Big data-based integral shopping mall order rolling processing optimization method and system

Abstract

The invention provides an integral shopping mall order rolling processing optimization method and system based on big data, and the method comprises the steps: employing a dynamic priority adjustment mechanism for a conflict order list, and adjusting the order processing priority based on the user behavior track data, including historical exchange preference, and obtaining a resource distribution scheme; integrating the order sequence through a queue reordering mechanism, reordering based on the resource allocation scheme to obtain a final processing sequence, preferentially executing the high-priority request, and maintaining the queue stability at the same time; if residual conflicts exist in the final processing sequence, supplementary resources are extracted from a preset standby inventory through a standby resource set scheduling mechanism, a complete execution plan is obtained, and the plan covers all order processing paths; and on the basis of the complete execution plan, monitoring the order processing progress, and obtaining a stable operation order flow through progress tracking and anomaly detection, real-time state feedback and iterative adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and particularly relates to an order rolling processing optimization method and system for an integral mall based on big data. BACKGROUND

[0002] As an important part of e-commerce and user incentive mechanism, the integral mall plays a key role in improving user participation and platform loyalty. With the rapid development of digital economy, the integral mall needs to handle a large amount of order data and meet the diversified redemption needs of users. Especially during peak periods such as promotional activities or holidays, the order processing efficiency and rationality of resource allocation directly affect user experience and platform operation effect. However, the traditional order processing method is difficult to meet the demand of modern integral mall for efficient and intelligent processing when facing complex and variable user behavior and dynamic resource demand.

[0003] Currently, the order processing method of the integral mall relies on static rules or simple priority scheduling, which is difficult to cope with the dynamic changes of user redemption behavior. For example, the existing system is usually based on fixed order queue and cannot flexibly adjust resource allocation according to user behavior or seasonal consumption trends. This method is prone to cause uneven resource allocation during peak periods, and some order processing is delayed, while other resources are in idle state. In addition, the existing system lacks real-time monitoring capability for order processing links, and cannot quickly identify the cause of delay, resulting in a decline in user experience. This static and fragmented processing method limits the running efficiency of the integral mall in complex scenarios.

[0004] In the order processing of the integral mall, one of the core technical difficulties is how to realize the dynamic adjustment of the order queue to adapt to the fluctuation of user redemption demand. The complexity of user behavior trajectory and consumption pattern makes the prediction of order demand extremely difficult. For example, in large-scale promotional activities, users may concentrate on redeeming popular goods, leading to intensified competition for specific resources, while the existing system is difficult to dynamically allocate processing capacity according to real-time demand. The lack of dynamic adjustment directly leads to low order processing efficiency, and even causes system congestion.

[0005] Another key technical difficulty is the conflict management of order competing resources. When multiple orders compete for limited processing resources in the same time period, such as limited inventory of popular goods, the system often cannot effectively identify and handle such conflicts. The competition for resources between orders may cause some orders to be delayed in processing, or even fail due to insufficient resources. For example, when a user tries to redeem a limited quantity of goods, the system may fail to detect other orders' competing requests in time, leading to chaotic processing timing, and the user ultimately faces the problem of redemption failure or long waiting time. The lack of conflict management further exacerbates the complexity of order processing.

[0006] How to dynamically adjust order queue and effectively manage resource competition in high concurrency scenarios has become a key problem for the integral mall to improve operation efficiency. In actual business, for example, during the promotion period, the integral mall may receive thousands of exchange requests at the same time, while the inventory of popular goods and processing capacity are limited, the system needs to quickly determine which orders can be processed first, and how to avoid processing failure caused by resource competition. Therefore, solving the problem of dynamic adjustment of order queue and resource conflict management has become a key problem for the integral mall to realize intelligent and smooth operation. SUMMARY

[0007] In one aspect, the application provides an integral mall order rolling processing optimization method based on big data, mainly including: By collecting integral mall order inflow data and user behavior trajectory data, calculating demand change parameters, and combining historical load data and preset load coefficients, the peak load level is determined, which reflects the peak demand of system processing capacity; Based on the peak load level, the priority sorting algorithm is used to classify the order queue, and the order sequence is obtained, which divides the order into high priority request and regular request; When the proportion of high priority request in the order sequence exceeds the preset threshold, by comparing the number of requests within the concurrent access window with the real-time inventory quantity multiplied by the inventory safety margin coefficient, the conflict point of insufficient inventory is identified, and the conflict order list is obtained; For the conflict order list, a preset weighted scoring model is used, combined with the historical exchange preference features in the user behavior trajectory data, to adjust the order processing priority, and obtain a resource allocation scheme; The order sequence is integrated through the queue reordering mechanism, and the order is rearranged based on the resource allocation scheme to obtain the final processing order; If there is a remaining conflict in the final processing order, the standby resource set scheduling mechanism is used to extract supplementary resources from the pre-device inventory, and a complete execution plan covering all order processing paths is obtained; Based on the complete execution plan, the order processing progress is monitored, the progress tracking and abnormal detection are performed, the real-time state is fed back, and the iterative adjustment is performed to obtain a stable running order flow.

[0008] In another aspect, the application also provides a big data-based integral mall order rolling processing optimization system, mainly comprising: a data acquisition and demand calculation module, configured to acquire integral mall order inflow data and user behavior trajectory data, calculate demand change parameters, and determine peak period load level by combining historical load data and a preset load coefficient, wherein the level reflects the peak demand of system processing capacity; an order classification and queue management module, configured to classify order queues based on the peak period load level by using a priority sorting algorithm, and obtain an order sequence in which orders are divided into high-priority requests and regular requests; a resource competition detection module, configured to, when the proportion of high-priority requests in the order sequence exceeds a preset threshold, identify conflict points of insufficient inventory by comparing the number of requests within a concurrent access window with real-time inventory multiplied by an inventory safety margin coefficient, and obtain a conflict order list; a dynamic priority adjustment module, configured to, for the conflict order list, adjust order processing priority by using a preset weighted scoring model in combination with historical exchange preference features in the user behavior trajectory data, and obtain a resource allocation scheme; a queue reordering module, configured to integrate the order sequence by a queue reordering mechanism, rearrange the order based on the resource allocation scheme, and obtain a final processing order; a standby resource scheduling module, configured to, if there is a remaining conflict in the final processing order, extract supplementary resources from a pre-device inventory by a standby resource set scheduling mechanism, and obtain a complete execution plan covering all order processing paths; a progress monitoring and iterative adjustment module, configured to monitor order processing progress based on the complete execution plan, feed back real-time status by progress tracking and abnormality detection, and obtain a stable running order stream through iterative adjustment. The technical scheme provided by the embodiment of the application can include the following beneficial effects. The application discloses a big data-based integral mall order rolling processing optimization method and system, and aims at the problems of integral mall peak period load surge, resource competition conflict and low order processing efficiency. The method and system fuse user behavior trajectory data and order inflow data to construct a dynamically optimized order processing flow. The method and system acquire browsing duration and exchange frequency, calculate demand change parameters to determine peak period load level, divide orders into high-priority and regular requests by using a priority sorting algorithm, identify conflict orders by scanning inventory and concurrent access state by using a resource competition detection module when the proportion of high-priority requests exceeds a threshold, and optimize resource allocation scheme by a dynamic priority adjustment mechanism in combination with historical exchange preference. The method and system integrate order sequence by a queue reordering mechanism, preferentially execute high-priority requests, supplement inventory by a standby resource scheduling mechanism, generate a complete execution plan covering all orders, and ensure stable running of order stream by real-time monitoring and abnormality detection iterative adjustment. The method and system improve integral mall peak period order processing efficiency, optimize resource allocation, reduce conflict rate, and realize improvement of system stability and user experience. Attached Figure Description

[0009] Figure 1 This is a flowchart of an optimization method for the rolling processing of orders in a points mall based on big data, according to the present invention.

[0010] Figure 2 This is a schematic diagram of the DVP demand change parameter calculation model in an embodiment of the present invention.

[0011] Figure 3 This diagram illustrates the concurrent access window (CAW) and inventory safety margin (ISM) detection mechanism of this invention.

[0012] Figure 4 This is a schematic diagram illustrating the working principle of the dynamic priority adjustment mechanism in an embodiment of the present invention.

[0013] Figure 5 A flowchart of the standby resource set scheduling mechanism according to an embodiment of the present invention.

[0014] Figure 6 This is a schematic diagram of the structure of an order rolling processing optimization system for an points mall based on big data, according to the present invention.

[0015] Figure 7 This is a comparison chart showing the effectiveness of the method of this invention and existing technologies in handling scarce goods during peak periods. Detailed Implementation

[0016] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0017] Example 1 like Figure 1 This embodiment presents an optimization method for the rolling processing of orders in a points mall based on big data, which may specifically include: S101. By collecting order inflow data and user behavior trajectory data from the points mall, including browsing time and redemption frequency, the demand change parameters are calculated to obtain the peak load level, which reflects the peak demand of the system's processing capacity.

[0018] For example, such as Figure 2As shown, in one embodiment, to accurately quantify the system pressure in the future period of time, the present application first calculates a Demand Variation Parameter (DVP). Specifically, the system collects the behavior trajectory data of all users in a preset time window T (e.g., the past 1 hour), and calculates the DVP according to the following formula: .

[0019] Wherein: i and j respectively traverse the browsing events and the exchange events in the time window T.

[0020] B i is the effective browsing duration of the i-th browsing event, which refers to the duration that the user stays on the product page for more than a preset threshold (e.g., 5 seconds).

[0021] Ej is the high-value identifier of the j-th exchange event, for example, 1 if the user exchanges a popular product or a high-credit product, otherwise 0.

[0022] w i and wj are time decay weights, for example w = e^(-λ*Δt), where Δt is the time from the occurrence of the event to the present, and λ is the decay coefficient. Its value range is usually between 0.1 and 0.5, used to adjust the influence of recent behavior on the prediction result, the larger the λ value, the higher the weight of recent behavior, which makes the recent behavior have a higher weight.

[0023] α and β are the business weight coefficients of browsing and exchange behavior.

[0024] N_B and N_E are the total number of browsing and the total number of exchange in the time window T, respectively.

[0025] The DVP value intuitively reflects the user attention input corresponding to the unit exchange behavior, the higher the value, the stronger the potential exchange demand.

[0026] Subsequently, the system determines the Peak Load Level (PLL) according to the DVP value, and the calculation formula is: .

[0027] Wherein: C_base is the base processing capacity of the system in the regular period, for example, processing 100 orders per second.

[0028] K_load is a preset load coefficient used to adjust the influence of DVP on the load level.

[0029] For example, if the DVP is calculated to be 0.8 and K load is 0.5, the predicted peak load level will reach 1.4 times the baseline processing capacity. The system accordingly performs resource pre-allocation, such as dynamically increasing server instances or database connection pool size. This quantitative prediction method provides accurate data input for subsequent order processing strategies.

[0030] S102, based on the peak load level, the order queue is classified by a priority sorting algorithm to obtain a classified order sequence, the sequence divides the orders into high-priority requests and regular requests, the high-priority requests involve high-frequency exchange of integral commodities.

[0031] According to the load level evaluation, high-frequency exchange identification features are extracted from the order queue using behavior trajectory fusion, the extraction is obtained by counting exchange frequency and combining user request layering to obtain a preliminary priority list, the list marks high-frequency exchange integral commodities as high-priority requests. For the preliminary priority list, peak demand response data is obtained in combination with a priority sorting mechanism to perform layered processing on the order queue, the peak demand response data is derived from the load level evaluation, and a classified order sequence is determined, which distinguishes high-priority requests from regular requests. From the classified order sequence, if the proportion of high-priority requests exceeds a preset threshold, the integral commodity division is adjusted by resource priority allocation, the adjustment uses sequence generation logic to fuse behavior trajectories to obtain an optimized sequence response scheme. The high-frequency exchange referred to in the present application refers to the commodity whose exchange frequency exceeds the average exchange frequency of all commodities within a preset statistical period (such as the past 24 hours).

[0032] For example, in one embodiment, based on the previously calculated peak load level, the system classifies the order queue using a priority sorting algorithm.

[0033] Specifically, the peak load level is used as an input parameter to adjust the threshold of the algorithm. For example, when the load level is higher than 120% of the normal value, the algorithm will preferentially identify orders involving high-frequency exchange of integral commodities. This way ensures that the system can efficiently allocate resources during the peak period of the integral mall. Further, the implementation process of the priority sorting algorithm includes several key steps. First, the system extracts the relevant attributes of each order from the order queue, such as the exchange frequency of integral commodities and user behavior data. High-frequency exchange integral commodities refer to those commodities whose exchange frequency exceeds the average value within the past 24 hours.

[0034] For example, popular electronic coupons or virtual props. By comparing the exchange frequency of each order with a preset threshold, the algorithm calculates a priority score. The specific process is: a score is assigned to each order, and the formula is based on the frequency weight.

[0035] For example, the order queue is sorted by the frequency of the items, and the orders of the items with high frequency are added 30% to the score. The sorted order sequence is obtained, and the high priority requests are placed at the front of the queue, and the regular requests are placed at the back. The high priority requests are specifically related to the high frequency exchange of the integral items, and the orders are ensured to be processed in priority during the load peak, avoiding delay. The principle of the algorithm is to dynamically adjust the sorting rule according to the load level.

[0036] For example, in a high load scenario, the priority weight of the high frequency items is increased to maintain the system response speed. Such detailed classification logic helps to optimize the order flow in the integral mall, especially during the active period of users. When the system monitors that the load level rises during the peak period, the priority sorting algorithm is automatically triggered to classify the real-time order queue. Assuming that an order queue contains 10 orders, 3 of which are related to high frequency exchange of integral items such as integral purchase of mobile phone accessories, these are classified as high priority requests, and the remaining orders such as low frequency home appliance orders are regular requests. The sorted sequence allows the system to process the high priority part first, thereby reducing the overall waiting time.

[0037] In a possible implementation, the technical solution is extended to the member activity scenario of the integral mall. Specifically, when the member day activity causes a surge in the load level, the algorithm classifies the order queue and prioritizes the orders of the high frequency exchange of integral items.

[0038] For example, the integral gift package frequently exchanged by the user is identified as high priority, ensuring that these requests are executed in advance in the queue, while the regular requests such as one-time integral query are placed at the back. Such classification enhances the robustness of the system and adapts to different activity intensities in the same field.

[0039] It should be noted that the business process of the priority sorting algorithm emphasizes objective classification criteria. The determination of high frequency exchange is based on historical data statistics. For example, the system queries the exchange records of the items in the database, calculates the average frequency, and if the exchange times of a certain item exceed a threshold such as 50 times per day, the related order is given high priority. After obtaining the sorted order sequence, the system can schedule server resources accordingly to ensure smooth operation under the load level during the peak period.

[0040] S103、When the proportion of high priority requests in the order sequence exceeds a preset threshold, the number of requests in the concurrent access window is compared with the real-time inventory multiplied by the inventory safety margin coefficient to identify the conflict point of inventory shortage, and a conflict order list is obtained.

[0041] For example, the integral mall is a platform for users to exchange integral for goods. The system needs to process a large number of orders in a short time. In order to ensure the smooth operation of the system, the system needs to classify the order queue according to the priority of the order, and then process the orders in the order queue according to the priority. The order queue is classified according to the priority of the order, and the order queue is classified according to the priority of the order. Figure 3As shown, in an embodiment, when the high-priority request proportion exceeds a preset threshold (for example, 10%), the system starts the resource competition detection module. In order to accurately identify the real system bottleneck caused by the instantaneous traffic flood peak, rather than misjudgment caused by long-time accumulated requests, the module does not simply compare the total number of requests with the inventory, but uses more refined judgment logic. First, the module defines a concurrent access window (CAW) with a time length of At, for example, 2 seconds. The module real-time counts the number of all exchange requests for the same high-priority commodity N_req in any CAW, which reflects the maximum concurrent pressure borne by the system at the moment. Second, the module introduces the concept of inventory safety margin (ISM), whose value is a coefficient S_factor less than 1, for example, 0.9, which represents the proportion of inventory reserved to respond to emergencies. When making conflict judgment, only 90% of the real-time inventory is considered as safe allocation capacity, and the remaining 10% is reserved. The trigger condition of conflict is defined as: N_req>(Current_Stock*S_factor). Wherein, Current_Stock is the real-time inventory of the commodity. Only when the instantaneous request quantity in a concurrent window exceeds the safe capacity of the inventory, the system determines that there is a resource conflict, and all N_req orders are added to the conflict order list.

[0042] For example, the real-time inventory of a commodity is 100 pieces, and the safety margin coefficient is 0.9. In a 2-second CAW, the system monitors 120 exchange requests. Since 120>(100*0.9), the conflict condition is established. This detection method based on time window and safety margin can more accurately identify the real system bottleneck, avoid misjudgment caused by temporary request fluctuations, and improve the accuracy of detection and the stability of the system. Compared with the simple method of comparing the total number of requests with the total inventory in a period of time, the present application can more accurately capture the instantaneous traffic flood peak that causes the system bottleneck by introducing the concurrent access window (CAW), avoiding misjudgment caused by accumulating scattered requests in a long period of time. At the same time, the introduction of inventory safety margin (ISM) provides fault tolerance space for unexpected delays in the system processing flow or small lag of inventory data synchronization, improving the accuracy of the entire conflict detection mechanism and the robustness of the system.

[0043] It should be noted that after obtaining the conflict order list, the list contains the attributes of the conflict orders, such as commodity ID, request time and priority. The generation of the list is based on the judgment output of the module, for example, through a sorting algorithm to place orders with high conflict degree at the front end of the list.

[0044] S104, for the conflict order list, a preset weighted scoring model is adopted, combined with the historical exchange preference characteristics in the user behavior trajectory data, to adjust the order processing priority, and a resource allocation scheme is obtained.

[0045] The conflict order list is obtained, the historical exchange preference is extracted from the user behavior trajectory data, the priority weight value is calculated through behavior data fusion, and the adjusted priority sequence is obtained. For the adjusted priority sequence, a dynamic priority adjustment mechanism based on historical exchange preference is adopted, the exchange frequency statistics obtained from the historical exchange preference are fused, and the order processing sequence corresponding to the order processing priority is determined. Through the order processing sequence, combined with the allocation balance mechanism extracted from the order processing sequence, the resource allocation scheme is obtained.

[0046] As shown in the example, Figure 4 For the identified conflict order list, a dynamic priority scoring model (DPSM) is used to recalculate the priority of each order. The model is a linear weighted model, and the formula is as follows: Where P_score is the final priority score, w1 to w4 are weight coefficients that can be trained by historical data or configured by operation strategy, and Σw = 1.

[0047] Each feature factor is defined as follows: historical exchange preference factor (F_pref): quantifies the user's preference for the current conflict commodity. Its calculation method is: F_pref = (the number of times the user historically exchanges the product category of the commodity) / (the total number of historical exchanges of the user). For example, user A has exchanged 10 times in total, of which 3 times are transportation cards, so when he exchanges a commemorative subway card, his F_pref is 0.3.

[0048] User level factor (F_level): maps the user's membership level to a normalized numerical value. For example, the level is from V1 to V5, which is linearly mapped to 0.2 to 1.0.

[0049] Order waiting time factor (F_wait): in order to reflect the urgency, but avoid infinite growth, a logarithmic function is used for quantification: F_wait = log 10 (T_wait + 1), where T_wait is the waiting time in seconds since the order is submitted.

[0050] Commodity scarcity factor (F_scarce): reflects the intensity of current resource competition. Its calculation method is: F_scarce = (total number of requests within the concurrent access window Δt) / (current remaining inventory of the commodity).

[0051] Through the DPSM model, the system calculates an accurate and dynamic P_score for each conflicting order. For example, an order from a high-level user (F_level = 1.0) with a very high historical preference (F_pref = 0.8) may have a higher P_score than a low-level user who clicked accidentally, even if the order was submitted later, and thus has a higher priority in the resource allocation scheme.

[0052] By way of example, the weight coefficients w1-w4 in the dynamic priority scoring model (DPSM) can be determined through offline training. For example, a large amount of order data and their processing results (such as whether the user complained, whether to repurchase, etc.) in historical promotion activities can be collected as training samples, and the optimal weight combination can be iteratively solved by using machine learning algorithms such as gradient descent, with the optimization goal of maximizing user satisfaction or minimizing operating cost. During system operation, these weights can also be dynamically adjusted according to real-time operating strategies.

[0053] Preferably, to enhance the flexibility of the mechanism, a real-time feedback loop can be introduced, and if there is still a conflict after adjustment, the system will recalculate the priority based on the latest user trajectory data. For example, in one scenario, the user continues to browse related goods after submitting the order, and the system detects this behavior trajectory and dynamically increases the preference score to ensure the adaptability of the allocation scheme. In another possible implementation, the generation of the resource allocation scheme is to apply the adjusted priority to the actual allocation logic.

[0054] Specifically, for high-priority orders, the system directly allocates resources; for low-priority orders, it enters a waiting queue or provides alternative resource suggestions. In this way, the scheme not only solves the conflict, but also improves user satisfaction, for example, in a time-limited purchase activity, to ensure that loyal users have priority to obtain resources.

[0055] S105, integrate the order sequence through a queue reordering mechanism, rearrange the order based on the resource allocation scheme to obtain a final processing order, and execute the high-priority request first while maintaining queue stability.

[0056] Obtain the order sequence, extract the priority indicator from the resource scheme, and use the bubble sort algorithm to compare and exchange the sequence pair by pair to obtain a preliminary reordering sequence. For the preliminary reordering sequence, fuse the queue stability attribute extracted from the preliminary reordering sequence, and if the high-priority request exceeds a preset threshold, adjust the execution position of the high-priority request to determine an intermediate processing order. Through the intermediate processing order, combine the sequence integration attribute extracted from the intermediate processing order to rearrange the low-priority request to obtain a final processing order.

[0057] Exemplarily, in one embodiment, the order sequence is integrated by a queue reordering mechanism, which first needs to be understood. The mechanism is a priority-based order arrangement method used to handle order flow when resources are limited on an e-commerce points exchange platform.

[0058] Specifically, it takes the original order sequence as input and adjusts the sequence in combination with the previously obtained resource allocation scheme. The resource allocation scheme contains priority information of each order, such as a quantitative score based on user historical exchange preferences. The principle of the mechanism is to use a sorting algorithm, such as a stable sorting variant, to rearrange the orders without changing the relative positions of orders with the same priority, thereby maintaining queue stability. Stability here refers to ensuring that orders with the same priority remain in the original order after reordering, avoiding unnecessary reversal that causes processing delays. In the points exchange activity, this mechanism helps to efficiently allocate limited resources such as gift inventories. Further, the process of rearranging the order based on the resource allocation scheme includes several key steps. The system first extracts the priority values in the resource allocation scheme, for example, marking high-priority orders as needing front-end processing. Then, apply a queue reordering mechanism such as a stable version of insertion sort or merge sort to integrate the order sequence. For example, in an exchange scenario, if multiple user orders compete for the same batch of coupons, the system will move user orders with high historical exchange preferences to the front of the queue according to the adjusted priority in the scheme.

[0059] It should be noted that reordering is not a complete reconstruction, but an incremental adjustment, only the conflicting part is moved to reduce computational overhead and maintain the stability of the overall queue. This method achieves ordered allocation of resources in business, avoiding the unfairness caused by random processing.

[0060] Preferably, after obtaining the final processing order, the system executes the high-priority request first. In a specific implementation, the final processing order is an ordered list with high-priority orders at the front. When executing, the system starts processing from the head of the queue, for example, directly allocating resources such as electronic gift exchange codes to high-priority orders. At the same time, maintaining queue stability is achieved through additional constraints, such as using timestamps as secondary keys to ensure that orders with the same priority are arranged in order of submission time.

[0061] It should be noted that stability maintenance is achieved by avoiding circular rearrangement, i.e. triggering sorting only once when resources are available. This mechanism improves allocation efficiency in business and ensures that loyal users benefit first.

[0062] In one embodiment, when users redeem video membership benefits, the system arranges order sequences based on a scheme, prioritizes high-priority requests, and maintains queue stability through a stable sorting algorithm. The entire process, from integrating sequences to obtaining the final order, demonstrates the versatility of the mechanism and provides a reliable processing path when resources are scarce.

[0063] Specifically, maintaining queue stability involves using a priority queue structure combined with stable sorting. The priority queue allows efficient insertion of high-priority orders, while stable sorting ensures the preservation of the original order.

[0064] For example, in a time-limited activity on an integral exchange platform, the system only adjusts the sequence locally after detecting a new order, avoiding the system burden caused by global rearrangement. This implementation supports the smooth operation of the business and ensures that order processing is not interrupted due to frequent changes.

[0065] S106, if the final processing order has remaining conflicts, extract additional resources from the pre-device inventory through a backup resource set scheduling mechanism to obtain a complete execution plan that covers all order processing paths.

[0066] For the final processing order, obtain the remaining conflict positions, extract additional resources from the pre-device inventory, and determine the conflict resolution path by comparing the conflict positions and inventory resources. Through the conflict resolution path, use resource set management to allocate resources to the conflict positions and adjust the execution order to obtain an intermediate plan that is covered by the path. According to the intermediate plan, integrate the order processing path to schedule the backup resources, and if the remaining conflicts exceed the preset threshold, extract additional resources from the pre-device inventory again to obtain a complete execution plan.

[0067] As shown in the example, Figure 5 In one embodiment, if the final processing order has remaining conflicts, extract additional resources from the pre-device inventory through a backup resource set scheduling mechanism to obtain a complete execution plan. Remaining conflicts refer to the situation where some orders cannot be immediately allocated resources after initial reordering, such as high-priority orders being processed but low-priority orders being stuck due to insufficient inventory in an integral exchange activity. The backup resource set scheduling mechanism is a resource replenishment strategy used to dynamically manage backup inventory to ensure smooth order processing.

[0068] Specifically, the mechanism first detects the remaining conflicts by scanning the unassigned orders in the final processing order to identify resource gaps. Further, the principle of the spare resource set scheduling mechanism lies in maintaining a pre-device inventory pool containing additional prepared resources, such as spare coupons or virtual product inventory. These resources do not participate in the initial allocation but are used as a supplement. On the e-commerce points exchange platform, the mechanism uses a scheduling algorithm to match conflict orders with available resources, such as allocating based on order priority and resource type.

[0069] As shown in Figure 5 , the spare resource scheduling mechanism can configure multiple strategies. In addition to coupon compensation, it can also include: 1) similar product recommendation strategy, i.e. recommending similar function or value products from spare inventory; 2) points compensation strategy, i.e. returning part of the user's points and giving additional points as a bonus; 3) waiting queue strategy, for non-limited but temporarily out-of-stock products, the user can be prompted to enter the waiting queue and informed of the estimated restocking time.

[0070] For example, in a points exchange limited gift activity, if there are still user orders in the final processing order that conflict due to inventory depletion, the system activates the spare resource set scheduling mechanism. The process of extracting supplementary resources from the pre-device inventory includes several steps: first, the mechanism queries the matching resource type in the inventory pool, such as electronic gift codes; then, the required amount of replenishment is calculated, and the extraction scale is determined according to the number of conflict orders; finally, these resources are allocated to the corresponding order path. The complete execution plan is thus generated, which is a detailed processing path list covering all orders, including processed and high-priority parts, as well as the remaining parts solved by supplementary resources.

[0071] For example, the plan specifies that order A is obtained from the main resource and order B is supplemented from the spare inventory, ensuring that there is no omission.

[0072] Preferably, in another embodiment, in the scenario of member points exchange for virtual rights, the remaining conflict may be caused by multiple users simultaneously applying for the same video membership duration. The spare resource set scheduling mechanism extracts supplementary resources such as additional member activation codes from the pre-device inventory. The specific process is: the mechanism assesses the severity of the conflict and filters orders that need to be supplemented immediately through a priority threshold; then, resources are extracted from the inventory pool to generate an execution plan. The plan covers all order processing paths, such as allocating supplementary resources to medium-priority orders, while recording the path as "order C waits for execution after supplement". This way, comprehensive coverage of resources is achieved.

[0073] Specifically, the backup resource set scheduling mechanism uses matching rules to optimize the process when extracting supplementary resources. For example, the mechanism compares order requirements with inventory attributes to ensure compatibility, such as matching point values. After the complete execution plan is generated, the coverage path includes the main path and the backup path, and the system executes order processing in this order.

[0074] In one possible implementation, in a time-limited point exchange activity, the mechanism handles the remaining conflicts by extracting resources from pre-equipment inventory, and the plan details the resource source and execution timing for each order.

[0075] For example, in the scenario of exchanging book coupons with points on an e-commerce platform, if there are remaining conflicts in the final processing order, the backup resource set scheduling mechanism intervenes. The process of extracting supplementary resources from pre-equipment inventory emphasizes efficiency, for example, the mechanism monitors inventory levels in real time and transfers resources immediately after conflict detection. The resulting complete execution plan covers all order paths, for example, providing a backup coupon allocation path for low-priority orders to ensure fair processing.

[0076] It should be noted that this mechanism supports order management in the case of resource shortages. By extracting supplementary resources from pre-equipment inventory, the system generates a comprehensive execution plan that avoids long waiting times for some orders.

[0077] S107, based on the complete execution plan, monitor the order processing progress, track the progress and detect abnormalities, feedback real-time status, iteratively adjust, and obtain a stable running order flow.

[0078] Obtain order progress data from the execution plan, compare the order progress data with progress tracking, and determine if it exceeds the preset deviation to determine the abnormal event threshold, and obtain the abnormal detection result. According to the abnormal detection result, feedback real-time status, combine the abnormal detection result with iterative parameter adjustment, and correct the deviation through multiple cycles to obtain the optimized order flow parameters. Monitor the processing delay analysis through the optimized order flow parameters, obtain the delay index from the processing delay analysis, and determine a stable running order flow if the delay index is below the preset threshold.

[0079] For example, in one embodiment, based on the complete execution plan, first establish the overall framework of order processing. This execution plan includes various links from order receipt, inventory inspection, payment confirmation to logistics distribution, and defines the expected time and resource allocation for each step through system modules.

[0080] For example, in an e-commerce platform, the complete execution plan can preset the time limit for an order from placement to shipment as 24 hours, ensuring that subsequent monitoring has a benchmark reference. Further, monitoring order processing progress involves real-time data collection.

[0081] Specifically, the database system records the status changes of each order, such as order ID, current stage, and timestamp. Inventory update data is obtained from the warehouse management system through integrated sensors or API interfaces, so as to track whether the order is progressing as planned. This monitoring mechanism can cover various scenarios, such as when orders surge during peak periods, the system automatically adjusts the sampling frequency to maintain accuracy.

[0082] For example, for a retail order, the tracking module compares the actual progress with the execution plan, and if the payment confirmation link exceeds the preset 5 minutes, it is marked as a potential delay. This tracking is not limited to a single order, but can also aggregate and analyze batch orders to identify overall bottlenecks. Anomaly detection is a key step that requires detailed explanation of the process. Specifically, anomaly detection is based on a rule engine and threshold judgment. First, define anomaly indicators, such as a progress deviation rate exceeding 10% or a stall caused by insufficient inventory. The system compares the current state with the execution plan in real time.

[0083] For example, if the actual time of the logistics link exceeds the expected time by 20%, the detection logic is triggered: calculate the deviation value = (actual time - expected time) / expected time; if the deviation value is greater than the threshold, it is classified as an anomaly type, such as "inventory anomaly" or "payment anomaly". In the context of supply chain orders, for batch procurement orders, the detection process further analyzes historical data to identify patterns such as seasonal delays. In this way, anomaly detection ensures that problems are detected early to support subsequent feedback.

[0084] For example, when an anomaly occurs, immediately feedback "Order 12345 is delayed in the logistics stage, and is expected to affect the delivery time by 2 hours". This feedback supports mobile access for real-time response. Iterative adjustments are based on feedback results to optimize the cycle.

[0085] In one embodiment, the system collects multiple feedback data to form an adjustment strategy: first, analyze the root cause of the anomaly, such as determining through log review that it is a supplier delay; then, update the execution plan, for example, shorten the inventory check time or add a backup supplier. The iterative process can be repeated multiple times until the order flow is stable, for example, the deviation rate decreases from 15% to 5% after the initial adjustment. In the context of e-commerce promotions, for high-frequency orders, adjustments can introduce automated rules, such as dynamically allocating resources to balance the load. This iteration ensures that the order flow gradually transitions from an unstable state to an efficient operation. The system can also use online learning or periodic model retraining. For example, the system adjusts the weight coefficients w1-w4 in the dynamic priority scoring model (DPSM) based on the actual processing data of the previous day (such as link delays, user complaint rates, etc.) through the gradient descent algorithm, so that the scoring results are closer to the actual operation goals.

[0086] Embodiment Two For example,Figure 6 As shown, the application provides a big data-based integral mall order rolling processing optimization system, mainly comprising: a data acquisition and demand calculation module, which is used to acquire integral mall order inflow data and user behavior trajectory data, the data including browsing duration and exchange frequency, calculate demand change parameters, obtain peak period load level, and the level reflects the peak demand of system processing capacity; an order classification and queue management module, which is used to classify order queue based on the peak period load level using a priority sorting algorithm, obtain a classified order sequence, the sequence divides orders into high priority requests and regular requests, and the high priority requests involve high-frequency exchanged integral goods; a resource competition detection module, which is used to scan resource availability state through the resource competition detection module if the high priority request proportion in the order sequence exceeds a preset threshold, the module judges conflict points of insufficient inventory based on inventory availability and concurrent access, and obtains a conflict order list; a dynamic priority adjustment module, which is used to adjust order processing priority based on the user behavior trajectory data including historical exchange preferences using a dynamic priority adjustment mechanism for the conflict order list, and obtain a resource allocation scheme; a queue reordering module, which is used to integrate the order sequence through a queue reordering mechanism, rearrange the order based on the resource allocation scheme, obtain a final processing order, preferentially execute the high priority request, and maintain queue stability; a standby resource scheduling module, which is used to extract supplementary resources from a pre-device inventory through a standby resource set scheduling mechanism if there are remaining conflicts in the final processing order, obtain a complete execution plan, and the plan covers all order processing paths; a progress monitoring and iterative adjustment module, which is used to monitor order processing progress based on the complete execution plan, feed back real-time state through progress tracking and abnormality detection, and obtain a stably running order flow through iterative adjustment.

[0087] Embodiment three This embodiment combines the specific scene of the integral mall large-scale promotion activity "annual grand ceremony" to explain the application process and technical advantages of the application in detail.

[0088] During the "annual grand ceremony", the integral mall of a certain citizen card APP launched two best-selling products: one is a limited edition "city memorial subway card" (physical product, high value, complex processing link) with only 500 pieces in stock, and the other is a "5-yuan call charge coupon" (virtual product, low value, instant processing). The activity started on time at 8 pm.

[0089] The flow of the method of the application is as follows: (1) S101: load prediction and demand calculation based on the DVP model. One week before the activity, the system has started collecting user behavior trajectory data. It is found that a large number of users repeatedly browse the "City Memorial Subway Card" page, with an average browsing time of 120 seconds, far exceeding the 15 seconds of ordinary goods. At the same time, the historical exchange frequency of these users is generally high, and they are marked by the system as high-value exchange events. One hour before the start of the activity, the system applies the demand variation parameter (DVP) model based on the data of the last one hour: Bi (effective browsing time), Ej (high-value exchange identifier), etc. are collected. Set the time decay coefficient λ = 0.2, the business weight α = 0.6, β = 0.4. After calculation, DVP = 4.0 is obtained, indicating that the potential exchange demand is extremely strong.

[0090] Subsequently, the system applies the peak load level (PLL) model for prediction: the system baseline processing capacity C_base = 1000 single / second; the preset load coefficient K_load = 1.0; the PLL = C_base * (1 + DVP * K_load) = 1000 * (1 + 4.0 * 1.0) = 5000 single / second is calculated.

[0091] The prediction result shows that the concurrent request at the start of the activity will reach 5 times the normal peak value, and the system will face an extremely high load. Based on this, the system generates a resource pre-allocation scheme in advance, dynamically expanding the number of server instances handling orders to a predetermined level.

[0092] (2) S102 & S103: order classification and resource conflict detection based on CAW and ISM. At 8:00 p.m., 5000 orders suddenly poured in.

[0093] S102 Order classification: based on the priority sorting algorithm, of which 800 orders for "City Memorial Subway Card" are identified as "high-frequency exchange" goods in the preheating period and are preliminarily marked as "high-priority requests"; the remaining 4200 phone card orders, etc. are marked as "regular requests".

[0094] S103 Resource conflict detection: the proportion of high-priority requests (800) is 16% (800 / 5000), which exceeds the system's preset resource tension threshold of 10%, and the resource competition detection module is triggered.

[0095] The resource competition detection module defines the concurrent access window (CAW) Δt = 2 seconds, and introduces the inventory safety margin (ISM) S_factor = 0.9.

[0096] At the first 2 seconds CAW after the activity starts, the resource competition detection module counts the number of requests for "commemorative metro card" N_req = 800.

[0097] The resource competition detection module applies the conflict triggering condition for judgment: N_req>(Current_Stock*S_factor). Substituting the numerical value: 800>(500*0.9), that is, 800>450.

[0098] The conflict condition is established. The system determines that there is a serious inventory conflict, and generates a "conflict order list" containing the 800 orders.

[0099] (3) S104: Dynamic priority adjustment based on DPSM model. The system starts the dynamic priority scoring model (DPSM) for the 800 conflict orders for accurate reordering. The model weight is trained by historical data, and is set as: w1 (preference)=0.4, w2 (level)=0.3, w3 (wait)=0.1, w4 (scarce)=0.2. Take two typical users as examples: User A: V5 member, historical conversion preference of traffic card type goods is very high.

[0100] User B: V2 member, first tries to exchange this type of goods.

[0101] The system calculates the feature factors for the orders of the two users: Historical conversion preference factor (F_pref): User A: F_pref=0.85 (historically converted 5 traffic cards, accounting for 85% of total conversion times); User B: F_pref=0.05 (historical preference of this type of goods accounts for 5%); User level factor (F_level): User A (V5): F_level=1.0; User B (V2): F_level=0.4; Order waiting time factor (F_wait): It is assumed that the request of user B arrives first at 1 second.

[0102] User A (wait 2 seconds): F_wait=log 10 (2+1)≈0.48; User B (wait 3 seconds): F_wait=log 10 (3+1)≈0.60; Good scarcity factor (F_scarce): The value is the same for both orders: F_scarce = 800 / 500 = 1.6; Final priority score P_score calculation: P_score_A = 0.40.85 + 0.31.0 + 0.10.48 + 0.21.6 = 0.34 + 0.3 + 0.048 +0.32 =1.008; P_score_B = 0.40.05 + 0.30.4 + 0.10.60 + 0.21.6 = 0.02 + 0.12 + 0.06 +0.32 =0.520; The result shows that, although user B's order submission is slightly earlier, user A with high loyalty and high preference gets a significantly higher priority score. The system generates a precise priority sequence reflecting users' real redemption intention for all 800 conflict orders, and forms a resource allocation scheme.

[0103] (4) S105 & S106: Stable queue reordering and backup resource scheduling; S105 Queue Reordering: The queue reordering module uses a stable sorting algorithm (such as merge sort) to integrate the entire order queue according to the new priority score calculated by DPSM. The final processing order is determined as follows: the top 500 "commemorative metro card" orders. 4200 "phone card" orders (as they are regular requests, they are placed at the end, and internally they can be sorted by submission time). The remaining 300 "commemorative metro card" orders. This ordering ensures that high-value, high-intention users' requests are prioritized, while not affecting virtual orders that can be processed quickly.

[0104] S106 Backup Resource Scheduling: For the 300 orders that are sorted at the end and do not get "commemorative metro cards", the system automatically triggers the backup resource set scheduling mechanism. From the pre-device inventory, it automatically matches and pushes a "9% off metro card exchange coupon" to these users as compensation. This forms a complete execution plan covering all paths, ensuring that no user's request is directly rejected.

[0105] (5) S107: Monitoring and closed-loop iteration; the system monitors the order processing progress in real time. It finds that due to the temporary delay of the logistics system interface, the average outbound time of physical card orders is 15 seconds slower than planned.

[0106] Abnormality detection: the system marks this event as a "moderate delay anomaly".

[0107] Feedback and iteration: the abnormal data is fed back to the iterative adjustment module. The system fine-tunes the weights in the DPSM model through online learning algorithms (such as gradient descent), slightly reducing the weight wi of physical goods that require complex logistics in subsequent scoring. At the same time, the load prediction model PLL learns that the actual processing capacity of this period is slightly lower than expected, providing more accurate predictions for the next similar activity. This closed-loop iteration enables the system to adaptively optimize and maintain high efficiency and stability in the long term.

[0108] Compared with the traditional first-come-first-served (FIFO) mechanism, the improvement of the present application is that under the FIFO mechanism, the first 500 users who click to redeem (which may be script or accidental click users) will get the commemorative card, and a large number of real users with high loyalty and high willingness may miss it due to network delay of zero point several seconds, resulting in poor user experience; while the present application accurately allocates limited resources to the user group most likely to generate high value and high satisfaction through intelligent prediction and dynamic adjustment, and pacifies other users through a standby resource mechanism, achieving improvement in system processing efficiency, resource allocation fairness and user satisfaction.

[0109] Figure 7 The figure compares the effects of the method of the present application and the prior art in processing scarce goods during peak hours. The figure contains three subgraphs, which intuitively show the superior technical effects of the present application from three dimensions of order processing time, resource utilization and system stability.

[0110] The upper left subgraph is an order processing time comparison graph. The horizontal axis of the column chart represents different system load levels, and the vertical axis represents the average processing time of orders (in seconds). As can be seen from the graph, at low load, the processing time of the present application method is similar to that of the traditional FIFO method; but as the load level increases, the processing time of the traditional FIFO method grows exponentially, reaching 8.5 seconds at 1.0 times the load and as high as 25.0 seconds at 2.0 times the load. In contrast, the processing time of the present application method grows very slowly and still maintains at a level of 12.8 seconds at 2.0 times the load. This proves that the present application can effectively avoid processing bottlenecks and shorten user waiting time in high-concurrency scenarios through intelligent scheduling and sorting.

[0111] The upper right subgraph is a resource utilization comparison graph. The horizontal axis of the column chart also represents the system load level, and the vertical axis represents the resource utilization (%). It can be seen that at each load level, the resource utilization of the present application method is significantly higher than that of the traditional FIFO method. Especially in the 1.0 to 2.0 times load interval, the resource utilization of the present application method can maintain at a high level of 75%-82%, while the traditional FIFO method has a utilization rate of 45% due to processing blockage and resource mismatch. This shows that the present application can more efficiently utilize server, inventory and other system resources.

[0112] The bottom right subgraph is a user satisfaction comparison graph. The bar chart compares the satisfaction of different user groups in the peak period when the application method and the prior art process scarce commodities. The user satisfaction of the application method remains at a high level under different system load conditions, and the user satisfaction is improved from 68% to 85% under 1.0 times the peak load. It can be seen that the application method significantly improves the satisfaction of high loyalty users and the overall user.

[0113] The bottom right subgraph is a system stability index graph. The horizontal axis of the line chart represents the change from normal load to super-peak load (5000 orders / second), and the vertical axis represents the system failure rate (%). Three areas are defined in the graph: stable operation area (<10%), warning area (10-20%) and dangerous area (>20%). The failure rate curve of the traditional FIFO method rises sharply with the increase of load, and enters the warning area at the peak load, and as high as 35% at the super-peak load, entering the dangerous area, and the system is on the verge of collapse. The failure rate curve of the application method is very flat, and the failure rate can be controlled at 18% even under super-peak load, always in the stable operation area. The application improves the stability and robustness of the system through forward-looking load prediction and dynamic resource conflict management.

[0114] In summary, compared with the prior art, the application has the following beneficial effects: 1. The application not only processes orders after they occur, but also realizes quantitative prediction of future system load by constructing a demand change parameter model based on user deep behavior. The user behavior such as browsing is associated with the back-end processing capacity, and combined with the dynamic trigger condition of high-priority request proportion exceeding threshold, a closed-loop mechanism from demand prediction to conflict warning is constructed. This enables the system to change from passive acceptance of requests to active risk management, effectively preventing system collapse or transaction failure caused by resource squeeze, which cannot be achieved by the simple order sorting or inventory checking of the prior art.

[0115] 2. In the face of resource conflicts, the application does not use a simple first-come-first-served or fixed membership level priority strategy, but uses a multi-dimensional dynamic priority scoring model to dynamically integrate and quantify multiple dimensions such as user historical preferences, real-time willingness (waiting time), identity value and resource competition intensity. This enables limited scarce resources to be accurately allocated to requests that best reflect business value and user loyalty, achieving a shift from opportunity fairness to value fairness, greatly improving user satisfaction and operational efficiency.

[0116] 3.The application not only solves the identified conflicts, but also provides a compensation path for the requests that cannot be met through the backup resource set scheduling mechanism, ensuring the integrity of the business process. More importantly, through the progress monitoring and iterative adjustment module, the system takes each processing result (such as delay, exception) as feedback to continuously optimize the load prediction model of the front end and the priority scoring weight of the middle end. This whole-process closed-loop adaptive capability enables the system to continuously learn and evolve, maintaining high efficiency and stability in various complex scenarios in the long term. The above is only an embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process transformation using the content of the application specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the application.

Claims

1. A method for optimizing the rolling processing of orders in a points mall based on big data, characterized in that, The method includes: By collecting order inflow data and user behavior trajectory data from the points mall, demand change parameters are calculated. Combined with historical load data and a preset load coefficient, peak load levels are determined, reflecting the peak demand for system processing capacity. Based on these peak load levels, a priority sorting algorithm is used to classify order queues, resulting in order sequences divided into high-priority requests and regular requests. When the proportion of high-priority requests in the order sequence exceeds a preset threshold, conflict points due to insufficient inventory are identified by comparing the number of requests within the concurrent access window with the real-time inventory level multiplied by the inventory safety margin coefficient, thus obtaining a list of conflicting orders. For these conflicting orders... A single list is used, employing a preset weighted scoring model and incorporating historical redemption preference features from user behavior trajectory data to adjust order processing priorities and obtain a resource allocation scheme. The order sequence is then integrated through a queue reordering mechanism, and the order is rearranged based on the resource allocation scheme to obtain the final processing order. If there are remaining conflicts in the final processing order, a backup resource set scheduling mechanism is used to extract supplementary resources from a preset backup inventory, resulting in a complete execution plan covering all order processing paths. Based on this complete execution plan, order processing progress is monitored, and real-time status is fed back through progress tracking and anomaly detection. Iterative adjustments are made to obtain a stable order flow.

2. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... The process involves collecting order inflow data from the points mall and user behavior trajectory data, calculating demand change parameters, and combining historical load data and preset load coefficients to determine peak-period load levels, including: By collecting order inflow data and user behavior trajectory data from the points mall, including browsing time records and redemption frequency statistics, the average duration value is extracted from the browsing time records and combined with the frequency count in the redemption frequency statistics to calculate the demand change parameter. The parameter is obtained by dividing the average duration value by the frequency count. Based on the demand change parameters, the peak load level is determined by comparing the parameters with historical load data. The level is calculated by multiplying the parameters by a preset load coefficient and is used to reflect the peak demand of the system's processing capacity. For the peak load level, if the demand change parameter exceeds a preset threshold, the order volume statistics in the order inflow data are combined with the load level to obtain a resource pre-allocation scheme. The scheme optimizes the response to peak demand by weighting the load level with the order volume statistics.

3. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 2, is characterized in that... The calculation of the demand change parameter includes: based on the time decay weight, performing weighted statistics on the effective browsing time and high-value redemption events within a preset time window to obtain a quantitative value reflecting the user's attention investment.

4. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... Based on the peak load level, a priority sorting algorithm is used to classify the order queue, resulting in a classified order sequence. This sequence divides orders into high-priority requests and regular requests. The high-priority requests involve frequently redeemed points-based items, including: Based on the load level assessment, high-frequency redemption identification features are extracted from the order queue using behavioral trajectory fusion. The extraction is obtained by statistically counting redemption frequencies and combining user request hierarchies to obtain a preliminary priority list. The list marks frequently redeemed points items as high-priority requests. For the preliminary priority list, peak demand response data is obtained and combined with the priority sorting mechanism to perform layered processing on the order queue. The peak demand response data is derived from the load level assessment to determine the classified order sequence, which distinguishes between high-priority requests and regular requests. From the categorized order sequence, if the proportion of high-priority requests exceeds a preset threshold, the division of points products is adjusted through resource priority allocation. The adjustment adopts sequence generation logic to fuse behavioral trajectories and obtains an optimized sequence response scheme.

5. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... When the proportion of high-priority requests in the order sequence exceeds a preset threshold, the conflict points of insufficient inventory are identified by comparing the number of requests within the concurrent access window with the real-time inventory quantity multiplied by the inventory safety margin coefficient, and a list of conflicting orders is obtained, including: When the proportion of high-priority requests exceeds a preset threshold, a Concurrent Access Window (CAW) is defined with a time length of Δt. The number of all redemption requests for the same high-priority product within any CAW is counted in real time. An Inventory Safety Margin (ISM) is defined, with a value of a coefficient S_factor less than 1, representing the proportion of inventory reserved to cope with emergencies. The trigger condition for a conflict is defined as follows: a resource conflict is determined to exist only when the instantaneous number of requests within a concurrent window exceeds the inventory safety capacity, and all orders with the corresponding requests are added to the conflict order list.

6. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... The weighted scoring model also incorporates at least one of the following features: user level, order waiting time, or product scarcity.

7. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... The order sequence is integrated through a queue reordering mechanism, and the order is rearranged based on the resource allocation scheme to obtain the final processing order, including: Obtain the order sequence, extract priority indicators from the resource plan, and use the bubble sort algorithm to compare, exchange, and integrate the sequence pairwise to obtain a preliminary reordered sequence. For the initial reordering sequence, the queue stability attributes extracted from the initial reordering sequence are integrated to determine if high-priority requests exceed a preset threshold. In this case, the execution position of the high-priority requests is adjusted to determine the intermediate processing order. By combining the intermediate processing order with the sequence integration attributes extracted from the intermediate processing order, low-priority requests are rearranged to obtain the final processing order.

8. The method for optimizing the rolling processing of orders in a points mall based on big data, as described in claim 1, is characterized in that... If there are remaining conflicts in the final processing order, supplementary resources are extracted from the preset backup inventory through the backup resource set scheduling mechanism to obtain a complete execution plan covering all order processing paths, including: For the final processing order, the remaining conflict locations are obtained, supplementary resources are extracted from the preset reserve inventory, and the conflict resolution path is determined by comparing the matching degree between the conflict locations and the inventory resources. Using the conflict resolution path, resource aggregation management is employed to allocate resources to conflict locations and adjust the execution order, resulting in an intermediate plan under path coverage assurance. Based on the intermediate plan, the order processing path is integrated to schedule backup resources. If the remaining conflicts exceed a preset threshold, resources are extracted again from the preset backup inventory to obtain a complete execution plan.

9. A big data-based points mall order rolling processing optimization system, characterized in that, The system includes: a data acquisition and demand calculation module, used to collect order inflow data from the points mall and user behavior trajectory data, calculate demand change parameters, and determine the peak load level by combining historical load data and a preset load coefficient, the level reflecting the peak demand of the system's processing capacity; an order classification and queue management module, used to classify order queues based on the peak load level using a priority sorting algorithm, obtaining an order sequence that divides orders into high-priority requests and regular requests; a resource contention detection module, used to identify conflict points of insufficient inventory when the proportion of high-priority requests in the order sequence exceeds a preset threshold, by comparing the number of requests within the concurrent access window with the real-time inventory quantity multiplied by the inventory safety margin coefficient, and obtaining a list of conflicting orders; and dynamic priority adjustment. The system comprises the following modules: a module for adjusting order processing priorities based on a pre-defined weighted scoring model and historical redemption preference features in user behavior trajectory data, resulting in a resource allocation scheme; a queue reordering module for integrating the order sequence through a queue reordering mechanism and rearranging the order based on the resource allocation scheme, resulting in a final processing order; a backup resource scheduling module for extracting supplementary resources from a pre-defined backup inventory through a backup resource set scheduling mechanism if there are remaining conflicts in the final processing order, resulting in a complete execution plan covering all order processing paths; and a progress monitoring and iterative adjustment module for monitoring order processing progress based on the complete execution plan, providing real-time status feedback through progress tracking and anomaly detection, and iteratively adjusting to obtain a stable order flow.

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