Multi-e-commerce platform order circulation method and system based on sorting optimization algorithm

Abstract

The embodiment of the invention provides a multi-e-commerce platform order circulation method and system based on a sorting optimization algorithm. Wherein real-time state information, priority information and optimization targets of orders are obtained from a plurality of e-commerce platforms, initial weights are dynamically calculated by using the information, a multi-target optimization function is constructed, a preliminary order sorting result is generated, and in the iteration process, weight parameters are monitored and adjusted in real time to adapt to changes; the method comprises the steps of obtaining a real-time sorting result through local optimization and constraint condition correction, generating and distributing an order circulation instruction to each e-commerce platform system according to the real-time sorting result, monitoring an order state in real time during an order circulation period, identifying an abnormality according to a preset rule, and generating a processing instruction for adjustment; according to the technical scheme provided by the embodiment of the invention, the response capability to the order state change is enhanced, the processing efficiency is improved, the data consistency and execution continuity of cross-platform order circulation are ensured, the abnormal condition is effectively identified and processed, and the stability of the order flow is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of enterprise order control, and in particular, to a method and system for order transfer among multiple e-commerce platforms based on a sorting optimization algorithm. Background Art

[0002] In the modern e-commerce environment, enterprises usually need to manage orders from multiple e-commerce platforms. To improve customer satisfaction and operational efficiency, these orders must be intelligently sorted and efficiently transferred according to multiple optimization goals (such as timeliness, cost control, customer priority, etc.). With the development of business, the types and attributes of orders are constantly increasing and changing, requiring the system to be able to quickly adapt to these changes and maintain flexibility and scalability. In addition, cross-platform order processing also needs to ensure data consistency and execution coherence to avoid problems such as information silos and inconsistent operations;

[0003] Current solutions often rely on a fixed structure (such as an order scoring tree) for multi-objective optimization sorting. Orders are initially sorted through predefined weights and rule sets, and based on this, order transfer instructions are generated and distributed to the order processing systems of each e-commerce platform. To ensure data consistency and execution coherence, system integration interfaces are used to connect the order processing systems of different platforms. At the same time, during the order transfer process, preset anomaly detection rules are used to identify and handle abnormal states and adjust the execution order of orders;

[0004] Although existing methods meet the basic requirements to a certain extent, there are still significant deficiencies in dealing with complex and changing business environments. First, how to find the best balance among multiple optimization goals such as timeliness, cost control, and customer priority in multi-objective optimization is a major challenge. Second, the real-time changes in order status and priority require the sorting algorithm to have the ability to respond quickly, and existing solutions are difficult to quickly adapt to these dynamic changes. Third, order transfer involves multiple systems and links, and ensuring seamless connection and data consistency among all links is another technical difficulty. Finally, various abnormal states may occur during the order transfer process, and the existing system's anomaly detection and handling mechanism is not efficient enough to identify and handle abnormal situations in a timely and accurate manner, affecting the stability and reliability of the overall process. These problems limit the flexibility, adaptability, and overall efficiency of the system. Summary of the Invention

[0005] An embodiment of the present application provides a method and system for order transfer in multiple e-commerce platforms based on a sorting optimization algorithm, aiming to solve the problem that the real-time changes in order status and priority require the sorting algorithm to have the ability to respond quickly in the prior art. However, the existing solutions are difficult to quickly adapt to these dynamic changes, and various abnormal states may occur during the order transfer process. The abnormal detection and processing mechanisms of the existing systems are not efficient enough to identify and process abnormal situations in a timely and accurate manner. Moreover, the order transfer involves multiple systems and links, and ensuring seamless connection and data consistency among all links is another technical difficulty.

[0006] In a first aspect, an embodiment of the present application provides a method for order transfer in multiple e-commerce platforms based on a sorting optimization algorithm, including:

[0007] According to the order data of multiple e-commerce platforms, real-time status information, priority information, and multiple optimization objectives of the order data are obtained in real time;

[0008] Using the real-time status information and priority information of the order, the initial weight of each optimization objective is dynamically calculated, a multi-objective optimization function is constructed based on the initial weight and iteratively optimized to generate a preliminary order sorting result. During the iterative process, the changes in the real-time status information and priority information of the order are monitored in real time, the initial weight parameters are dynamically adjusted, and the preliminary order sorting result is locally optimized. Constraint conditions are introduced for correction to generate a real-time sorting result;

[0009] According to the real-time sorting result, an order transfer instruction is generated and distributed to the order processing systems of multiple e-commerce platforms;

[0010] Through the system integration interface, the order transfer instruction is docked with the order processing systems of multiple e-commerce platforms to ensure the consistency and execution coherence of the data in each link during the order transfer process;

[0011] During the order transfer process, the execution status of the order is monitored in real time, and according to the preset abnormal detection rules, the abnormal status occurring during the transfer process is identified, and an abnormal handling instruction is generated. The abnormal handling instruction is used to adjust the execution order of the order.

[0012] Optionally, using the real-time status information and priority information of the order, the initial weight of each optimization objective is dynamically calculated, a multi-objective optimization function is constructed based on the initial weight and iteratively optimized to generate a preliminary order sorting result. During the iterative process, the changes in the real-time status information and priority information of the order are monitored in real time, the initial weight parameters are dynamically adjusted, and the preliminary order sorting result is locally optimized. Constraint conditions are introduced for correction to generate a real-time sorting result, including:

[0013] Using the real-time status information and priority information, dynamically calculate the corresponding initial weight values for each optimization objective, and construct a multi-objective optimization framework based on the initial weight values;

[0014] According to the multi-objective optimization framework, combine the information of all current orders to generate a preliminary order sorting result;

[0015] During the process of generating the preliminary order sorting result, monitor the changes in the real-time status information and priority information of the orders in real time, and dynamically adjust the initial weights of the corresponding optimization objectives based on the changes to obtain updated weights;

[0016] Use the updated weight values to perform local optimization processing on the preliminary order sorting result, introduce a series of constraint conditions for key orders for correction, and generate a real-time sorting result.

[0017] Optionally, use the updated weight values to perform local optimization processing on the preliminary order sorting result, introduce a series of constraint conditions for specific orders for correction, and generate a real-time sorting result, including:

[0018] Based on the updated weight values and the preliminary order sorting result, identify the key orders for local optimization, and use the real-time status information and priority information of the key orders to obtain optimization requirements;

[0019] According to the optimization requirements, set a personalized set of constraint conditions for each key order, and based on the set of constraint conditions, adjust the corresponding part of the preliminary order sorting result to obtain the corresponding part of the sorting result after local optimization processing;

[0020] According to the corresponding part of the sorting result after local optimization processing, monitor the status changes of all affected orders and the impact of the affected orders on the overall sorting result in real time. When it is found that the overall efficiency decreases, re-evaluate and adjust the corresponding constraint conditions based on the preset feedback mechanism to obtain an adjusted set of constraint conditions;

[0021] According to the adjusted set of constraint conditions, readjust the preliminary order sorting result so that all applied constraint conditions are correctly executed and no problems outside the existing objectives are introduced. When an unexpected situation is detected, based on the adjustment process, correct the problematic part to obtain a sorting result that passes the review;

[0022] Based on the sorting result that passes the review, combine the corresponding part of the sorting result after local optimization processing to generate a real-time sorting result.

[0023] Optionally, it is characterized in that, in the process of generating the preliminary order sorting result, the changes of the real-time status information and the priority information of the order are monitored in real time, and the initial weights of the corresponding optimization objectives are dynamically adjusted based on the changes to obtain updated weights, and further includes:

[0024] Based on the multi-objective optimization framework, when generating preliminary order sorting results, a real-time monitoring mechanism is used to perform a preset periodic scan on the real-time status information and priority information of all orders, identify changes in the real-time status information and the priority information, and record the time point and specific content of the real-time status information change and the priority information change, and generate a dynamic change log;

[0025] Analyze the influence of each order change on the optimization target of each order according to the content in the dynamic change log, so as to determine the optimization target for adjusting the weight value;

[0026] For the optimization target determined to have an adjusted weight value, based on the current overall business demand and system resource status, the initial weight of the optimization target is dynamically adjusted using the status and priority information outside the existing target of the order to obtain an updated weight.

[0027] Optionally, the order flow instruction is connected to the order processing systems of multiple e-commerce platforms through a system integration interface, so that the data of each link in the order flow process has consistency and execution coherence, including:

[0028] Based on the real-time sorting results, the flow path of each order is planned, and a flow plan matching each order is formulated;

[0029] Generate an order transfer instruction for each order according to the transfer plan;

[0030] Using the system integration interface, the order flow instruction is converted into a data packet that conforms to the format of the order processing system of each e-commerce platform, and the data packet is sent to the corresponding e-commerce platform through a communication channel;

[0031] After the data packet is sent, a real-time tracking mechanism is implemented to monitor the execution status of each order in the e-commerce platform in real time, so that the order can be circulated according to the predetermined plan.

[0032] Optionally, it is characterized in that, according to the order data of multiple e-commerce platforms, real-time status information, priority information and multiple optimization targets of the order data are obtained in real time, including:

[0033] Based on the interfaces provided by multiple e-commerce platforms, a data collection framework is established, and the data collection framework is used to capture the original data related to orders from each e-commerce platform in real time;

[0034] Using the unified data collection framework, standardize the original data of each e-commerce platform, convert order data in different formats and structures into a unified format, and obtain the standardized order data;

[0035] According to the standardized order data, extract the real-time status information and priority information of each order, and associate the real-time status information and priority information with the order identifier to generate order description information;

[0036] During the process of extracting the real-time status information and priority information of the order, identify and define multiple optimization goals.

[0037] Optionally, during the order flow process, monitor the execution status of the order in real time, and according to the preset anomaly detection rules, identify the abnormal status that appears during the flow process, and generate an anomaly handling instruction, where the anomaly handling instruction is used to adjust the execution order of the order, including:

[0038] After the order flow instruction is distributed to the order processing systems of multiple e-commerce platforms, use the integration interface to establish a cross-platform real-time monitoring framework;

[0039] Use the real-time monitoring framework to perform preset periodic checks on the status of each order, collect various data points related to order execution, and generate an order execution log;

[0040] According to the information in the order execution log, combined with the preset anomaly detection rules, comprehensively evaluate the execution status of each order, identify the situations that do not conform to the expected process, and determine the existing abnormal status;

[0041] For the abnormal status, based on the type and severity of the abnormal status, automatically trigger the corresponding response mechanism, and the response mechanism includes: pausing the further flow of the affected order, notifying the relevant parties to take actions, and generating an anomaly handling instruction.

[0042] In a second aspect, an embodiment of the present application provides a multi-e-commerce platform order flow system based on a sorting optimization algorithm, including:

[0043] An acquisition module, configured to obtain the real-time status information, priority information, and multiple optimization goals of the order data in real time according to the order data of multiple e-commerce platforms;

[0044] A correction module, which is used to dynamically calculate the initial weights of each optimization objective by using the real-time status information and priority information of orders, construct a multi-objective optimization function based on the initial weights and perform iterative optimization to generate a preliminary order sorting result. During the iterative process, it monitors the changes in the real-time status information and priority information of orders in real time, dynamically adjusts the initial weight parameters, and locally optimizes the preliminary order sorting result, and introduces constraint conditions for correction to generate a real-time sorting result;

[0045] A distribution module, which is used to generate an order transfer instruction according to the real-time sorting result and distribute the order transfer instruction to the order processing systems of multiple e-commerce platforms;

[0046] A docking module, which is used to dock the order transfer instruction with the order processing systems of multiple e-commerce platforms through a system integration interface, so that the data in each link during the order transfer process has consistency and execution coherence;

[0047] An identification module, which is used to monitor the execution status of orders in real time during the order transfer process, and identify abnormal statuses that occur during the transfer process according to preset abnormal detection rules, and generate abnormal handling instructions, and the abnormal handling instructions are used to adjust the execution order of orders.

[0048] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-e-commerce platform order transfer method based on a sorting optimization algorithm as described in the first aspect above.

[0049] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it implements a multi-e-commerce platform order transfer method based on a sorting optimization algorithm as described in the first aspect.

[0050] In the embodiments of the present application, according to the order data of multiple e-commerce platforms, the real-time status information, priority information, and multiple optimization objectives of the order data are obtained in real time; the initial weights of each optimization objective are dynamically calculated by using the real-time status information and priority information of the order, a multi-objective optimization function is constructed based on the initial weights and iteratively optimized to generate a preliminary order sorting result. During the iteration process, the changes in the real-time status information and priority information of the order are monitored in real time, the initial weight parameters are dynamically adjusted, and the preliminary order sorting result is locally optimized, and constraint conditions are introduced for correction to generate a real-time sorting result; according to the real-time sorting result, an order transfer instruction is generated, and the order transfer instruction is distributed to the order processing systems of multiple e-commerce platforms; through the system integration interface, the order transfer instruction is docked with the order processing systems of multiple e-commerce platforms to ensure the consistency and execution coherence of the data in each link during the order transfer process; during the order transfer process, the execution status of the order is monitored in real time, and according to the preset anomaly detection rules, the abnormal status occurring during the transfer process is identified, and an anomaly handling instruction is generated, and the anomaly handling instruction is used to adjust the execution order of the order;

[0051] The technical solution of the present application has the following beneficial effects:

[0052] Through the real-time monitoring and dynamic adjustment mechanism, this method not only enhances the system's rapid response ability to order status changes, but also improves the order processing efficiency and accuracy through precise multi-objective optimization. At the same time, it ensures the data consistency and execution coherence of cross-platform order transfer, and can effectively identify and handle abnormal situations, thereby greatly improving the stability and reliability of the overall order management process. This method enables efficient order transfer management even in the face of frequently changing business requirements, meeting the needs in diverse business scenarios;

[0053] Furthermore, by using the real-time status information and priority information of the order, the initial weight values of each optimization objective are dynamically calculated, and a multi-objective optimization framework is constructed based on these weight values. Combining the information of all orders, a preliminary order sorting result is generated. During the process of generating the preliminary order sorting result, by monitoring the status and priority changes of the order in real time, the initial weights of the optimization objectives are dynamically adjusted to obtain updated weights. Subsequently, the updated weight values are used to locally optimize the preliminary order sorting result, and a series of constraint conditions are introduced for correction for critical orders, and finally a real-time sorting result is generated;

[0054] This method not only realizes the effective management of multi-objective optimization problems, but also can quickly respond to changes in order status and priority, ensuring that the sorting results always reflect the latest business requirements. By dynamically adjusting the weight values and performing local optimization processing, the system can find the best balance among multiple optimization objectives while maintaining high efficiency and flexibility. In addition, introducing constraint conditions for correction further improves the accuracy and adaptability of the sorting results, making the entire order flow process more intelligent, stable and highly scalable, thus significantly improving the order processing efficiency and customer satisfaction.

[0055] These aspects or other aspects of the present application will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application or in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 The flowchart of a multi-e-commerce platform order flow method provided by the present application based on a sorting optimization algorithm is shown;

[0058] Figure 2 The structural schematic diagram of a multi-e-commerce platform order flow system provided by the present application based on a sorting optimization algorithm is shown;

[0059] Figure 3 The structural schematic diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] In order to enable those skilled in the art of the present technology to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application.

[0061] In some of the processes described in the specification, claims and above-mentioned drawings of the present application, there are multiple operations that appear in a specific order. However, it should be clearly understood that these operations can be executed not in the order in which they appear herein or in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations can be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0062] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art based on the embodiments in the present application without creative efforts belong to the scope of protection of the present application.

[0063] Figure 1 The following is a flowchart of a multi-e-commerce platform order transfer method based on a sorting optimization algorithm provided for an embodiment of the present application. As Figure 1 shown, the method includes:

[0064] Step 101, according to the order data of multiple e-commerce platforms, obtain the real-time status information, priority information, and multiple optimization goals of the order data in real time;

[0065] In this step, the real-time status information includes data such as the current processing stage of the order (such as pending, in processing, completed), estimated completion time, etc., which is used to evaluate the order transfer efficiency; the priority information refers to the priority order of the order in the processing queue, usually set based on customer requirements or business rules; the optimization goal refers to the specific indicators that the system hopes to achieve, such as timeliness, cost control, and customer satisfaction, etc.;

[0066] First, by docking with the interfaces of multiple e-commerce platforms, the system can capture the latest real-time status information and priority information of each order in real time. At the same time, the system identifies multiple optimization goals, which cover key consideration factors in the order transfer process, such as timely delivery, cost minimization, and improving customer experience. All this information is integrated into a multi-dimensional order feature library, providing a solid data foundation for subsequent multi-objective optimization;

[0067] In a practical application scenario, an e-commerce company manages orders from multiple platforms such as Taobao, JD.com, and Pinduoduo. Whenever a new order is generated or the status of an existing order changes, the system automatically synchronizes the latest status and priority information from each platform and stores it in the order feature library. For example, an order from Taobao is marked as high priority due to the urgent needs of the customer, and at the same time, the system identifies that this order needs to meet the optimization goal of fast delivery.

[0068] Step 102, using the real-time status information and priority information of the order, dynamically calculate the initial weight of each optimization target, construct a multi-objective optimization function based on the initial weight and perform iterative optimization to generate a preliminary order sorting result, monitor the changes of the real-time status information and priority information of the order in real time during the iteration process, dynamically adjust the initial weight parameters, and locally optimize the preliminary order sorting result, introduce constraint conditions for correction, and generate a real-time sorting result;

[0069] In this step, the multi-objective optimization function is a mathematical model built based on different optimization objectives and their weight values ​​to find the best order sorting solution; local optimization refers to the process of fine-tuning specific orders or order groups to ensure that specific business needs are met without affecting overall efficiency;

[0070] The system uses the real-time status and priority information of the order to dynamically calculate the initial weight value of each optimization target and build a multi-objective optimization framework based on these weight values. Subsequently, the system generates preliminary order sorting results based on the information of all current orders. Throughout the process, the system continuously monitors changes in order status and priority, dynamically adjusts weight values ​​based on these changes, and generates updated weight values. Finally, the updated weight values ​​are used to perform local optimization on the preliminary order sorting results, and constraints are introduced for correction, and finally a real-time sorting result is generated.

[0071] Continuing with the above scenario, the system assigns a corresponding weight value to each order based on the order priority and timeliness requirements, and generates a preliminary order sorting result based on this. When a high-priority order is found to have an estimated completion time delay, the system immediately adjusts its weight value and performs local optimization on the order to ensure that it can be processed in the shortest time possible without affecting the normal flow of other orders.

[0072] Step 103, generating an order transfer instruction according to the real-time sorting result, and distributing the order transfer instruction to order processing systems of multiple e-commerce platforms;

[0073] Order flow instructions refer to the specific operation guidelines generated by the system based on the real-time sorting results, instructing each e-commerce platform on how to process related orders; order flow instruction distribution refers to sending these instructions to the order processing system of each e-commerce platform;

[0074] Based on the real-time sorting results, the system automatically generates detailed order flow instructions, clarifying the processing path and steps of each order within each e-commerce platform. Then, through a secure and reliable communication channel, these instructions are distributed to the corresponding e-commerce platform order processing system to ensure that each order can be smoothly transferred according to the predetermined plan;

[0075] In the aforementioned case, the system generated specific order transfer instructions for each order based on the real-time sorting results. For example, it prioritized the processing of certain urgent orders and distributed these instructions to the order processing systems of Taobao, JD.com, and Pinduoduo. In this way, even in the face of a large number of orders, it can ensure that each order is transferred along the optimal path.

[0076] Step 104: Through the system integration interface, dock the order transfer instructions with the order processing systems of multiple e-commerce platforms to ensure the consistency and execution coherence of data in each link during the order transfer process;

[0077] The system integration interface refers to the middleware that connects the order processing systems of different e-commerce platforms, ensuring the consistency and accuracy of data transmission; consistency means the consistency of data in each link, avoiding information silos; execution coherence guarantees the continuity of the operation process and prevents interruptions;

[0078] The system seamlessly docks the order transfer instructions into the order processing systems of multiple e-commerce platforms through the integration interface, ensuring the data consistency and execution coherence in each link during the order transfer process. This step ensures the accurate transmission of information by standardizing the data format and protocol, avoiding operation errors caused by inconsistent data;

[0079] In the above case, the system successfully docked with the order processing systems of Taobao, JD.com, and Pinduoduo through the integration interface, ensuring that all order transfer instructions were correctly received and executed. For example, when an order needs to be transferred across platforms, the system can ensure that relevant information remains consistent among platforms, avoiding duplicate operations or omissions.

[0080] Step 105: During the order transfer process, monitor the execution status of the order in real time, and according to the preset anomaly detection rules, identify the abnormal status that appears during the transfer process, and generate anomaly handling instructions, where the anomaly handling instructions are used to adjust the execution order of the order.

[0081] The anomaly detection rules are a series of predefined criteria used to identify abnormal situations during the order transfer process; the anomaly handling instructions are specific solutions generated for abnormal situations, used to correct problems and restore normal order transfer;

[0082] The system monitors the execution status of each order in real time during the order transfer process. Once any deviation from the expected situation is found, it identifies the abnormal status according to the preset anomaly detection rules and generates corresponding anomaly handling instructions. These instructions are designed to adjust the execution order of the order or take other measures to ensure the restoration of the normal order transfer process;

[0083] In previous cases, the system monitored the order flow in real time. When it was found that a certain order could not be delivered on time due to logistics delays, the system automatically generated an exception handling instruction, suggesting to prioritize the arrangement of alternative logistics solutions and notifying the relevant departments for handling. In this way, even in case of emergencies, the system can respond quickly and solve problems to ensure the smooth flow of orders.

[0084] In summary, this method obtains real-time order status information and priority information, dynamically calculates the weights of optimization objectives, constructs a multi-objective optimization function to generate a preliminary order sorting result, and continuously adjusts during the process to generate a real-time sorting result. Then, according to the real-time sorting result, order flow instructions are generated and distributed to each e-commerce platform, ensuring data consistency and execution coherence through system integration interfaces. Finally, the system monitors the order execution status in real time, identifies abnormal situations and generates handling instructions to ensure the efficiency, stability and flexibility of the order flow process. This method significantly improves order processing efficiency and customer satisfaction, enabling enterprises to more effectively cope with complex business environments.

[0085] To address the challenge of balancing different optimization objectives in multi-objective optimization and further improve the flexibility and response speed in the order sorting process, in some embodiments, according to step 102, the real-time status information and priority information of the order are used to dynamically calculate the initial weight of each optimization objective. Based on the initial weight, a multi-objective optimization function is constructed and iteratively optimized to generate a preliminary order sorting result. During the iteration process, the changes in the real-time status information and priority information of the order are monitored in real time, the initial weight parameters are dynamically adjusted, and the preliminary order sorting result is locally optimized, and constraint conditions are introduced for correction to generate a real-time sorting result, including:

[0086] Using the real-time status information and priority information, dynamically calculate the corresponding initial weight values for each optimization objective, and construct a multi-objective optimization framework based on the initial weight values; according to the multi-objective optimization framework, combine the information of all current orders to generate a preliminary order sorting result; during the process of generating the preliminary order sorting result, monitor the changes in the real-time status information and priority information of the order in real time, dynamically adjust the initial weights of the corresponding optimization objectives based on the changes to obtain updated weights; use the updated weight values to perform local optimization processing on the preliminary order sorting result, and introduce a series of constraint conditions for correction for critical orders to generate a real-time sorting result;

[0087] In this embodiment, the real-time status information includes data such as the current processing stage of the order and the estimated completion time, which is used to evaluate the order turnover efficiency; the priority information refers to the priority order of the order in the processing queue, usually set based on customer requirements or business rules; the optimization goal refers to the specific indicators that the system hopes to achieve, such as timeliness, cost control, and customer satisfaction. These information together constitute the basis of the order feature library, supporting the subsequent multi-objective optimization process. In addition, the constraint conditions are a series of restrictive conditions, such as timeliness requirements and cost limitations, to ensure that specific orders or order groups can meet specific business needs without affecting the overall efficiency;

[0088] In the embodiment of the present application, first, the real-time status information and priority information of the order are used to dynamically calculate the initial weight values of each optimization goal, and a multi-objective optimization framework is constructed based on these weight values. Then, combined with the information of all current orders, a preliminary order sorting result is generated. During this process, the system continuously monitors the status and priority changes of the orders. Once any significant changes are detected, the corresponding optimization goal weight values are immediately adjusted dynamically to obtain updated weights. Next, the updated weight values are used to perform local optimization processing on the preliminary order sorting result, and a series of constraint conditions are introduced for correction for critical orders to ensure that while meeting specific business needs, the overall process runs efficiently, and finally a real-time sorting result is generated;

[0089] For example, in a practical application scenario, an e-commerce company manages orders from multiple e-commerce platforms (such as platform A, platform B, and platform C). Whenever a new order is generated or the status of an existing order changes, the system automatically synchronizes the latest status and priority information from each platform and stores it in the order feature library. For example, a high-priority order from platform A is marked for urgent processing due to the urgent needs of the customer. The system dynamically calculates the initial weight values of optimization goals such as timeliness, cost control, and customer satisfaction based on the status and priority information of the order, and generates a preliminary order sorting result based on this. When it is detected that the estimated completion time of this order is delayed, the system immediately adjusts its weight value and performs local optimization on this order to ensure that it can be processed in the shortest time without affecting the normal turnover of other orders. In this way, the system not only improves the flexibility and response speed of order processing but also ensures the efficiency and stability of the entire order turnover process.

[0090] To solve the complexity problem in applying constraint conditions for local optimization based on the preliminary order sorting result and further improve the flexibility and efficiency in the order turnover process, as another embodiment, according to the previous embodiment, the updated weight values are used to perform local optimization processing on the preliminary order sorting result, and a series of constraint conditions are introduced for correction for specific orders to generate a real-time sorting result, including:

[0091] Based on the updated weight values and the preliminary order sorting results, identify the critical orders that need local optimization, and utilize the real-time status information and priority information of the critical orders to obtain optimization requirements; according to the optimization requirements, set a personalized set of constraint conditions for each critical order, and based on the set of constraint conditions, adjust the corresponding part in the preliminary order sorting results to obtain the sorted results of the corresponding part after local optimization processing; according to the sorted results of the corresponding part after local optimization processing, monitor the status changes of all affected orders and the impact of the affected orders on the overall sorting results in real time. When it is found that the overall efficiency decreases, re-evaluate and adjust the corresponding constraint conditions based on a preset feedback mechanism to obtain an adjusted set of constraint conditions; according to the adjusted set of constraint conditions, readjust the preliminary order sorting results so that all applied constraint conditions are correctly executed and no problems outside the existing goals are introduced. When an unexpected situation is detected, based on the adjustment process, correct the problematic part to obtain a sorting result that passes the review; based on the sorting result that passes the review, combine the sorted results of the corresponding part after local optimization processing to generate a real-time sorting result;

[0092] In this embodiment, the real-time status information includes data such as the current processing stage and the estimated completion time of the order, which is used to evaluate the order turnover efficiency; the priority information refers to the priority order of the order in the processing queue, which is usually set based on customer requirements or business rules; the set of constraint conditions is a series of restrictive conditions, such as timeliness requirements and cost limitations, to ensure that specific orders or order groups can meet specific business needs without affecting the overall efficiency. In addition, the feedback mechanism is an automatic detection and adjustment mechanism used to monitor problems that occur during the optimization process and make timely corrections to maintain the stability and efficiency of the system;

[0093] In the embodiments of the present application, first, based on the updated weight values and the preliminary order sorting results, key orders that need to be locally optimized are identified, and their optimization requirements are determined using the real-time status information and priority information of these orders. Then, according to the specific optimization requirements of each key order, a personalized set of constraint conditions is set, and based on this, the relevant part of the preliminary order sorting results is adjusted to obtain a partially sorted result after local optimization. Throughout the process, the system continuously monitors the status changes of all affected orders and their impact on the overall sorting result. Once any situation that may lead to a decrease in overall efficiency is detected, the corresponding constraint conditions are immediately re-evaluated and adjusted based on a preset feedback mechanism. Next, according to the adjusted set of constraint conditions, the preliminary order sorting results are adjusted again to ensure that all applied constraint conditions have been correctly executed and no new problems have been introduced. If an unexpected situation is detected, the problematic part is corrected based on the adjustment process, and finally, a sorting result that passes the review is obtained. Finally, based on the sorting result that passes the review and combining all the partially sorted results after local optimization, a final real-time sorting result is generated;

[0094] For example, in a practical application scenario, an e-commerce company manages orders from multiple e-commerce platforms (such as platform A, platform B, and platform C). Whenever a new order is generated or the status of an existing order changes, the system automatically synchronizes the latest status and priority information from each platform and stores it in the order feature library. For example, a high-priority order from platform A is marked for urgent processing due to the urgent needs of the customer. The system dynamically calculates the initial weight values of optimization objectives such as timeliness, cost control, and customer satisfaction based on the status and priority information of the order, and generates a preliminary order sorting result based on this. When it is detected that the estimated completion time of the order is delayed, the system immediately adjusts its weight value and performs local optimization on the order to ensure that it can be processed within the shortest possible time. At the same time, the system sets personalized constraint conditions for the order (such as it must be delivered within 24 hours) and makes corresponding adjustments to the preliminary order sorting results. During this process, the system continuously monitors the status changes of all affected orders and automatically triggers the feedback mechanism for adjustment when any situation that may lead to a decrease in overall efficiency is found, ensuring that the entire order flow process is both efficient and flexible. In this way, the system not only improves the flexibility and response speed of order processing but also ensures the efficiency and stability of the entire order flow process.

[0095] To address the challenge of balancing different optimization objectives in multi-objective optimization and further improve the dynamic response ability and flexibility in the order sorting process, as another embodiment, according to the previous embodiment, during the process of generating the preliminary order sorting result, the real-time status information and priority information of the orders are monitored in real time, and the initial weights of the corresponding optimization objectives are dynamically adjusted based on the changes to obtain updated weights, including:

[0096] Based on the multi-objective optimization framework, when generating the preliminary order sorting result, use a real-time monitoring mechanism to perform a preset periodic scan on the real-time status information and priority information of all orders, identify changes in the real-time status information and priority information, and record the time points and specific contents of the changes in the real-time status information and priority information to generate a dynamic change log; according to the content in the dynamic change log, analyze the impact degree of each order change on the optimization objective to which each order belongs to determine the optimization objective for adjusting the weight value; for the optimization objective determined to adjust the weight value, based on the current overall business requirements and system resource status, use the new status and priority information of the order to dynamically adjust the initial weight of the optimization objective to obtain the updated weight;

[0097] In this embodiment, the preset periodic scan means that the system automatically checks the status and priority information of all orders at a predetermined time interval to ensure that any changes are captured in a timely manner. The dynamic change log records the time points and specific contents of the changes in the status and priority of all orders for subsequent analysis of the impact of each order change on the optimization objective. Through these records, the system can accurately track the change trajectory of each order during its life cycle and make corresponding adjustments accordingly;

[0098] In the embodiment of the present application, first, based on the multi-objective optimization framework, while generating the preliminary order sorting result, use a real-time monitoring mechanism to perform a periodic scan on the real-time status information and priority information of all orders. After each scan, the system will identify any significant changes and record the time points and specific contents of these changes to form a dynamic change log. Next, according to the content in the dynamic change log, the system analyzes the impact degree of each order change on the optimization objective to which it belongs, evaluates whether this impact is sufficient to trigger the recalculation or adjustment of the weight value, and thus determines the optimization objective that needs to adjust the weight value. For those optimization objectives determined to need to adjust the weight value, the system dynamically adjusts the initial weights of these optimization objectives based on the current overall business requirements and system resource status, using the new status and priority information of the order, to obtain the updated weight. Throughout the process, the system continuously monitors and adjusts the weight value to ensure that the sorting result always reflects the latest business requirements and order status;

[0099] For example, in a practical application scenario, an e-commerce company manages orders from multiple e-commerce platforms (such as Platform X, Platform Y, and Platform Z). Whenever a new order is generated or the status of an existing order changes, the system automatically synchronizes the latest status and priority information from each platform and stores it in the order feature library. For example, a high-priority order is marked for urgent processing due to the urgent needs of the customer. Based on the status and priority information of the order, the system dynamically calculates the initial weight values of optimization objectives such as timeliness, cost control, and customer satisfaction, and generates a preliminary order sorting result based on this. During the process of generating the preliminary order sorting result, the system periodically scans all order status and priority information according to a preset periodic scanning mechanism. When it detects a change in the status of a certain order (such as a logistics delay), the system immediately records this change and analyzes its impact on the timeliness optimization objective. If it is found that this change seriously affects the achievement of the timeliness objective, the system will dynamically adjust the initial weight of this optimization objective and recalculate the preliminary order sorting result accordingly to ensure that the affected orders can be processed as soon as possible. In addition, the system will continue to monitor the status changes of all affected orders and adjust the weight value again when necessary to ensure the efficiency and flexibility of the entire order flow process. In this way, the system not only improves the flexibility and response speed of order processing but also ensures the efficiency and stability of the entire order flow process.

[0100] To address the challenges of cross-platform data consistency and execution coherence in the order flow process, and to further improve order processing efficiency and customer satisfaction, in some embodiments, as described in step 104, through the system integration interface, the order flow instruction is docked with the order processing systems of multiple e-commerce platforms to ensure data consistency and execution coherence in each link of the order flow process, including:

[0101] Based on the real-time sorting result, plan the flow path for each order and formulate a flow plan that matches each order; according to the flow plan, generate an order flow instruction for each order; use the system integration interface to convert the order flow instruction into a data packet that conforms to the format of the order processing systems of each e-commerce platform, and send it to the corresponding e-commerce platform through a communication channel; after the data packet is sent, implement a real-time tracking mechanism to monitor the execution status of each order within the e-commerce platform in real time, so that the order flows according to the predetermined plan;

[0102] In this embodiment, the transfer path planning refers to determining the specific transfer path of each order from generation to final completion based on information such as the priority and timeliness requirements of the order; the order transfer instruction is a specific operation guide generated for each order to ensure that the order can be efficiently transferred along the predetermined path; the system integration interface is a middleware used to connect the order processing systems of different e-commerce platforms to ensure the consistency and accuracy of data transmission; the real-time tracking mechanism continuously monitors the execution status of each order during the transfer process to ensure that it proceeds as planned and can promptly detect and resolve abnormal situations;

[0103] In the embodiment of the present application, first, based on the real-time sorting result, the system makes a detailed plan for the transfer path of each order. Considering the processing capabilities and rules of different e-commerce platforms, a specific transfer plan suitable for each order is formulated. Then, according to these detailed transfer plans, the system generates order transfer instructions for each order to ensure that the instructions can accurately guide the transfer process of the order within each e-commerce platform. Next, using the system integration interface, these customized order transfer instructions are converted into data packets conforming to the formats of the order processing systems of each e-commerce platform and sent to the corresponding e-commerce platforms through a secure and reliable communication channel. After the data packet is sent, the system implements a real-time tracking mechanism to continuously monitor the execution status of each order within the e-commerce platform to ensure that the order is smoothly transferred according to the predetermined plan. If any abnormal situation (such as delay or error) is detected, the system will immediately take corresponding measures for adjustment to ensure the coherence and consistency of the entire process;

[0104] For example, in an actual application scenario, an e-commerce company manages orders from multiple e-commerce platforms (such as Platform X, Platform Y, and Platform Z). Whenever a new order is generated, the system plans the best transfer path for each order according to its real-time sorting result and generates customized order transfer instructions. For example, for a high-priority order that requires fast delivery, the system formulates a special transfer path for it to ensure its priority processing and fast delivery. Subsequently, the system converts these customized order transfer instructions into data packets conforming to the formats of the order processing systems of each e-commerce platform and sends them to the corresponding e-commerce platforms through a secure channel. Once the data packet is successfully sent, the system starts the real-time tracking mechanism to continuously monitor the execution status of the order within the e-commerce platform. If the system detects that a certain order has a logistics delay, it will immediately trigger an early warning mechanism and notify the relevant parties to take emergency measures, such as rearranging the logistics or adjusting the order priority, to ensure the order is delivered on time. In this way, the system not only improves the flexibility and response speed of order processing but also ensures the efficiency and stability of the entire order transfer process.

[0105] To solve the problems of inconsistent formats and data processing complexity faced when obtaining order data from multiple e-commerce platforms, and to further improve the efficiency and accuracy of order processing, in some embodiments, as described in step 101, based on the order data of multiple e-commerce platforms, real-time status information, priority information, and multiple optimization goals of the order data are obtained in real time, including: establishing a data acquisition framework based on the interfaces provided by multiple e-commerce platforms, and using the data acquisition framework to grab the original order-related data from each e-commerce platform in real time; using the unified data acquisition framework to standardize the original data of each e-commerce platform, converting order data in different formats and structures into a unified format to obtain standardized order data; according to the standardized order data, extracting the real-time status information and priority information of each order, and associating the real-time status information and priority information with the order identifier to generate order description information; during the process of extracting the real-time order status information and priority information, identifying and defining multiple optimization goals;

[0106] In this embodiment, the unified data acquisition framework is a middleware or toolset for connecting different e-commerce platform interfaces and grabbing the original order-related data therefrom; the standardization process refers to converting order data from different platforms into a unified format for subsequent processing; the order description information includes the basic information of the order (such as status, priority) and its unique identifier, and is used to accurately describe and manage each order; the optimization goal is a specific indicator that the system hopes to achieve, such as timeliness, cost control, and customer satisfaction, etc., and these goals guide the sorting and optimization process of the system;

[0107] In the embodiments of the present application, first, a data acquisition framework is established based on the interfaces provided by multiple e-commerce platforms. This framework can grab the original order-related data from each e-commerce platform in real time. Then, using this unified data acquisition framework, the original data of each e-commerce platform is standardized, converting order data in different formats and structures into a unified format to ensure that all data has consistency and comparability. Next, the system extracts the real-time status information and priority information of each order according to the standardized order data, and associates them with the order identifier to generate detailed order description information. During this process, the system also identifies and defines multiple optimization goals, which cover aspects such as timeliness, cost control, and customer satisfaction, providing a basis for subsequent multi-objective optimization. In this way, the system not only improves the efficiency and accuracy of data processing, but also lays a solid foundation for subsequent order sorting and optimization;

[0108] For example, in a practical application scenario, an e-commerce company needs to manage orders from multiple e-commerce platforms (such as Platform A, Platform B, and Platform C). The company has established a unified data collection framework that can synchronize the latest order data from each e-commerce platform in real time. Whenever a new order is generated or the status of an existing order changes, the system automatically fetches the relevant data from each platform and converts these data into a unified format through standardized processing. For example, when an order is marked as high-priority due to the urgent needs of the customer, the system extracts the real-time status information and priority information of the order and associates them with the order identifier to generate detailed order description information. At the same time, the system identifies optimization goals such as timeliness, cost control, and customer satisfaction, and formulates corresponding strategies based on these goals. In this way, the system not only improves the efficiency and accuracy of order data processing but also ensures the efficiency and flexibility of the entire order flow process. This enables the company to quickly respond to customer needs in a complex business environment, improving overall operational efficiency and service quality.

[0109] To solve the problems of timely identification and effective handling of abnormal states in the order flow process, and to further improve the stability and response speed during the order processing, in some embodiments, as described in step 105, during the order flow process, the execution status of the order is monitored in real time, and according to the preset abnormal detection rules, the abnormal states occurring during the flow process are identified, and an abnormal handling instruction is generated. The abnormal handling instruction is used to adjust the execution order of the order, including:

[0110] After the order flow instruction is distributed to the order processing systems of multiple e-commerce platforms, a cross-platform real-time monitoring framework is established using the integration interface; using the real-time monitoring framework, a preset periodic check is performed on the status of each order, various data points related to the order execution are collected, and an order execution log is generated; according to the information in the order execution log, combined with the preset abnormal detection rules, a comprehensive evaluation is performed on the execution status of each order to identify situations that do not conform to the expected process, so as to determine the existing abnormal states; for the abnormal states, based on the type and severity of the abnormal states, a corresponding response mechanism is automatically triggered. The response mechanism includes: pausing the further flow of the affected order, notifying the relevant parties to take actions, and generating an abnormal handling instruction;

[0111] In this embodiment, the cross-platform real-time monitoring framework is a middleware that connects the order processing systems of multiple e-commerce platforms through an integration interface, and can monitor the execution status of each order in its life cycle in real time; the order execution log records the data points at each stage during the transfer process of each order, such as processing time, current step, estimated completion time, etc., and is used to analyze the execution of the order subsequently; the anomaly detection rules are a series of predefined criteria for identifying anomalies in the order transfer process; the response mechanism is a series of measures taken for the identified abnormal status, aiming to correct the anomaly and restore the normal order transfer;

[0112] In the embodiment of the present application, first, after the order transfer instruction is distributed to the order processing systems of multiple e-commerce platforms, a cross-platform real-time monitoring framework is established using the integration interface. This framework can continuously track the execution status of each order in its life cycle. Then, using this real-time monitoring framework, the system periodically checks the status of each order, collects various data points related to order execution (such as processing time, current step, estimated completion time, etc.), and forms a detailed order execution log. Next, based on the information in the order execution log and in combination with the preset anomaly detection rules, the system comprehensively evaluates the execution status of each order, identifies any situations that deviate from the expected process or standard operations, and determines whether there is an abnormal status. Once an abnormal status is identified, the system automatically triggers the corresponding response mechanism based on its type and severity, such as pausing the further transfer of the affected order, notifying the relevant parties to take actions, and generating anomaly handling instructions, to ensure that the order can resume normal transfer as soon as possible. In this way, the system not only improves the efficiency of anomaly detection and handling, but also ensures the stability and reliability of the entire order transfer process;

[0113] For example, in a practical application scenario, an e-commerce company manages orders from multiple e-commerce platforms (such as Platform X, Platform Y, and Platform Z). Whenever the order transfer instruction is distributed to the order processing systems of each platform, the system establishes a cross-platform real-time monitoring framework using the integration interface to monitor the status of all orders in real time. For example, if a certain order fails to be delivered on time due to a logistics delay, the system generates an order execution log through the relevant data points (such as extended processing time, stagnation of the current step, etc.) collected by periodic checks. Based on this information and in combination with the preset anomaly detection rules, the system identifies that this order has an abnormal status. Since this anomaly seriously affects the timeliness of the order, the system automatically triggers the response mechanism, pauses the further transfer of this order, and notifies the relevant departments to take emergency measures, such as re-arranging logistics or adjusting the order priority. At the same time, the system generates anomaly handling instructions to ensure that the affected order can resume normal transfer in the shortest possible time. In this way, the system not only improves the flexibility and response speed of order processing, but also ensures the efficiency and stability of the entire order transfer process;

[0114] Figure 2 This is a schematic structural diagram of a multi-e-commerce platform order transfer device (or system) provided by an embodiment of the present application based on a sorting optimization algorithm. As Figure 2 shown, the device includes:

[0115] An acquisition module 21, configured to obtain real-time status information, priority information, and multiple optimization objectives of order data in real time according to order data of multiple e-commerce platforms;

[0116] A correction module 22, configured to dynamically calculate the initial weight of each optimization objective by using the real-time status information and priority information of the order, construct a multi-objective optimization function based on the initial weight and perform iterative optimization to generate a preliminary order sorting result, monitor the changes in the real-time status information and priority information of the order in real time during the iteration process, dynamically adjust the initial weight parameters, and perform local optimization on the preliminary order sorting result, introduce constraint conditions for correction, and generate a real-time sorting result;

[0117] A distribution module 23, configured to generate an order transfer instruction according to the real-time sorting result and distribute the order transfer instruction to the order processing systems of multiple e-commerce platforms;

[0118] An interface module 24, configured to dock the order transfer instruction with the order processing systems of multiple e-commerce platforms through a system integration interface, so that the data in each link during the order transfer process has consistency and execution coherence;

[0119] An identification module 25, configured to monitor the execution status of the order in real time during the order transfer process, and identify abnormal statuses occurring during the transfer process according to preset abnormal detection rules, and generate an abnormal handling instruction, where the abnormal handling instruction is used to adjust the execution order of the order.

[0120] Figure 2 The multi-e-commerce platform order transfer device based on the sorting optimization algorithm described above can execute Figure 1 the multi-e-commerce platform order transfer method based on the sorting optimization algorithm described in the embodiment shown, and its implementation principle and technical effects will not be elaborated. For the multi-e-commerce platform order transfer device based on the sorting optimization algorithm in the above embodiment, the specific manners in which each module and unit perform operations have been described in detail in the embodiment related to the method, and will not be elaborated here.

[0121] In a possible design, Figure 2 the multi-e-commerce platform order transfer device based on the sorting optimization algorithm described in the embodiment shown can be implemented as a computing device. As Figure 3 shown, the computing device may include a storage component 31 and a processing component 32;

[0122] The storage component 31 stores one or more computer instructions, and among them, the one or more computer instructions are called and executed by the processing component 32.

[0123] The processing component 32 is used for the above Figure 1 A multi-e-commerce platform order transfer method based on a sorting optimization algorithm in the above embodiment.

[0124] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0125] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0126] Of course, the computing device may necessarily further include other components, such as an input / output interface, a display component, a communication component, etc.

[0127] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0128] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0129] Among them, the computing device may be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device may refer to a cloud server, and the above processing component, storage component, etc. may be basic server resources leased or purchased from a cloud computing platform.

[0130] The embodiment of the present application also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 A multi-e-commerce platform order transfer method based on a sorting optimization algorithm in the above embodiment.

[0131] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0132] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-e-commerce platform order flow method based on a sorting optimization algorithm, characterized in that: include: Based on the order data of multiple e-commerce platforms, obtain the real-time status information, priority information and multiple optimization goals of the order data in real time; Using the real-time status information and priority information of the order, dynamically calculate the initial weight of each optimization target, build a multi-objective optimization function based on the initial weight and perform iterative optimization to generate a preliminary order sorting result, monitor the changes of the real-time status information and priority information of the order in real time during the iteration process, dynamically adjust the initial weight parameters, and locally optimize the preliminary order sorting result, introduce constraints to make corrections, and generate a real-time sorting result; Generate an order transfer instruction based on the real-time sorting result, and distribute the order transfer instruction to the order processing systems of multiple e-commerce platforms; Through the system integration interface, the order flow instruction is connected with the order processing systems of multiple e-commerce platforms to ensure data consistency and execution continuity in each link of the order flow process; During the order flow process, the execution status of the order is monitored in real time, and according to the preset exception detection rules, the abnormal status occurring during the flow process is identified, and exception handling instructions are generated. The exception handling instructions are used to adjust the execution order of the order.

2. The method according to claim 1, characterized in that Using the real-time status information and priority information of the order, dynamically calculate the initial weight of each optimization target, build a multi-objective optimization function based on the initial weight and perform iterative optimization to generate a preliminary order sorting result, monitor the changes of the real-time status information and priority information of the order in real time during the iteration process, dynamically adjust the initial weight parameters, and locally optimize the preliminary order sorting result, introduce constraints for correction, and generate a real-time sorting result, including: Using the real-time status information and priority information, dynamically calculate the corresponding initial weight value for each optimization target, and build a multi-objective optimization framework based on the initial weight value; According to the multi-objective optimization framework, combined with the information of all current orders, a preliminary order ranking result is generated; In the process of generating the preliminary order sorting result, the changes of the real-time status information and the priority information of the order are monitored in real time, and the initial weights of the corresponding optimization objectives are dynamically adjusted based on the changes to obtain updated weights; The updated weight values ​​are used to locally optimize the preliminary order sorting results, and a series of constraints are introduced for key orders to make corrections and generate real-time sorting results.

3. The method according to claim 2, characterized in that Use the updated weight values ​​to locally optimize the preliminary order sorting results, introduce a series of constraints for specific orders, and generate real-time sorting results, including: Based on the updated weight values ​​and the preliminary order sorting results, key orders for local optimization are identified, and the optimization requirements are obtained using the real-time status information and priority information of the key orders; According to the optimization requirements, a personalized set of constraints is set for each key order, and based on the set of constraints, a corresponding part of the preliminary order sorting result is adjusted to obtain a sorting result of the corresponding part after local optimization processing; According to the partial sorting results after the local optimization, the status changes of all affected orders and the impact of the affected orders on the overall sorting results are monitored in real time. When a situation that leads to a decrease in overall efficiency is found, the corresponding constraint conditions are re-evaluated and adjusted based on a preset feedback mechanism to obtain an adjusted constraint condition set; According to the adjusted set of constraints, the preliminary order sorting results are adjusted again, so that all applied constraints are correctly executed and no problems outside the existing goals are introduced. When a situation that does not meet expectations is detected, the problematic part is corrected based on the adjustment process to obtain a sorting result that has passed the review; Based on the sorting results that have passed the review and combined with the corresponding partial sorting results that have undergone local optimization processing, real-time sorting results are generated.

4. The method according to any one of claims 1 to 3, characterized in that In the process of generating the preliminary order sorting result, real-time monitoring of changes in order status information and priority information is performed, and initial weights of corresponding optimization targets are dynamically adjusted based on the changes to obtain updated weights, further comprising: Based on the multi-objective optimization framework, when generating preliminary order sorting results, a real-time monitoring mechanism is used to perform a preset periodic scan on the real-time status information and priority information of all orders, identify changes in the real-time status information and the priority information, and record the time point and specific content of the real-time status information change and the priority information change, and generate a dynamic change log; Analyze the influence of each order change on the optimization target of each order according to the content in the dynamic change log, so as to determine the optimization target for adjusting the weight value; For the optimization target determined to have an adjusted weight value, based on the current overall business demand and system resource status, the initial weight of the optimization target is dynamically adjusted using the status and priority information outside the existing target of the order to obtain an updated weight.

5. The method according to claim 1, characterized in that Through the system integration interface, the order flow instructions are connected with the order processing systems of multiple e-commerce platforms to ensure data consistency and execution continuity in each link of the order flow process, including: Based on the real-time sorting results, the flow path of each order is planned, and a flow plan matching each order is formulated; Generate an order transfer instruction for each order according to the transfer plan; Using the system integration interface, the order flow instruction is converted into a data packet that conforms to the format of the order processing system of each e-commerce platform, and the data packet is sent to the corresponding e-commerce platform through a communication channel; After the data packet is sent, a real-time tracking mechanism is implemented to monitor the execution status of each order in the e-commerce platform in real time, so that the order can be circulated according to the predetermined plan.

6. The method according to claim 1, characterized in that Based on the order data of multiple e-commerce platforms, real-time status information, priority information and multiple optimization goals of order data are obtained in real time, including: Based on the interfaces provided by multiple e-commerce platforms, a data collection framework is established, and the data collection framework is used to capture the original data related to orders from each e-commerce platform in real time; Using the data collection framework, the original data of each e-commerce platform is standardized, and the order data of different formats and structures are converted into a unified format to obtain standardized order data; Extracting real-time status information and priority information of each order based on the standardized order data, and associating the real-time status information and priority information with an order identifier to generate order description information; Identify and define multiple optimization goals in the process of extracting real-time order status and priority information.

7. The method according to claim 1, characterized in that During the order flow process, the execution status of the order is monitored in real time, and according to the preset exception detection rules, the abnormal state that occurs during the flow process is identified, and an exception handling instruction is generated. The exception handling instruction is used to adjust the execution order of the order, including: After the order flow instructions are distributed to the order processing systems of multiple e-commerce platforms, a cross-platform real-time monitoring framework is established using an integrated interface; Using the real-time monitoring framework, the status of each order is checked periodically, various data points related to order execution are collected, and order execution logs are generated; Based on the information in the order execution log and in combination with the preset anomaly detection rules, a comprehensive evaluation is performed on the execution status of each order to identify situations that do not conform to the expected process, so as to determine the existence of abnormal status; For the abnormal state, based on the type and severity of the abnormal state, a corresponding response mechanism is automatically triggered, and the response mechanism includes: suspending further circulation of the affected orders, notifying relevant parties to take actions, and generating exception handling instructions.

8. A multi-e-commerce platform order flow system based on a sorting optimization algorithm, characterized in that: include: The acquisition module is used to obtain the real-time status information, priority information and multiple optimization targets of the order data in real time based on the order data of multiple e-commerce platforms; A correction module is used to use the real-time status information and priority information of the order to dynamically calculate the initial weight of each optimization target, construct a multi-objective optimization function based on the initial weight and perform iterative optimization to generate a preliminary order sorting result, monitor the changes of the real-time status information and priority information of the order in real time during the iteration process, dynamically adjust the initial weight parameters, and locally optimize the preliminary order sorting result, introduce constraint conditions for correction, and generate a real-time sorting result; A distribution module, used to generate an order flow instruction according to the real-time sorting result, and distribute the order flow instruction to the order processing systems of multiple e-commerce platforms; A docking module is used to connect the order flow instruction with the order processing systems of multiple e-commerce platforms through a system integration interface, so that the data of each link in the order flow process is consistent and the execution is coherent; The identification module is used to monitor the execution status of orders in real time during the order flow process, and to identify abnormal conditions that occur during the flow process according to preset abnormality detection rules, and to generate abnormality handling instructions, which are used to adjust the execution order of the orders.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-e-commerce platform order flow method based on a sorting optimization algorithm as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multi-e-commerce platform order flow method based on a sorting optimization algorithm as described in any one of claims 1 to 7 is implemented.

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