sequence to obtain an initial order generation sequence; Based on the predicted product production time of all sub-orders of each user, the order time type of each user, and the order time point of each user, the order time adjustment analysis strategy is used to identify the order time adjustment range corresponding to each user, and based on the product urgency of each product demand type of each user and the order time adjustment range corresponding to each user, the order generation position information of each product demand type of each user in the initial order generation sequence is sequence adjusted to obtain an order generation sequence.

4. The method according to claim 2, characterized in that The identifying sequence anomaly information of the order generation sequence includes: The order generation sequence is divided into sub-order generation sequences for each generation period using a time period division strategy. Based on the predicted product production durations of all sub-orders in each sub-order generation sequence and the number of all sub-orders in each sub-order generation sequence, the product generation frequency and the average product production duration of each sub-order generation sequence are calculated. In each of the sub-order generation sequences, the sub-order generation sequences whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time are screened as abnormal sub-order generation sequences, and all abnormal sub-order generation sequences are used as sequence abnormality information of the order generation sequence.

5. The method according to claim 4, characterized in that The order sequence optimization adjustment strategy is used to adjust the sequence abnormality information of the order generation sequence to obtain a target order generation sequence, including: For each abnormal sub-order generation sequence, extract the predicted product production time of each abnormal sub-order in the abnormal sub-order generation sequence and the abnormal user corresponding to each abnormal sub-order; Based on the predicted product production time of each abnormal sub-order and the abnormal user corresponding to each abnormal sub-order, a new order generation sequence is generated through the abnormal sequence adjustment network; The order generation sequence is replaced by the new order generation sequence, and the process returns to executing the time period division strategy to divide the order generation sequence into sub-order generation sequence steps for each generation period, until there is no sub-order generation sequence whose product generation frequency is lower than the preset product generation frequency threshold and whose average product generation time is lower than the preset product average generation time. The new order generation sequence obtained from the last iteration is used as the target order generation sequence.

6. The method according to claim 1, characterized in that The method further comprises: Based on the target order generation sequence, the intelligent liquid dispensing machine is controlled to execute the product generation task corresponding to each user's sub-order, and user feedback information of each user and order generation status information of each sub-order are collected; Based on the order generation status of each child order and the user feedback information of each user, a sequence adjustment process is performed on the unexecuted current order generation sequence in the target order generation sequence to obtain a current new order generation sequence, and the current new order generation sequence replaces the target order generation sequence; Return to execute based on the target order generation sequence, control the intelligent liquid dispensing machine, and execute the product generation task steps corresponding to each user's sub-order until the product generation tasks of all sub-orders of the target order generation sequence are completed.

7. A device for generating a product order sequence, characterized in that: The device comprises: An acquisition module is used to acquire product order information of each user, and split each product order information into order demand information of each user, order time type of each user, and order time point of each user; a generation module configured to identify, based on the order demand information of each user and using an order feature analysis strategy, product feature information of each product demand type of each user, and generate an order generation sequence based on the order time point of each user, the order time type of each user, and the product feature information of each product demand type of each user; The identification module is used to identify sequence anomaly information of the order generation sequence, and adjust the sequence anomaly information of the order generation sequence through an order sequence optimization adjustment strategy to obtain a target order generation sequence.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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