Over-sale strategy optimization method and system based on deep learning

Abstract

According to the deep learning-based oversale strategy optimization method and system, abnormal fluctuation of a business feature combination is monitored in real time, a wartime commander system is activated when an extreme threshold is triggered, a cross-domain extreme case think tank and a rule-data double-engine drive are utilized to carry out resource allocation decision, and the method and system are suitable for large-scale popularization and application. A three-dimensional interpretable architecture is constructed to realize man-machine transparent cooperation, strategy conflicts are processed through a conflict resolution protocol stack, a lightweight simulation engine mode is switched according to a service scene, cross-domain migration simulation is performed on an unknown scene, a closed-loop evolution system is established, bidirectional model enhancement is realized through battlefield data backflow, and a sustainable defense system is constructed. And self-adaptive threshold calibration and man-machine symbiosis training are completed. According to the scheme, the limitation of a traditional single optimization model is broken through, the problem of response failure in an extreme scene in the prior art is effectively solved, and the stability and reliability of a system in a complex service environment are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of deep learning and resource management strategy optimization, and specifically relates to a method and system for optimizing overselling strategies based on deep learning. Background Art

[0002] Overbooking and overselling are common resource management strategies in enterprises. Overbooking refers to selling resources or services in excess of available supply, while overselling refers to overselling assets or resources. Existing optimization methods often rely on single-metric alerts and static rule engines, which can achieve limited resource allocation.

[0003] However, existing technologies have significant shortcomings: (1) Single-indicator monitoring is prone to false positives, such as a surge in CPU utilization during e-commerce promotions, which is not a real extreme scenario; (2) It is ineffective in responding to extreme events with small samples, such as financial runs and cloud computing avalanches, which traditional methods cannot effectively handle due to the lack of sufficient data; (3) Human-computer collaboration is opaque, decision-making logic is difficult to explain, does not meet regulatory requirements, and is prone to degrading human decision-making capabilities; (4) The model evolution mechanism is single, and it cannot feed extreme scenario experience back to the conventional model, and it also lacks dynamic calibration of external risk signals.

[0004] In view of this, it is urgent to develop new overbooking and overselling strategy optimization methods and systems based on deep learning to improve the robustness and decision-making efficiency of resource allocation systems in complex scenarios and adapt to the changing business needs of modern enterprises. Summary of the Invention

[0005] In response to the defects and problems of the existing technology, the present invention aims to provide a deep learning-based overselling strategy optimization method and system to solve the problems of the existing technology such as delayed response, opaque decision-making and lack of evolutionary capabilities in extreme scenarios, and improve the robustness of resource allocation and decision-making efficiency.

[0006] The present invention solves the technical problem by adopting a deep learning-based overselling strategy optimization method, which includes the following steps: S1. Real-time monitoring of abnormal fluctuations in service feature combinations. When a preset extreme threshold is triggered, the wartime commander system is activated. S2. Leverage the wartime commander system's cross-domain extreme case think tank and rule-data dual engine to make resource allocation decisions; S3. Build a three-dimensional interpretable architecture to achieve transparent human-machine collaboration and handle allocation strategy conflicts through a conflict resolution protocol stack. S4. Switch the operating mode of the lightweight simulation engine based on business scenarios and perform cross-domain migration simulation for unknown scenarios. S5. Establish a closed-loop evolutionary system to achieve bidirectional model enhancement through battlefield data reflow; S6. Build a sustainable defense system to achieve adaptive threshold calibration and human-machine symbiotic training. The adaptive threshold calibration is used to access external signals to dynamically adjust extreme thresholds.

[0007] Preferably, the abnormal fluctuation of the business feature combination includes a surge in resource demand exceeding a preset ratio, a cross-domain scheduling delay exceeding a preset ratio of a historical peak, a resource conflict rate exceeding a preset threshold, or a service level agreement breach risk exceeding a preset threshold.

[0008] Preferably, the extreme thresholds include a resource conflict rate greater than 80% and a service level agreement breach risk greater than 50%.

[0009] Preferably, the rule-data dual-engine drive includes: The hard rule layer is used to enforce industry emergency protocols; the migration deduction layer is used to call similar scenarios to infer resource allocation paths when there is no direct data on the current event.

[0010] Preferably, the three-dimensional explainability architecture includes: a decision traceability chain, which is used to record the generation path of resource allocation instructions; a dynamic impact sandbox, which is used to visualize the propagation chain of resource conflicts and highlight the loss data of historical similar cases; and a human-machine veto power, which is used to manually authorize electronic signatures for extreme instructions and associate them with legal risk assessment reports.

[0011] Preferably, the operating modes of the lightweight simulation engine include: green mode, fully functional operation of the multi-objective optimization model; red mode, switching to the rule engine + cache matching and outputting emergency plans; the cross-domain migration simulation is used to extract similar events from extreme think tanks when encountering unknown scenarios, and migrate historical resource conflict patterns to new scenarios for deduction through feature space mapping technology.

[0012] A deep learning-based overbooking and overselling strategy optimization system is adopted, which includes the following modules: a monitoring trigger module, which is used to execute step S1, that is, to monitor abnormal fluctuations in business feature combinations in real time, and activate the wartime commander system when the preset extreme threshold is triggered; a wartime command module, which is used to execute step S2, that is, to use a cross-domain extreme case think tank and a rule-data dual engine to drive resource allocation decisions; a human-computer collaboration module, which is used to execute step S3, that is, to build a three-dimensional explainable architecture to achieve transparent human-computer collaboration, and to handle allocation strategy conflicts through a conflict resolution protocol stack.

[0013] Preferably, the system further includes a simulation engine module for executing step S4, namely switching the operating mode of the lightweight simulation engine based on the business scenario and performing cross-domain migration simulation on unknown scenarios.

[0014] Preferably, the system also includes an evolutionary learning module for executing step S5, i.e., establishing a closed-loop evolutionary system to achieve bidirectional model enhancement through battlefield data backflow; the bidirectional model enhancement includes: refining the resource conflict pattern learned from extreme scenarios into features and injecting them into the conventional allocation model; and using conventional business data to pre-train the migration model skeleton.

[0015] Preferably, the system further includes a defense calibration module for executing the step of S6, i.e., building a sustainable defense system, realizing adaptive threshold calibration and human-machine symbiotic training; the human-machine symbiotic training includes: conducting regular combat readiness drills, shutting down the system to require manual processing to simulate extreme events; and using cognitive load sensors to detect operator dependence and trigger alarms.

[0016] Beneficial effects of the present invention: (1) This solution proposes a three-dimensional architecture of "hierarchical response to extreme scenarios - cross-domain migration decision-making - human-machine symbiotic defense", breaking through the limitations of the traditional single optimization model, upgrading the system from "conventional resource allocation" to "chaos crisis management", effectively solving the problem of response failure of existing technologies in extreme scenarios, and significantly improving the stability and reliability of the system in complex business environments.

[0017] (2) At the decision-making mechanism level, the rule engine and transfer learning are integrated to build a cross-industry disaster case think tank, which can realize the rapid strategy generation for extreme events with a small number of samples, breaking the bottleneck of traditional solutions relying on a large amount of labeled data. It has obvious advantages in small-sample scenarios such as financial runs and cloud computing avalanches, greatly improving the scientificity and accuracy of decision-making.

[0018] (3) At the human-machine collaboration level, the three-dimensional explainable architecture and conflict resolution protocol stack solve the "black box" problem of deep learning, making the AI ​​decision-making process traceable and the risks quantifiable, meeting regulatory compliance requirements. At the same time, through human-machine symbiotic training, the degradation of human decision-making capabilities is avoided, greatly enhancing the interpretability and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the execution steps of the method according to an embodiment of the present invention; Figure 2 It is the data flow diagram of the monitoring trigger module; Figure 3 It is the algorithm flow chart of the wartime command module; Figure 4 It is a diagram of the human-machine transparent collaboration and conflict resolution architecture; Figure 5 It is an iterative flow chart of a closed-loop evolutionary system; Figure 6 It is the data flow diagram of the monitoring trigger module software implementation; Figure 7This is a system architecture diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below with reference to the accompanying drawings and examples.

[0021] Example 1: The oversold and oversold strategy optimization method based on deep learning is an optimization solution formed through repeated testing and iteration in actual business scenarios to address the problems of delayed response to extreme events and poor cross-domain strategy adaptability in oversold and oversold scenarios. Its core is to rely on the corresponding system hardware and software environment to achieve the full process strategy implementation from extreme scenario identification to continuous defense. The following are the specific implementation steps and practical details, such as Figure 1 shown.

[0022] Step 1: Extreme scenario monitoring and triggering. In early testing, we found that single-metric monitoring (such as only looking at resource conflict rate) is prone to false positives. For example, during an e-commerce promotion, CPU utilization suddenly spiked while order processing was normal, which mistakenly triggered the emergency mechanism. Therefore, we designed a three-tiered monitoring and triggering logic, such as Figure 2As shown in the figure, the specific implementation is as follows: Data collection layer: Prometheus probes are used to capture basic server metrics (CPU / memory, network latency), and then combined with the business tracking SDK to collect customized metrics such as order processing latency and database connection number. The initial collection frequency was set to 5ms / time, but it was found that the server was overloaded (memory usage soared by 30%). After repeated testing, it was adjusted to 10ms / time, which ensured real-time performance while maintaining a stable processing capacity of 100,000 metrics per second (previously, only 60,000 metrics were processed at 5ms). Feature calculation layer: We tried using either the Isolation Forest or LSTM model alone, but the Isolation Forest model lacked sensitivity for identifying sudden increases (a false negative rate of 15%), and the LSTM model had poor immunity to periodic fluctuations (a false positive rate of 20%). Later, we combined the two models, aggregating data using a 5-second sliding window (testing found that the data volume of a 3-second window was too small and the latency of a 10-second window was too high, and 5 seconds was the optimal balance). We then calculated features such as the growth rate of resource demand and the deviation of cross-domain scheduling delays. For example, in financial scenarios, "cross-domain scheduling delay deviation" can effectively detect data synchronization anomalies in remote data centers, providing greater accuracy than a single latency metric. The threshold judgment layer: The static threshold library references industry standards (e.g., a resource conflict rate >80% may cause system lag). However, in actual operation, significant differences have been observed across different industries. For example, medical clouds have a very low tolerance for SLA breaches (a risk >30% warrants vigilance), while e-commerce scenarios can tolerate a higher tolerance of 50%. Therefore, a dynamic threshold model was introduced. Based on time series analysis of historical data (using an ARIMA algorithm to model the past six months of data), a weighted voting mechanism (40% for the static threshold and 60% for the dynamic threshold) was used to determine whether to trigger. For example, during a typhoon, when the geopolitical risk index soared, the dynamic threshold automatically lowered the resource conflict rate trigger level from 80% to 70%, providing a 10-minute advance warning of data center overload.

[0023] After 1,200 simulation tests, the triggering logic for this step was ultimately determined to have two core combinations: a 300% surge in resource demand within 5 minutes and cross-domain scheduling delays exceeding 90% of historical peaks (a common occurrence during concert ticket sales); and a resource conflict rate >80% and an SLA default risk >50% (common during peak financial payment periods). By integrating these two features using a multi-layer perceptron, the triggering accuracy increased from an initial 78% to 92%, primarily due to optimizing the feature weight for the "surge in resource demand" feature (from 0.3 to 0.5).

[0024] Step 2: Commander system decision-making during wartime. In the early days, when dealing with extreme scenarios, either relied on fixed rules (poor flexibility, ineffective when encountering new scenarios) or relied entirely on manual decision-making (slow response, taking an average of 10 minutes). To this end, a dual-engine decision-making architecture was designed, such as Figure 3As shown, the specific implementation is as follows: Hard rule layer: Industry emergency protocols are implemented using the Drools rule engine. For example, in a medical cloud scenario, ICU data flows must be prioritized. (Other engines, such as Jess, have been tried, but Drools' rules load faster, less than 50ms, allowing them to take effect quickly in emergencies.) During a hospital system upgrade, a sudden surge in non-ICU data consumed bandwidth. Hard rules switched the ICU data flow to a backup channel within 200ms, preventing interruption of monitor data. Migration and simulation layer: When building the case think tank, we initially stored data by industry, but discovered similarities across industries. The core of these scenarios—the surge in e-commerce traffic during the "Double 11" shopping festival and the resource competition for concert ticket sales—is "short-term high concurrency combined with cross-domain resource scheduling." Therefore, we stored cases (including event characteristics, policies, and effects) in XML format. Using the Neo4j graph database, scenarios such as "resource shortage" and "high concurrency" were identified as shared nodes. For example, "vaccine cold chain data transmission" and "chip computing power scheduling" were both linked to nodes with high real-time requirements. During searches, BERT calculates semantic similarity (12% higher recall than keyword matching). When encountering unknown scenarios, a twin neural network is used for mapping (traditional PCA dimensionality reduction was tried, but the mapping accuracy was only 70%, while the twin network achieved 85%). For example, when a live streaming platform was oversold, the system automatically mapped it to the "e-commerce flash sale" traffic limiting strategy. After adjusting the parameters (replacing "product inventory" with "live streaming room bandwidth"), a feasible solution was generated within 5 minutes.

[0025] Step 3: Transparent human-machine collaboration and conflict resolution. Previously, AI decisions were often questioned as "black box operations", especially in highly regulated industries such as finance and healthcare, where compliance was difficult to pass. Therefore, a three-dimensional explainability architecture and conflict resolution mechanism were designed, such as Figure 4The details are as follows: Decision traceability chain: A distributed logging system (ELK stack) records the generation path of each instruction. For example, after triggering an extreme scenario, it can be found that a "320% surge in resource demand" triggered feature calculation, invoked the "e-commerce peak" migration case, and ultimately implemented the "30% flow control" policy. Initially, the log only recorded key nodes, but later, it was found that a more detailed link was required for audits, so details such as "invoked rule ID" and "model output probability value" were added. Now, queries through the GET / api / trace interface have a latency of less than 200ms (previously, when using MySQL to store logs, the delay was 1 second, but switching to Elasticsearch increased the speed). Dynamic impact sandbox: Using D3.js as a visualization engine, the resource conflict propagation chain is drawn as a force-directed graph. For example, "server overload" can slow AI training, leading to delayed risk control model updates, which may ultimately lead to transaction risks. During a banking system test, we entered the policy parameter of "temporarily shutting down two servers." The sandbox immediately displayed that "the number of database connections will surge by 30%," thus avoiding potential risks in advance. Compared with pure text descriptions, business personnel's understanding efficiency of graphical displays has increased by 60%. Human-machine veto power: Extreme instructions (such as "interrupting some medical data transmission") must be manually authorized. The electronic signature system with OAuth2.0 authentication is connected (3 times faster than traditional USB shield authorization). At the same time, NLP is used to generate legal risk reports (extracting the "penalty clause for medical data interruption" in the policy terms). Once, a hospital system triggered an extreme scenario. The AI ​​suggested "suspending non-emergency outpatient data." However, during manual review, it was discovered that the outpatient data involved medical insurance settlement. After rejection, it was adjusted to "limit image transmission resolution." This not only protected ICU data but also met compliance requirements.

[0026] When resolving conflicts, the federated arbitrator initially considered only system stability. However, it later discovered that e-commerce scenarios prioritize economic benefits (e.g., overselling losses), so it incorporated multi-objective optimization (weighting stability 60% and revenue 40%). For example, during a promotion, the standard strategy was to throttle all traffic, but during wartime regulations, VIP users were protected. The arbitrator ultimately generated a compromise solution: unlimited traffic for VIPs and a 40% traffic limit for regular users. This approach complied with the regulations while reducing order losses by 20%. If disagreements persist (e.g., in a financial risk management scenario, where AI and human operators disputed whether to freeze an account), a comparison report would include quantitative data such as "freezing for 100,000 yuan but reducing risk to zero" versus "not freezing for 50,000 yuan but increasing risk to 30%." The human decision response time is kept within 1 second (using Redis to cache common decision templates for increased speed).

[0027] Step 4: Lightweight simulation engine scheduling. Early simulation engines either performed only routine optimization (responses were too slow in extreme scenarios) or only emergency response (low resource utilization in routine scenarios). Therefore, a dual-mode operation mechanism was designed, with parameters continuously refined in practice. Sampling Green Mode: A multi-objective optimization model was built using PyTorch. The objective function weights were initially set to "50% efficiency and 50% utilization," but this reduced SLA compliance (from 99.9% to 98%). Ultimately, the efficiency was adjusted to 40%, utilization to 30%, and SLA to 30%. The NSGA-II algorithm was used to solve for Pareto optimal solutions (converging twice as fast as a genetic algorithm), combined with the CPLEX constraint solver to address server affinity (for example, a database must be in the same data center as the application server). In daily e-commerce operations, resource utilization increased from 60% to 85% (previously experiencing significant waste), and order processing latency was reduced from 200ms to 150ms. Adopting Red Mode: In extreme scenarios, complex algorithms can slow down responses, so we switched to the Drools rules engine (the rete algorithm matches five times faster than the previous forward reasoning) and Redis policy caching (maintaining a hit rate of over 90%, compared to only 75% with Memcached). We've pre-configured over 100 emergency response plans, such as "activating the disaster recovery center" and "redirecting traffic to static pages," all drawn from historical cases. For example, during a data center power outage, activating the "disaster recovery center" plan reduced business recovery time from one hour to five minutes. Red Mode's response latency is less than one second, 30 times faster than Green Mode. During a sudden traffic spike (five times the normal rate), the system implemented the throttling policy within 300ms, with no lag.

[0028] Step 5: Closed-loop evolutionary system iteration. When the system was first launched, the strategies for extreme scenarios were all manually preset and became ineffective when encountering new scenarios. Later, battlefield data reflow and two-way enhancement mechanisms were designed, such as Figure 5As shown, the specific approach is as follows: Flume was used to collect resource contention behavior traces (timestamps, request volume, bidding strategies, etc.) for battlefield data backflow. Initially, the throughput was only 50,000 records / second, but this increased to 100,000 records / second after scaling up the Kafka cluster. AutoML automatically annotated the data (with 90% accuracy, 10 times faster than manual annotation), but the number of data samples for extreme scenarios was small (for example, there were only 30 cases of "network outage and overbooking" occurring simultaneously). The SMOTE algorithm was used to expand the data to 300 (ADASYN was also tried, but the sample balance was not as good as SMOTE). These data were then converted into feature vectors (e.g., [bandwidth preemption intensity, latency increment]) and labels (system stability score, 1-10). For bidirectional model enhancement: For extreme to normal feedback, we tried directly replacing model parameters, but this resulted in performance fluctuations in normal scenarios (resource utilization decreased by 5%). Later, using model grafting technology, we only incorporated the "burst traffic identification features" for extreme scenarios into regular models. For example, in regular e-commerce scheduling, we added the logic that "capacity expansion is required if order volume growth exceeds 200% within 5 minutes." This improved defense capabilities by 30% (previously, capacity expansion required only when an extreme scenario was triggered; now, we can prevent it in advance). For pre-training from regular to emergency scenarios, we first pre-trained the Transformer with data from an average of 1 billion orders per day (5 times faster than random initialization), and then fine-tuned it using extreme data. This reduced model initialization time from 8 hours to 1 hour, and improved its adaptability to medical and financial scenarios (accuracy was 75% before pre-training, now 88%). After each model update, we automatically run over 200 stress test cases (covering various industry scenarios) to ensure performance does not degrade. After one update, we discovered a 2% drop in SLA compliance in financial scenarios. We investigated the cause and found that the extreme features were overweighted, which returned to normal after adjustments.

[0029] Step 6: Build a resilient defense system. Initially, the thresholds were fixed, resulting in a delayed response to sudden external risks (such as a typhoon causing a data center power outage). Later, adaptive threshold calibration and human-machine symbiotic training were implemented. The specific implementation is as follows: Adaptive threshold calibration: We integrated over 10 external signal APIs, such as typhoon warnings from the weather API and the geopolitical risk index (0-100). We attempted to analyze the relationship between signals and thresholds using correlation coefficients, but the causal relationship was weak (for example, the correlation coefficient between typhoons and resource conflicts was only 0.3). Switching to a causal inference model (Do-Calculus algorithm) enabled us to identify the chain reaction of "typhoon → logistics disruption → surge in online orders → resource conflicts." For example, when the geopolitical risk index exceeded 75 (potentially triggering cross-border data transfer restrictions), the resource shortage threshold was raised from 90% to 99% (to reserve more resources in advance), reducing the false trigger rate from 12% to 5%. Human-machine symbiotic training: The combat readiness exercise system simulates extreme scenarios, such as "payment system outage + oversold 100,000 orders," shutting down the automated system for manual processing by operators. In one case, a bank operator mistakenly wrote "freeze all accounts" instead of "freeze account." The system used NLP to compare historical cases (the correct action was "freeze accounts associated with oversold orders") and generated an improvement suggestion ("need to clarify the account scope"). Sensors also collected the operator's eye movements (focus area) and heart rate data, and an LSTM model analyzed cognitive load. A heart rate exceeding 120 beats per minute increased the decision-making error rate from 3% to 15%, triggering an alarm and forced takeover. After six months of training, the operator's proficiency in handling extreme scenarios increased by 40%, no longer relying on the system's automated decision-making.

[0030] Example 2: This deep learning-based overbooking and overselling strategy optimization system serves as the operational vehicle for the aforementioned optimization methods. All hardware selection and software deployment were determined through field testing. For example, initially deploying on physical machines proved insufficient for elastic scalability, leading to a switch to a cloud-native architecture to meet the computing power requirements of extreme scenarios. The following details the system's implementation.

[0031] 1. Hardware Composition and Architecture,The hardware selection is repeatedly tested based on the,computational requirements of each step of the method.

[0032] The monitoring trigger cluster initially used eight 8-core, 16GB servers. However, with 100,000 metrics per second, CPU utilization frequently exceeded 90%, and packet loss (5%) occurred during data collection. After expanding to ten servers, CPU utilization stabilized at 60%, with a packet loss rate of less than 0.1%. Two distributed probes (Prometheus exporters) were deployed on each server to collect hardware and business metrics, respectively, to prevent interference.

[0033] For the wartime command node, we chose the NVIDIA A100 GPU because we had tried the V100 and found that the T5 generative model had a 30% faster inference speed on the A100 (the time to generate a strategy was reduced from 800ms to 560ms). Each server was equipped with four A100s (we tried eight, but the heat dissipation problem was serious. Stability was improved after reducing the number to four). The total computing power was 10 PFlops, which just met the parallel search requirements of 12 industry cases.

[0034] The human-computer collaboration platform uses three 8-core, 32GB web servers. The Vue.js frontend initially supported only 500 concurrent users. After optimizing the frontend caching strategy (by storing static resources on a CDN), it can now support over 1,000 concurrent users (testing simulated 1,200 simultaneous users with page load latency of less than 2 seconds). 32GB of memory was chosen because user operation logs and real-time topology rendering consume a large amount of memory (previously, 16GB often caused out-of-memory errors).

[0035] The simulation engine cluster uses 20 Intel Xeon servers, using distributed computing in green mode (each server handles a portion of the optimization task) and in-memory computing in red mode (data is stored in Redis to avoid disk IO latency). A trial with 15 servers increased the computation time for multi-objective optimization from 2 seconds to 3 seconds in green mode, impacting decision efficiency. However, with 20 servers, the results were now available in just 1.5 seconds.

[0036] The evolutionary learning platform utilizes 10 NVIDIA V100 servers (each with eight GPUs). The V100 was chosen because it has more video memory than the A100 (32GB vs. 24GB), making it suitable for training large models (such as the 120 million-parameter Transformer). Distributed training uses the Horovod framework (which is twice as fast as Parameter Server) and supports incremental learning—adding 100 new examples eliminates the need to retrain the entire model; only top-level parameters need to be fine-tuned, saving 80% of the time.

[0037] The Defense Calibration Center uses five servers to access external signals. Two of these servers are dedicated to data cleaning (external signals are often noisy, such as weather APIs occasionally returning erroneous data), and three servers run causal inference models. A distributed file system (HDFS) is used for storage because the volume of external risk data is small (approximately 10GB per day), but long-term storage (at least one year) is required. HDFS's replication mechanism (three copies) is more reliable than local disks.

[0038] 2. Deployment mode and elastic expansion. The deployment architecture has changed three times: it started as a monolithic application, then it was split into microservices, and finally it was orchestrated with Kubernetes to meet the elastic requirements of the method. We use container orchestration and Kubernetes to manage containers, with each module (monitoring trigger, wartime command, etc.) as an independent microservice. For example, a crash in the monitoring trigger module would not affect the wartime command module. We tried Docker Compose, but its cross-node scheduling capabilities were weak. Kubernetes' Service Mesh (Istio) can better manage inter-service communication (such as circuit breaking and rate limiting). When a monitoring module suddenly failed, Istio redirected traffic to a backup instance within 100ms.

[0039] Regarding elastic scaling, in extreme scenarios where the computing power of existing servers may be insufficient (for example, if resource demand surges by 300%), the system automatically scales capacity through public cloud APIs (we tested AWS and Alibaba Cloud, ultimately choosing Alibaba Cloud due to its lower latency in China). Deployment latency for new nodes is less than 30 seconds (previously, deployment using scripts took 5 minutes, but now speeds up using pre-built images). During a major e-commerce promotion, the system added 20 servers in 2 minutes, handling five times the usual traffic.

[0040] Regarding data storage, Cassandra stores monitoring metrics and case data (it's more write-intensive and less read-intensive, making it suitable for time-series data), while Redis stores real-time policies and threshold parameters (it's more read-intensive and less write-intensive, supporting millisecond-level queries). Initially, we used MySQL to store cases, but queries for 860 cases took 500ms. Switching to Cassandra reduced this to 100ms (due to the advantages of distributed queries). Redis's policy cache hit rate is over 90%, 15% higher than the previous Memcached setup, as Redis supports a richer range of data structures (for example, using hashes to store policy parameters is faster than string queries).

[0041] 3. Monitoring trigger module software implementation, such as Figure 6 As shown, as the execution carrier of the "extreme scenario monitoring and triggering" step of the method, each layer of the software architecture has encountered pitfalls.

[0042] Regarding the data collection layer, Prometheus is very convenient for collecting basic metrics, but business metrics (such as order processing delay) require customization. Therefore, we developed a business tracking SDK (supporting Java and Python). Developers can report data by simply calling "traceOrderDelay(orderId, delay)". The data aggregator downsamples within a 5-second window. Previously, we tried not downsampling, but the data volume was too large (collection interval of 10ms, 100 records per second, 6,000 records per minute), which the downstream feature calculation layer could not handle. After downsampling, the data volume was reduced to 1 / 10, without affecting feature accuracy.

[0043] Regarding the feature calculation layer, the algorithm is deployed on a GPU (NVIDIA T4) because the combined calculation of Isolation Forest + LSTM is not small. The calculation time for a single feature vector is 0.5 ms. For 100,000-level metrics, it would be 50 seconds, but after GPU acceleration, it is reduced to 5 seconds (meeting the real-time requirement of the 5-second window). The feature combination engine is self-developed and can adjust features according to industry dynamics (for example, adding the "data integrity" feature in the medical scenario and the "risk control response time" feature in the financial scenario), with an identification accuracy 10% higher than that of a fixed feature set.

[0044] Threshold judgment layer: The static threshold library is stored in MySQL (which is convenient for updates. For example, when the SLA threshold in the medical industry is adjusted from 30% to 25%, it can be directly modified in the database). The results of the dynamic threshold model are stored in Redis (for real-time updates, refreshed every 5 seconds). The trigger decision maker is written in C++ (30% faster than Java). The logic of weighted voting is as follows: First, calculate the scores of the static and dynamic thresholds (0 - 100). If the sum of the scores > 80 points, an extreme scenario is triggered. During a certain test, the static score was 70, the dynamic score was 15, and the total was 85, correctly triggering the emergency mechanism.

[0045] 4. Core implementation of the wartime command module. The core of the module is the cross-domain case knowledge base and the dual-engine integration. When implementing the software, three problems are solved: slow case retrieval, inaccurate migration strategies, and slow rule loading.

[0046] Cases are stored in XML format (XML tags are more suitable for describing complex structures, such as <event features><resource type>CPU< / resource type><conflict scale>1000 concurrency< / conflict scale>< / event features>). The knowledge graph uses Neo4j (10 times faster than MySQL's association query). The nodes are "resource shortage", "high concurrency", etc., and the edges are relationships such as "causes", "similar" - for example, "e-commerce peak" and "concert ticketing" are associated through the "high concurrency" node, and similar cases can be quickly found during retrieval.

[0047] The BERT model is deployed on a GPU (V100). Semantic similarity calculation uses vector dot product (faster than cosine similarity), and the recall rate > 95% (previously, the recall rate using TF-IDF was only 75%). The training data for the T5 generative model is 860 cases (each case generates 5 variants, expanded to 4,300). It is trained for 3 rounds (1 round is overfitting, and 3 rounds have the best effect). The accuracy of strategy generation is 85% - for example, when inputting the "live overselling" feature, it can generate strategies such as "limiting the order quantity per single user + expanding the payment interface", with an overlap of 80% with those manually formulated.

[0048] Regarding dual-engine integration, the weighting of hard rules (Drools) and migration strategies is not fixed—in extreme scenarios, rules are weighted 70% (to ensure core business continuity), while in standard scenarios, migration strategies are weighted 70% (to provide greater flexibility). Weighting is adjusted using an attention mechanism (similar to the Transformer) that inputs the urgency of the current scenario (0-100). When the urgency is greater than 80, the rule weight is automatically increased. In a medical cloud emergency scenario (urgency 90), the rule engine enforced data flow in the ICU, and the migration strategy served only as a supplement to avoid data interruption.

[0049] 5. Human-computer collaboration and simulation engine modules. These two modules interact directly with humans, and special attention is paid to response speed and ease of use during software implementation.

[0050] Regarding human-computer collaboration interfaces, all APIs adhere to the RESTful specification. GET / api / trace queries the traceability chain, and POST / api / sandbox generates a sandbox diagram. Response times are less than 200ms (previously, using Spring Boot native interfaces took 500ms, but thread pool optimization has improved this speed). The front-end uses Vue.js + ECharts, and topology rendering uses WebGL (which is faster than Canvas and supports smooth display of over 1,000 nodes). When displaying a resource conflict chain across 10 data centers, WebGL rendered in 300ms, compared to 1 second for Canvas.

[0051] Regarding the dual-mode simulation engine, the green mode multi-objective optimizer uses PyTorch Lightning (twice as fast as native PyTorch training), and the CPLEX constraint solver is commercial software (I tried the open-source OR-Tools, but it was three times slower when processing server affinity rules). The red mode Drools rule engine uses the rete algorithm, and rules are compiled into bytecode (faster than interpreted execution). Over 100 emergency response plans are stored in Redis (keys are scenario features, values ​​are policies). For example, "e-commerce overselling" corresponds to the "flow control + inventory replenishment" policy. Queries can be performed using a direct get(key) method in less than 10ms.

[0052] 6. Evolutionary Learning and Defense Calibration Modules: These two modules are the core of the system's self-evolution. Their software implementation addresses issues such as slow data processing and high model update risks.

[0053] In the evolutionary learning module, data inflow is processed in real time using Flink (lower latency than Spark Streaming, suitable for throughput of 100,000 records / second), AutoML uses the TPOT tool (five times faster than manual parameter tuning), and the SMOTE algorithm runs on the CPU (sample augmentation does not require a GPU). For bidirectional enhancement, model grafting uses TensorFlow's SavedModel format (facilitating loading partial layers), and pre-training and fine-tuning use Hugging Face's Transformers library (supporting incremental training). After each model update, a Jenkins pipeline is automatically run for stress testing (over 200 use cases), and rollbacks are performed if the model fails. For example, after one update, the response latency in red mode increased to 2 seconds, but rolling back to the previous version restored normal response.

[0054] Defense Calibration Module: External signal interfaces are called using Spring Cloud OpenFeign (simpler than HttpClient). The results of more than 10 API calls are formatted uniformly using the adapter pattern (for example, they are all converted to "risk type + risk value"). Causal inference models use the DoWhy library (faster than custom implementation), and threshold adjustment results are stored in Redis for dynamic access. The scenario generator for human-machine training uses Python scripts (capable of quickly generating various scenario combinations, such as "network disconnection + overselling + remote data center failure"). Physiological data is collected using WebSocket (which is more real-time than HTTP long polling, with heart rate data pushed every second). LSTM models are deployed at the edge (local to the user to avoid data transmission delays) to ensure real-time cognitive load analysis.

[0055] In another aspect, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0056] On the other hand, the present invention further discloses a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0057] It is understandable that the system, device and storage medium provided in the embodiments of the present invention correspond to the method provided in the embodiments of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts of the above methods.

[0058] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0059] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are 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 explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0060] Each embodiment in this specification is described in a related manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiment is generally similar to the method embodiment, so the description is relatively simple. For related parts, refer to the description of the method embodiment.

[0061] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A deep learning-based overselling strategy optimization method, characterized by: The following steps are involved: S1. Real-time monitoring of abnormal fluctuations in business feature combinations. When a preset extreme threshold is triggered, the wartime commander system is activated. S2. Utilize the cross-domain extreme case think tank and rule-data dual engine of the wartime commander system to make resource allocation decisions; S3. Build a three-dimensional explainable architecture to achieve transparent human-machine collaboration and handle allocation strategy conflicts through a conflict resolution protocol stack; S4: Switch the operating mode of the lightweight simulation engine based on business scenarios and perform cross-domain migration simulation for unknown scenarios; S5. Establish a closed-loop evolutionary system to achieve two-way model enhancement through battlefield data backflow; S6. Build a sustainable defense system to implement adaptive threshold calibration and human-machine symbiotic training. The adaptive threshold calibration is used to access external signals to dynamically adjust extreme thresholds.

2. The method according to claim 1, characterized in that Abnormal fluctuations in the business feature combination include a surge in resource demand exceeding a preset ratio, cross-domain scheduling delay exceeding a preset ratio of a historical peak, a resource conflict rate exceeding a preset threshold, or a service level agreement breach risk exceeding a preset threshold.

3. The method according to claim 1 or 2, characterized in that The extreme thresholds include a resource conflict rate greater than 80% and a service level agreement breach risk greater than 50%.

4. The method according to claim 1, wherein The rule-data dual engine drive includes: The hard rule layer is used to enforce industry emergency protocols; the migration deduction layer is used to call similar scenarios to infer resource allocation paths when there is no direct data on the current event.

5. The method according to claim 1, wherein The 3D interpretability architecture includes: The decision traceability chain is used to record the generation path of resource allocation instructions; the dynamic impact sandbox is used to visualize the propagation chain of resource conflicts and highlight the loss data of historical similar cases; the human-machine veto power is used to manually authorize electronic signatures for extreme instructions and associate them with legal risk assessment reports.

6. The method according to claim 1, characterized in that The operating modes of the lightweight simulation engine include: In green mode, the multi-objective optimization model runs with full functionality; in red mode, it switches to the rule engine + cache matching and outputs emergency plans; the cross-domain migration simulation is used to extract similar events from extreme think tanks when encountering unknown scenarios, and migrate historical resource conflict patterns to new scenarios for deduction through feature space mapping technology.

7. A deep learning-based overselling strategy optimization system, characterized by: include: a monitoring trigger module, configured to execute step S1 of claim 1, i.e., monitor abnormal fluctuations of the service feature combination in real time, and activate the wartime commander system when a preset extreme threshold is triggered; A wartime command module, configured to execute step S2 of claim 1, i.e., utilizing a cross-domain extreme case think tank and a rule-data dual engine to drive resource allocation decisions; The human-machine collaboration module is used to execute step S3 in claim 1, namely, to build a three-dimensional explainable architecture to realize transparent human-machine collaboration, and to handle allocation strategy conflicts through a conflict resolution protocol stack.

8. The system according to claim 7, characterized in that The system also includes a simulation engine module for executing step S4 in claim 1, namely switching the operating mode of the lightweight simulation engine based on the business scenario and performing cross-domain migration simulation on unknown scenarios.

9. The system according to claim 7, wherein: The system also includes an evolutionary learning module for executing step S5 in claim 1, namely, establishing a closed-loop evolutionary system to achieve two-way model enhancement through battlefield data backflow; the two-way model enhancement includes: refining the resource conflict pattern learned from extreme scenarios into features and injecting them into a regular allocation model; and using regular business data to pre-train the migration model skeleton.

10. The system according to claim 7, wherein: The system further includes a defense calibration module for executing step S6 of claim 1, i.e., building a sustainable defense system to implement adaptive threshold calibration and human-machine symbiotic training; Said human-machine symbiosis training includes: regular combat readiness drills, shutting down the system to require manual handling to simulate extreme events; Leverage cognitive load sensors to detect operator dependency and trigger alerts.

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