Efficient and reliable order flood storage system

Abstract

The invention relates to a high-efficiency and reliable order flood storage system, which comprises the following parts: a dynamic capacity expansion and contraction mechanism for enabling the system to carry out elastic expansion and contraction of resources in a short time according to the actual condition of order flow by utilizing a dynamic resource allocation function of cloud service; the order data buffering queue is used for receiving all entered orders in a short time in a peak period and storing the orders in the memory database; a multi-level order processing engine which creates a multi-level processing engine to ensure that each order can be processed in time; the order intelligent distribution module uses a machine learning algorithm to intelligently classify and distribute orders, and preferentially processes the orders having more important meanings for business; a pressure test and load balancing module; and an after-sales service processing module. According to the invention, the stability and reliability of the order processing system in the face of high-flow impact can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of order processing, and particularly relates to an efficient and reliable order flood storage system. Background Art

[0002] In the fields of e-commerce, logistics, and supply chain management, the order processing ability is often a key factor for an enterprise to maintain operations under high pressure and complex environments. Especially in scenarios such as promotional seasons or when demand suddenly surges in a short period, how to process a large number of orders without sacrificing the customer experience has become a major challenge in the industry. Traditional order processing systems are often designed to handle a fixed order flow, and are prone to system crashes when faced with a sudden surge in the order volume. Therefore, developing an "order flood storage system" that can adapt to peak order flows is the key to solving existing problems. Summary of the Invention

[0003] (1) Object of the Invention

[0004] In order to overcome the above deficiencies, the object of the present invention is to provide an efficient and reliable order flood storage system to solve the above technical problems.

[0005] (2) Technical Solution

[0006] To achieve the above object, the technical solution provided by the present application is as follows:

[0007] An efficient and reliable order flood storage system, including the following parts:

[0008] A dynamic capacity scaling mechanism, which utilizes the dynamic resource allocation function of cloud services to enable the system to elastically scale resources according to the actual situation of order flow in a short time;

[0009] An order data buffer queue, equipped with an efficient data queue, for receiving all incoming orders in a short time during peak periods and storing them in an in-memory database;

[0010] A multi-level order processing engine, which creates a multi-level processing engine to ensure that each order can be processed in a timely manner, and low-priority orders can be stored and waited, while high-priority orders can be quickly responded to;

[0011] An order intelligent distribution module, which uses machine learning algorithms to intelligently classify and distribute orders, and preferentially processes orders that are more important to the business;

[0012] A stress test and load balancing module, which regularly conducts stress tests on the system to ensure high availability of the system and reasonably distributes the load among multiple servers;

[0013] After-sales service processing module: After the peak period, it can reprocess order data and simultaneously handle customer service requests that may not have been processed immediately during the high-traffic period.

[0014] Preferably, the dynamic capacity scaling mechanism includes the following steps:

[0015] A1 Monitor the order flow. Through the traffic monitoring system, achieve real-time monitoring of the order inflow and capture the subtle changes in the order flow.

[0016] A2 Traffic evaluation. Based on the preset traffic threshold, the system automatically evaluates the current traffic level, determines whether it is in a normal, peak, or abnormal state, and the evaluation result will trigger the corresponding warning mechanism to provide immediate traffic status feedback to the decision maker for quick response.

[0017] A3 Calculate the resource requirements. Combining the real-time traffic data and historical order processing records, the system uses algorithms to predict the additional resource amounts required at different traffic levels.

[0018] A4 Resource allocation. By docking with the interface of the cloud service provider, the system can automatically apply for additional computing resources, and the computing resources include CPU, memory, and storage space. The allocation process follows the cost-benefit principle and gives priority to the resource expansion plan with lower costs to achieve the optimal allocation of resources.

[0019] A5 Resource distribution. The newly allocated resources will be quickly integrated into the order processing process to enhance the processing capacity of the system. The system uses intelligent scheduling algorithms to ensure that resources are allocated to the most needed links to maximize resource utilization.

[0020] A6 Performance monitoring. The system continuously monitors the performance of the newly added resources to ensure that it can effectively improve the order processing speed and system stability.

[0021] A7 Resource feedback adjustment. According to the data collected by the performance monitoring, the system automatically adjusts the resource allocation strategy to adapt to the changing order flow. The adjustment strategies include increasing or decreasing resources, reallocating, and optimizing the configuration to ensure that the system always operates in the best state.

[0022] Preferably, the order data buffer queue specifically includes the following steps:

[0023] B1 Queue initialization. Initialize the buffer queue in the memory and set the queue size and timeout mechanism.

[0024] B2 Receive orders. During the high-traffic period, all incoming orders are first written into the buffer queue.

[0025] B3 Queue storage. The order data stored in the queue needs to ensure data integrity and consistency.

[0026] B4 Data persistence: To prevent system crashes, the data in the queue is periodically persisted to the database.

[0027] B5 Order processing pulling: When the system traffic drops, orders are pulled from the buffer queue and processed.

[0028] B6 Pulling monitoring: Monitor the order processing progress and ensure that the processing speed matches the current system load.

[0029] B7 Queue maintenance: Maintain the running status of the queue and clean up expired or duplicate order data.

[0030] Preferably, the multi-level order processing engine specifically includes the following steps:

[0031] C1 Define priority rules: Determine the priority rules for different orders.

[0032] C2 Order classification: Classify orders according to the priority rules into three levels: high, medium, and low.

[0033] C3 Allocate processing queues: Allocate orders to the corresponding processing queues according to the order priority.

[0034] C4 System preferential processing: Preferentially process the orders in the high-priority order queue.

[0035] C5 Level conversion and processing: When there is remaining processing capacity, gradually process the orders in the medium- and low-priority queues.

[0036] C6 Status feedback: Real-time feedback the order processing status to ensure that users can understand the order progress.

[0037] C7 Abnormal order processing: Transfer the abnormal orders that occur during the processing to the abnormal processing process.

[0038] Preferably, the order intelligent distribution module specifically includes the following steps:

[0039] D1 Feature analysis: Extract the features in the order data.

[0040] D2 Model training and evaluation: Train a machine learning model to identify order priorities and distribution rules.

[0041] D3 Intelligent distribution: Use the trained model to intelligently allocate and distribute orders.

[0042] D4 System performance feedback: Collect the actual impact data of intelligent distribution on system performance.

[0043] D5 Model adjustment: Adjust and optimize the distribution model according to the feedback to improve the distribution accuracy.

[0044] D6 Replacement processing strategy: When the results provided by the model do not meet expectations, manual intervention is carried out and the processing strategy is replaced;

[0045] D7 Recording and monitoring: Record the results of intelligent distribution and continuously monitor the overall performance of the system.

[0046] Preferably, the stress testing and load balancing module specifically includes the following steps:

[0047] E1 Defining test scenarios: Define different stress test scenarios according to historical order peaks and expected business expansion;

[0048] E2 Implementing stress testing: Simulate a large number of orders flowing in through an automated tool to test the maximum load capacity of the system;

[0049] E3 Performance data analysis: Collect and analyze the performance data during stress testing;

[0050] E4 Load adjustment plan: Formulate or adjust the load balancing and resource allocation plan according to the test results;

[0051] E5 Configuring load balancing: Configure load balancing at the hardware and system levels to ensure that requests can be evenly distributed to each processing unit;

[0052] E6 Real-time load monitoring: Real-time monitor the system load situation and automatically adjust the load distribution strategy;

[0053] E7 Feedback optimization: Continuously optimize the load distribution and system resource configuration according to the monitoring data.

[0054] Preferably, the after-sales service processing module specifically includes the following steps:

[0055] F1 Identifying unprocessed requests: After the end of the order peak period, identify and sort out the unprocessed orders and customer requests;

[0056] F2 Classification and filing: Classify and file the unprocessed requests according to service types;

[0057] F3 Service level assessment: Evaluate the service level of users during the peak period and identify service gaps;

[0058] F4 Request priority ranking: Set priorities for unprocessed requests according to the service level assessment;

[0059] F5 Processing requests: Process these requests in an orderly manner according to the set priorities;

[0060] F6 Processing status feedback: Real-time feedback the processing progress to users to enhance user trust and satisfaction;

[0061] F7 Service effect evaluation. After the processing is completed, collect users' evaluations of the service effect;

[0062] F8 Follow-up optimization. Continuously optimize the service process based on the feedback to improve the user experience during future peak periods.

[0063] Preferably, the intelligent scheduling algorithm in step A5 specifically includes the following steps:

[0064] A51 Data collection and preprocessing. Collect real-time order flow data, including order arrival rate, order type, and estimated processing time, and extract order processing data for similar time periods from the historical database for prediction and comparison;

[0065] A52 Flow prediction. Use time series analysis methods to predict the order flow within a future period of time;

[0066] Ft = α·F t-1 +(1 - α)·(O t +β·Tt)

[0067] where F t is the predicted order flow, F t-1 is the actual flow of the previous period, O t is the current order volume, T t is the time trend factor, and α and β are model parameters;

[0068] A53 Resource requirement calculation

[0069] Calculate the required amount of resources according to the predicted order flow and the processing requirements of each order;

[0070]

[0071] where Rt is the required amount of resources, P is the average processing requirement of each order, and E is the processing capacity of each resource unit;

[0072] A54 Resource allocation. Through the cloud service interface, apply for additional computing resources according to the calculated resource requirements, and select the most suitable resource expansion plan using cost-benefit analysis;

[0073]

[0074] where C is the cost of the selected resource plan, C i is the cost of the i-th resource plan, and P i is the performance of the i-th resource plan;

[0075] A55 Resource distribution. Allocate the newly allocated resources to different processing units through the intelligent scheduling algorithm;

[0076]

[0077] Among them, A i is the amount of resources allocated to the i-th processing unit, W i is the weight of the ith processing unit, R t is the total resource requirement;

[0078] A56 performance monitoring and feedback adjustment: real-time monitoring of the performance of each processing unit, including processing speed and error rate, and dynamic adjustment of resource allocation strategies based on performance data;

[0079] A' i =A i +ΔA i

[0080] Among them, Ai′ is the adjusted resource allocation, and ΔAi is the amount of resources adjusted according to performance feedback.

[0081] Beneficial effects:

[0082] The present invention can effectively improve the stability and reliability of the order processing system when facing high traffic impact. Through effective order buffering and intelligent distribution, the user experience can be guaranteed even during peak hours, avoiding system crashes or long periods of unresponsiveness. In addition, scientific order distribution with the help of advanced technologies such as machine learning can not only improve processing capabilities, but also reduce operating costs, with significant economic benefits.

[0083] In summary, the order flood storage system of this solution demonstrates high efficiency and reliability under high traffic conditions, and can effectively manage sudden order traffic, making it an ideal choice for e-commerce and logistics service providers. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 It is the overall flow chart of the present invention;

[0085] Figure 2 is a flow chart of a dynamic capacity scaling mechanism according to an embodiment of the present invention;

[0086] Figure 3 is a flow chart of an order data buffer queue according to an embodiment of the present invention;

[0087] Figure 4 is a flow chart of a multi-level order processing engine according to one embodiment of the present invention;

[0088] Figure 5 is a flow chart of an order intelligent distribution module according to an embodiment of the present invention;

[0089] Figure 6It is a flowchart of the pressure test and load balancing module according to an embodiment of the present invention. Specific embodiments

[0091] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the attached Figure 1-6 , to further illustrate the present invention. It should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present invention.

[0092] An efficient and reliable order flood storage system includes the following parts:

[0093] Dynamic capacity scaling mechanism, which utilizes the dynamic resource allocation function of cloud services to enable the system to elastically scale resources according to the actual situation of order traffic in a short period of time;

[0094] Order data buffer queue, which is equipped with an efficient data queue for receiving all incoming orders in a short period of time during peak periods and storing them in an in-memory database;

[0095] Multi-level order processing engine, which creates a multi-level processing engine to ensure that each order can be processed in a timely manner. Low-priority orders can be stored and waited, while high-priority orders can receive a quick response;

[0096] Order intelligent distribution module, which uses machine learning algorithms to intelligently classify and distribute orders, and preferentially processes orders that are more important to the business;

[0097] Pressure test and load balancing module, which regularly conducts pressure tests on the system to ensure high availability of the system and reasonably distributes the load among multiple servers;

[0098] After-sales service processing module: After the peak period, it can reprocess order data and at the same time process customer service requests that may not have been processed immediately during the high-traffic period.

[0099] Preferably, the dynamic capacity scaling mechanism includes the following steps:

[0100] A1 Monitor order traffic, and through the traffic monitoring system, realize real-time monitoring of order inflows and capture subtle changes in order traffic;

[0101] A2 Traffic evaluation, based on a preset traffic threshold, the system automatically evaluates the current traffic level, determines whether it is in a normal, peak or abnormal state, and the evaluation result will trigger the corresponding warning mechanism to provide instant traffic status feedback to decision-makers for quick response;

[0102] A3 Resource Requirement Calculation. By combining real-time traffic data and historical order processing records, the system uses algorithms to predict the additional resource amounts required at different traffic levels;

[0103] A4 Resource Allocation. Through the interface docking with cloud service providers, the system can automatically apply for additional computing resources, which include CPU, memory, and storage space. The allocation process follows the cost-benefit principle, giving priority to resource expansion plans with lower costs to achieve the optimal allocation of resources.

[0104] A5 Resource Distribution. The newly allocated resources will be quickly integrated into the order processing flow to enhance the system's processing capacity. The system uses intelligent scheduling algorithms to ensure that resources are allocated to the most needed links, maximizing resource utilization;

[0105] A6 Performance Monitoring. The system continuously monitors the performance of the newly added resources to ensure that it can effectively improve the order processing speed and system stability.

[0106] A7 Resource Feedback and Adjustment. Based on the data collected by performance monitoring, the system automatically adjusts the resource allocation strategy to adapt to the changing order traffic. The adjustment strategies include increasing or decreasing resources, reallocating, and optimizing the configuration to ensure that the system always operates at the best state.

[0107] Preferably, the order data buffer queue specifically includes the following steps:

[0108] B1 Queue Initialization. Initialize the buffer queue in memory, and set the queue size and timeout mechanism;

[0109] B2 Receive Orders. During high-traffic periods, all incoming orders are first written to the buffer queue;

[0110] B3 Queue Storage. The order data stored in the queue needs to ensure data integrity and consistency;

[0111] B4 Data Persistence. To prevent system crashes, periodically persist the data in the queue to the database;

[0112] B5 Order Processing Pull. When the system traffic drops, pull and process orders from the buffer queue;

[0113] B6 Pull Monitoring. Monitor the order processing progress and ensure that the processing speed matches the current system load;

[0114] B7 Queue Maintenance. Maintain the queue running status and clean up expired or duplicate order data.

[0115] Preferably, the multi-level order processing engine specifically includes the following steps:

[0116] C1 Define the priority rules to determine the priority rules for different orders;

[0117] C2 Order classification, classify orders according to the priority rules into three levels: high, medium, and low;

[0118] C3 Allocate the processing queue, and allocate orders to the corresponding processing queues according to the priority of the orders;

[0119] C4 System preferential processing, preferentially process the orders in the high-priority order queue;

[0120] C5 Level conversion and processing, when there is remaining processing capacity, gradually process the orders in the medium- and low-priority queues;

[0121] C6 Status feedback, real-time feedback on the order processing status to ensure that users can understand the order progress;

[0122] C7 Exception order processing, transfer the exception orders that occur during the processing to the exception handling process.

[0123] Preferably, the order intelligent distribution module specifically includes the following steps:

[0124] D1 Feature analysis, extract the features in the order data;

[0125] D2 Model training and evaluation, train a machine learning model to identify order priorities and distribution rules;

[0126] D3 Intelligent distribution, use the trained model to perform intelligent allocation and distribution of orders;

[0127] D4 System performance feedback, collect the actual impact data of intelligent distribution on system performance;

[0128] D5 Model adjustment, adjust and optimize the distribution model according to the feedback to improve the accuracy of distribution;

[0129] D6 Replace the processing strategy, when the result provided by the model does not meet the expectation, manually intervene and replace the processing strategy;

[0130] D7 Record and monitor, record the results of intelligent distribution, and continuously monitor the overall performance of the system.

[0131] Preferably, the stress testing and load balancing module specifically includes the following steps:

[0132] E1 Define the test scenarios, define different stress test scenarios according to the historical order peaks and expected business expansion;

[0133] E2 Implement stress testing, simulate a large number of orders flowing in through automated tools to test the maximum load capacity of the system;

[0134] E3 Performance data analysis, collect and analyze performance data during stress testing;

[0135] E4 Load adjustment plan, formulate or adjust load balancing and resource allocation plans according to test results;

[0136] E5 Configure load balancing, configure load balancing at the hardware and system levels to ensure that requests can be evenly distributed to each processing unit;

[0137] E6 Real-time load monitoring, monitor the system load in real time and automatically adjust the load distribution strategy;

[0138] E7 Feedback optimization, continuously optimize load distribution and system resource configuration according to monitoring data.

[0139] Preferably, the after-sales service processing module specifically includes the following steps:

[0140] F1 Identify unprocessed requests, identify and sort out unprocessed orders and customer requests after the peak order period;

[0141] F2 Classification and filing, classify and file unprocessed requests according to service types;

[0142] F3 Service level evaluation, evaluate the service level of users during the peak period and find service gaps;

[0143] F4 Request priority sorting, set priorities for unprocessed requests according to service level evaluation;

[0144] F5 Process requests, process these requests in an orderly manner according to the set priorities;

[0145] F6 Process status feedback, provide real-time feedback on the processing progress to users to enhance user trust and satisfaction;

[0146] F7 Service effect evaluation, collect users' evaluations of service effects after processing;

[0147] F8 Follow-up optimization, continuously optimize the service process according to feedback to improve the user experience during future peak periods.

[0148] Preferably, the intelligent scheduling algorithm in step A5 specifically includes the following steps

[0149] A51 Data collection and preprocessing, collect real-time order flow data, including order arrival rate, order type and estimated processing time, and extract order processing data for similar time periods from the historical database for prediction and comparison;

[0150] A52 Flow prediction, use time series analysis methods to predict the order flow in the future for a period of time;

[0151] Ft = α·F t-1 +(1 - α)·(O t +β·Tt)

[0152] where F t is the predicted order flow, F t-1 is the actual flow in the previous period, O t is the current order volume, T t is the time trend factor, and α and β are model parameters;

[0153] A53 Resource Requirement Calculation

[0154] Calculate the required amount of resources based on the predicted order flow and the processing requirements of each order;

[0155]

[0156] where Rt is the required amount of resources, P is the average processing requirement of each order, and E is the processing capacity of each resource unit;

[0157] A54 Resource Allocation, Apply for additional computing resources through the cloud service interface according to the calculated resource requirements, and select the most appropriate resource expansion plan using cost - benefit analysis;

[0158]

[0159] where C is the cost of the selected resource plan, C i is the cost of the i - th resource plan, and P i is the performance of the i - th resource plan;

[0160] A55 Resource Assignment, Allocate the newly assigned resources to different processing units through an intelligent scheduling algorithm;

[0161]

[0162] where A i is the amount of resources allocated to the i - th processing unit, W i is the weight of the i - th processing unit, and R t is the total resource requirement;

[0163] A56 Performance Monitoring and Feedback Adjustment, Monitor the performance of each processing unit in real - time, including processing speed and error rate, and dynamically adjust the resource allocation strategy according to the performance data;

[0164] A′ i = A i +ΔA i

[0165] Among them, Ai′ is the adjusted resource allocation, and ΔAi is the amount of resources adjusted according to the performance feedback.

[0166] An efficient resource dynamic scaling mechanism to ensure that the system adapts to different levels of order traffic. The design of the buffer queue ensures that order data will not be lost during peak hours. The design of the multi-level order processing engine optimizes the order processing sequence and efficiency of the system. The order intelligent distribution system, with the optimized application of machine learning algorithms. Secure and stable data processing.

[0167] The present invention can effectively improve the stability and reliability of the order processing system in the face of high-traffic impacts. Through effective order buffering and intelligent distribution, it ensures a good user experience even during peak periods and avoids system crashes or long periods of unresponsiveness. In addition, by using advanced technologies such as machine learning for scientific order allocation, it not only improves the processing capacity but also reduces the operating costs, with significant economic benefits.

[0168] In summary, the order flood storage system of this solution demonstrates high efficiency and reliability under high-traffic conditions, can effectively manage sudden order traffic, and is an ideal choice for e-commerce and logistics service providers.

[0169] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

Claims

1. An efficient and reliable order flood storage system, characterized in that: Includes the following parts: Dynamic capacity scaling mechanism, using the dynamic resource allocation function of cloud services, enables the system to elastically scale resources in a short period of time according to the actual order flow; The order data buffer queue is equipped with an efficient data queue to receive all incoming orders in a short period of time during peak hours and store them in the memory database; Multi-level order processing engine: Create a multi-level processing engine to ensure that each order can be processed in a timely manner. Low-priority orders can be stored and waited first, while high-priority orders can be responded to quickly. The order intelligent distribution module uses machine learning algorithms to intelligently classify and distribute orders, giving priority to orders that are more important to the business; Stress testing and load balancing module: the system performs stress testing regularly to ensure high availability of the system and reasonably distribute the load among multiple servers; After-sales service processing module: After the peak, it can reprocess the order data and handle customer service requests that may not be processed immediately during high traffic periods.

2. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The dynamic capacity expansion mechanism includes the following steps: A1 monitors order flow. Through the flow monitoring system, it can monitor order inflow in real time and capture subtle changes in order flow. A2 Traffic Assessment: Based on the preset traffic threshold, the system automatically assesses the current traffic level to determine whether it is normal, peak or abnormal. The assessment result will trigger the corresponding early warning mechanism to provide decision makers with instant traffic status feedback for quick response. A3 resource demand calculation, combining real-time traffic data and historical order processing records, the system uses algorithms to predict the amount of additional resources required at different traffic levels; A4 resource allocation: By connecting to the interface of the cloud service provider, the system can automatically apply for additional computing resources, including CPU, memory and storage space. The allocation process follows the principle of cost-effectiveness and gives priority to resource expansion solutions with lower costs to achieve optimal resource configuration. A5 Resource Allocation: Newly allocated resources will be quickly integrated into the order processing process to enhance the system's processing capabilities. The system uses intelligent scheduling algorithms to ensure that resources are allocated to the most needed links to maximize resource utilization. A6 performance monitoring: the system continuously monitors the performance of newly added resources to ensure that order processing speed and system stability can be effectively improved. A7 Resource Feedback Adjustment: Based on the data collected by performance monitoring, the system automatically adjusts the resource allocation strategy to adapt to the changing order flow. The adjustment strategy includes the increase, decrease, reallocation and optimization of resources to ensure that the system always runs in the best state.

3. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The order data buffer queue specifically includes the following steps: B1 queue initialization, initialize the buffer queue in memory, set the queue size and timeout mechanism; B2 receives orders. During high traffic periods, all incoming orders are first written into a buffer queue; B3 queue storage: order data stored in the queue must ensure data integrity and consistency; B4 data persistence: to prevent system crashes, the data in the queue is persisted to the database regularly; B5 orders are processed and pulled. When the system traffic drops, orders are pulled from the buffer queue and processed. B6 pulls monitoring to monitor the order processing progress and ensure that the processing speed matches the current system load; B7 queue maintenance, maintains the queue operation status and cleans up expired or duplicate order data.

4. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The multi-level order processing engine specifically includes the following steps: C1 defines the priority rules and determines the priority rules for different orders; C2 order classification, which classifies orders according to priority rules into three levels: high, medium, and low; C3 allocates processing queues and assigns orders to corresponding processing queues according to their priorities; The C4 system gives priority to processing orders in the high-priority order queue; C5 level conversion and processing: when there is spare processing capacity, the orders in the medium and low priority queues are gradually processed; C6 status feedback, real-time feedback on order processing status, ensuring that users can understand the order progress; C7 abnormal order processing, abnormal orders that appear during the processing are transferred to the abnormal processing process.

5. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The order intelligent distribution module specifically includes the following steps: D1 feature analysis, extracting features from order data; D2 model training and evaluation, training machine learning models to identify order priorities and distribution rules; D3 intelligent distribution uses trained models to intelligently allocate and distribute orders; D4 system performance feedback, collecting data on the actual impact of intelligent distribution on system performance; D5 model adjustment: adjust and optimize the distribution model based on feedback to improve the accuracy of distribution; D6: Replace the processing strategy. When the results provided by the model do not meet expectations, manual intervention is required to replace the processing strategy. D7 records and monitors the results of intelligent distribution and continuously monitors the overall performance of the system.

6. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The stress test and load balancing module specifically includes the following steps: E1 defines test scenarios and defines different stress test scenarios based on historical order peaks and expected business expansion; E2 stress test implementation, simulating a large number of order inflows through automated tools to test the system's maximum load capacity; E3 performance data analysis, collecting and analyzing performance data during stress testing; E4 load adjustment plan, formulate or adjust load balancing and resource allocation plan according to test results; E5 configures load balancing at the hardware and system levels to ensure that requests are evenly distributed to each processing unit; E6 real-time load monitoring, real-time monitoring of system load conditions, and automatic adjustment of load distribution strategies; E7 feedback optimization continuously optimizes load distribution and system resource configuration based on monitoring data.

7. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The after-sales service processing module specifically includes the following steps: F1 identifies unprocessed requests, identifying and sorting out unprocessed orders and customer requests after the peak order period ends; F2 classification and filing: unprocessed requests are classified and filed according to service type; F3 service level assessment, assessing the user service level during peak hours and identifying service gaps; F4 request prioritization, setting priorities for unprocessed requests based on service level assessment; F5 processes the requests and processes them in order according to the set priority; F6 processing status feedback, real-time feedback of processing progress to users, enhancing user trust and satisfaction; F7 service effect evaluation: after completing the processing, collect users’ evaluation on the service effect; F8 will conduct subsequent optimization and continuously optimize service processes based on feedback to enhance user experience during future peak periods.

8. According to the efficient and reliable order flood storage system described in claim 1, it is characterized in that: The intelligent scheduling algorithm in step A5 specifically includes the following steps: A51 data collection and preprocessing, collects real-time order flow data, including order arrival rate, order type and estimated processing time, and extracts order processing data of similar time periods from the historical database for prediction and comparison; A52 traffic forecasting, using time series analysis methods to predict order traffic in the future; Ft=α·F t-1 +(1-a)·(O t +β·Tt) Among them, F t is the predicted order flow, F t-1 is the actual flow of the previous period, O t is the current order quantity, T t is the time trend factor, α and β are model parameters; A53 resource requirement calculation, Calculate the amount of resources required based on the predicted order flow and the processing requirements of each order; Among them, Rt is the required amount of resources, P is the average processing requirement of each order, and E is the processing capacity of each resource unit; A54 resource allocation, through the cloud service interface, applies for additional computing resources based on the calculated resource requirements, and uses cost-benefit analysis to select the most appropriate resource expansion plan; Where C is the cost of the selected resource solution, C i is the cost of the i-th resource solution, P i is the performance of the i-th resource solution; A55 resource allocation, which allocates newly allocated resources to different processing units through intelligent scheduling algorithms; Among them, A i is the amount of resources allocated to the i-th processing unit, W i is the weight of the ith processing unit, R t is the total resource requirement; A56 performance monitoring and feedback adjustment: real-time monitoring of the performance of each processing unit, including processing speed and error rate, and dynamic adjustment of resource allocation strategies based on performance data; A'=Ai+ΔAi Among them, Ai′ is the adjusted resource allocation, and ΔAi is the amount of resources adjusted according to performance feedback.

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