Resource presetting and cost optimization method and system for potential service interruption event

Abstract

The invention relates to the technical field of business resource scheduling cost optimization, in particular to a resource presetting and cost optimization method and system for a potential business interruption event, and the method comprises the steps: recognizing potential risk points through fusing multi-source data, generating a multi-probability discrete risk scene, and precisely quantifying the demand of emergency resources; constructing a two-stage stochastic programming model, and solving an optimal baseline resource preset scheme and a scenarized emergency scheduling strategy set by taking the minimization of the total expected cost as a target; risk symptoms are tracked in real time through a multi-dimensional monitoring system, an adaptive scheduling strategy is automatically triggered, and model parameters are calibrated to realize iterative optimization. According to the method, accurate risk identification, scientific cost management and control and rapid dynamic response are realized, continuous operation of services is effectively guaranteed, and the resource utilization efficiency and the risk response capability are improved.

Description

Technical Field

[0001] This invention relates to the field of business resource scheduling cost optimization technology, specifically to a method and system for resource pre-provisioning and cost optimization in response to potential business interruption events. Background Technology

[0002] Current resource management methods for business interruption risks in the industry have many shortcomings. In the risk identification and demand assessment stages, traditional methods often rely on single-dimensional historical data analysis, lacking effective integration of real-time business operation status and external environmental data. This leads to incomplete identification of potential risk points and easily overlooks low-probability, high-impact critical risks. Simultaneously, the estimation of emergency resource requirements often uses static average methods, failing to fully consider dynamic factors such as business fluctuations caused by interruption events and resource substitution elasticity. This results in significant deviations in demand quantification, either failing to meet emergency needs due to underestimation or leading to resource idleness due to overestimation.

[0003] In terms of resource allocation decisions, existing solutions mostly adopt a static pre-provisioning model, lacking phased dynamic optimization design. Some enterprises over-provision baseline resources to avoid risks, leading to a significant increase in resource acquisition costs, and the maintenance and opportunity costs of idle resources continue to accumulate, resulting in serious waste. Other enterprises reduce pre-provisioning resources to control costs, but when sudden risks occur, emergency resource scheduling lacks scientific strategy support, with unreasonable scheduling paths and inaccurate time control. This not only makes it difficult to quickly fill resource gaps, but may also further amplify losses due to excessively high incremental scheduling costs. In addition, existing decision-making models often focus only on a single cost type, failing to comprehensively balance baseline pre-provisioning costs, idle penalty costs, emergency scheduling costs, and business interruption loss costs, resulting in poor overall cost optimization.

[0004] At the risk response execution level, the traditional model lacks an integrated monitoring system covering external risk sources in resource reserve operations, resulting in delayed identification of risk signs and failure to trigger emergency responses in a timely manner. Furthermore, emergency dispatch strategies are not sufficiently adaptable to risk scenarios, often employing a uniform dispatch scheme that struggles to meet the differentiated needs of various risk scenarios. The dispatch execution process also lacks effective status tracking and feedback mechanisms, making it impossible to adjust strategies based on actual performance, leading to low business recovery efficiency.

[0005] These problems make it difficult for existing resource management methods to achieve an effective balance between business continuity assurance and cost control, failing to meet the requirements of modern business for accurate, efficient, and economical risk response. Therefore, there is an urgent need for a resource provisioning and cost optimization solution that can comprehensively identify risks, accurately quantify needs, scientifically optimize decision-making, and provide rapid dynamic response to address the pain points of existing technologies. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for resource provisioning and cost optimization in response to potential business disruption events, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: The resource provisioning and cost optimization methods for potential business disruption events include the following steps: Step S1, Resource Demand Risk Quantification: Based on historical and real-time data, identify potential resource interruption risk points, and simulate multiple discrete risk scenarios with different probabilities of occurrence within a preset optimization period for each risk point; for each risk scenario, quantify the emergency resource demand it will trigger when it occurs. Step S2, Two-stage stochastic optimization decision: Establish a stochastic programming model with the objective of minimizing the total expected cost within a preset optimization period; wherein, the total expected cost includes the baseline resource pre-set cost and the pre-set resource idle penalty cost corresponding to the first stage decision, and the business interruption loss cost caused by resource shortage in each risk scenario and the incremental cost generated by triggering emergency resource scheduling corresponding to the second stage decision; The constraints of the stochastic programming model include: resource supply and demand balance constraints under each risk scenario, and total budget constraints for resource acquisition; The model is solved using a stochastic programming algorithm, which outputs a globally optimal baseline resource pre-configuration scheme and a set of optimal emergency resource scheduling strategies corresponding to multiple discrete risk scenarios. Step S3, Dynamic Response Execution: Perform initial resource deployment according to the baseline resource pre-configuration plan; monitor risk signs in real time, and when the monitoring information matches any simulated risk scenario, automatically trigger and execute the optimal emergency resource scheduling strategy corresponding to that risk scenario.

[0008] As a preferred approach, based on historical and real-time data, potential resource interruption risk points are identified, and for each risk point, multiple discrete risk scenarios with different probabilities of occurrence within a preset future optimization period are simulated and generated; including: Perform multi-dimensional feature analysis on historical business interruption event data to extract risk triggering factors associated with resource supply; combine real-time business operation status data and external environment monitoring data, and use multi-source data fusion technology to identify a set of potential risk points that may lead to resource supply interruption during the optimization cycle. For each identified risk point, a scenario building method is used to generate multiple possible risk evolution paths based on its risk intensity, scope of impact and duration. Each evolution path is matched with a preset probability threshold range to obtain multiple discrete risk scenarios corresponding to different probability levels of occurrence. Each risk scenario includes specific triggering conditions, impact start time and duration.

[0009] As a preferred approach, for each risk scenario, the required emergency resources when it occurs are quantified; including: Based on the triggering conditions, impact start time and duration defined for each risk scenario, and combined with historical business load data and resource consumption patterns, a resource demand prediction model is constructed. The resource demand forecasting model is used to simulate the baseline resource demand of the affected business units at each moment during the duration of the scenario when the risk scenario occurs. The business fluctuation coefficient and resource substitution elasticity coefficient caused by the interruption event are further superimposed to calculate the total demand and time-period demand of various emergency resources required to maintain continuous business operation.

[0010] As a preferred approach, a stochastic programming model is established with the objective of minimizing the total expected cost within a preset optimization period, including: The first-stage decision is defined as the baseline resource provision that needs to be determined at the beginning of the optimization cycle and before the occurrence of risk scenarios; The second-stage decision is defined as the amount of emergency resources that need to be allocated when any risk scenario actually occurs within the optimization cycle. A target function is constructed using the baseline resource pre-configuration amount as the first variable and the emergency resource scheduling amount under each risk scenario as the second variable. The objective function is to minimize the total expected cost consisting of the following components: Baseline resource pre-configuration cost determined based on baseline resource pre-configuration quantity and unit pre-configuration cost; The pre-set resource idle penalty cost is determined based on the baseline resource pre-set amount that may be idle and the unit idle penalty cost; Based on the probability of occurrence of each risk scenario, the amount of emergency resources required for that risk scenario and the unit scheduling cost that is higher than the baseline preset cost, the expected incremental cost of emergency resources for all risk scenarios is determined. Based on the probability of occurrence of each risk scenario, the amount of emergency resource shortage that cannot be met under that risk scenario, and the unit shortage penalty cost, the expected business interruption loss cost under all risk scenarios is determined.

[0011] As a preferred option, the constraints of the stochastic programming model specifically include: For each risk scenario, a resource supply and demand balance constraint is defined. This constraint requires that, under the risk scenario, the sum of the baseline resource pre-positioned quantity and the corresponding emergency resource scheduling quantity should not be less than the emergency resource demand caused by the risk scenario. Define a total budget constraint for resource acquisition, which requires that the baseline resource provisioning cost must not exceed a preset budget limit; Define a non-negativity constraint for emergency dispatch resources; It also includes emergency dispatch trigger constraints: for any risk scenario, the corresponding emergency resource dispatch quantity can only be greater than zero when the scenario is determined to have occurred; otherwise, the dispatch quantity is zero. In addition, there are constraints on the availability of emergency resources: for any risk scenario, the corresponding emergency resource allocation shall not exceed the maximum resource supply that can be obtained from emergency channels in advance when the scenario is triggered.

[0012] As a preferred approach, a stochastic programming algorithm is used to solve the model, specifically including: The stochastic programming model is constructed as a two-stage stochastic integer programming problem, where the decision variables in the first stage are integers, and the decision variables in the second stage are integers or continuous variables depending on the resource characteristics. The solution is obtained using a scene decomposition-based algorithm, which specifically includes: The original problem is decomposed into a deterministic main problem and multiple sub-problems corresponding to different risk scenarios. The main problem includes the first-stage decision variables and the expected cost approximation terms related to all scenarios. Each sub-problem is for a specific risk scenario and is used to evaluate the optimal second-stage decision and corresponding cost in that scenario given the first-stage decision scheme. Approximation is achieved by iteratively solving the main problem and sub-problems: First, the main problem is solved to obtain an initial baseline resource pre-configuration scheme; then, this scheme is used as input parameters to solve the sub-problems corresponding to all risk scenarios, obtaining the optimal scheduling strategy and accurate scenario cost for each scenario; then, the optimality cut is generated based on the sub-problem solution results, and this cut is added to the constraints of the main problem to correct the approximation of the expected cost in the main problem; Repeat the above iterative process until the difference between the objective function value of the main problem and the actual expected cost obtained from the subproblems is less than the preset convergence threshold. At this point, output the current solution to the main problem as the globally optimal baseline resource provisioning scheme, and output the solutions to all subproblems as the optimal emergency resource scheduling strategy set.

[0013] As a preferred embodiment, step S3 specifically includes: Based on the globally optimal baseline resource provisioning scheme, at the beginning of the optimization cycle, the corresponding type and quantity of baseline resources are configured to the designated resource reserve nodes to complete the initial resource deployment and generate an initial deployment list containing resource locations and statuses. Based on the initial deployment list, a multi-dimensional monitoring system covering all resource reserve nodes, business operation units and external risk sources is established to collect resource inventory data, business load data and external environment data in real time; the collected data is then fused and processed using the multi-dimensional monitoring system to generate real-time risk symptom feature vectors containing timestamps. The real-time risk symptom feature vector is synchronously compared and similarity is calculated with the preset trigger conditions of each risk scenario in multiple discrete risk scenarios; when the matching degree of a certain risk scenario exceeds the preset trigger threshold, the risk scenario is determined to be activated, and the corresponding optimal emergency resource scheduling strategy is locked. Based on the locked optimal emergency resource dispatch strategy, generate detailed dispatch instructions including emergency resource type, dispatch quantity, supply path and time window; automatically issue dispatch instructions to resource reserve nodes and emergency supply channels to initiate the emergency resource transportation process; During the execution of the emergency resource allocation process, the availability of resources and the status of business recovery are continuously tracked to generate execution feedback data. Based on the execution feedback data and the actual resource consumption cost, the unit scheduling cost and unit shortage penalty cost parameters in the stochastic programming model are calibrated, and the calibrated parameters are fed back to step S2 in the subsequent optimization cycle for iterative updates of the model.

[0014] A resource provisioning and cost optimization system for potential business interruption events. The system is used to execute resource provisioning and cost optimization methods for potential business interruption events.

[0015] As can be seen from the technical solutions provided by the present invention above, the resource pre-provisioning and cost optimization method and system for potential service interruption events provided by the present invention have the following beneficial effects: Accurate and efficient risk identification and demand quantification: This invention integrates historical business interruption data, real-time business operation data, and external environment data to extract risk-inducing factors from multiple dimensions, accurately identify potential resource interruption risk points, and generate multi-probability discrete risk scenarios by combining risk intensity, scope of impact, and duration. At the same time, by superimposing business fluctuation coefficients and resource substitution elasticity coefficients into a resource demand prediction model, it accurately calculates total demand and demand in different time periods, effectively solving the problems of vague risk identification and large deviations in demand estimation in traditional methods. This provides reliable data support for subsequent resource allocation and avoids waste caused by business interruption due to insufficient resource estimation or waste caused by excessive resource pre-provisioning. Significant cost optimization results: The two-stage stochastic programming model constructed in this invention comprehensively covers the baseline resource pre-positioning cost, idle penalty cost, emergency scheduling incremental cost, and business interruption loss cost. With the goal of minimizing the total expected cost, it combines multiple constraints to ensure the feasibility of the solution. Through efficient solution by scenario decomposition algorithm, it outputs the globally optimal baseline resource pre-positioning solution and scenario-based emergency scheduling strategy, realizing a dynamic balance between resource pre-positioning and emergency scheduling. This significantly reduces the idle cost caused by blind pre-positioning and the interruption loss cost under sudden scenarios, and significantly improves resource utilization efficiency and cost control accuracy. The dynamic response is timely and highly adaptable: The multi-dimensional monitoring system established by this invention collects multi-source data in real time, and quickly activates the optimal scheduling strategy for the corresponding risk scenario through feature vector matching. It generates detailed instructions including resource type, scheduling quantity, supply path and time window, so as to realize the rapid transportation and precise deployment of emergency resources. At the same time, it continuously tracks the resource arrival status and business recovery status during the execution process, calibrates the key parameters of the model and feeds them back to the subsequent optimization cycle, promotes the continuous iteration and upgrading of the decision model, and constantly adapts to the changes in actual business scenarios, thereby improving the pertinence and effectiveness of the response strategy. Outstanding Business Continuity Assurance Capabilities: This invention combines forward-looking baseline resource pre-provisioning with scenario-based emergency dispatch strategies to ensure that resource supply can cover demand under various risk scenarios, effectively avoiding business interruptions caused by resource shortages; at the same time, the visualized tracking and time window control of the emergency dispatch process ensures that resources are delivered accurately within the effective time, helping affected businesses to quickly resume normal operation, significantly improving the business system's anti-interference capability and stability in the face of potential interruption risks, and providing a solid guarantee for continuous business operation. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the steps of the resource pre-provisioning and cost optimization method for potential business interruption events according to the present invention; Figure 2 This is a schematic diagram of the resource pre-provisioning and cost optimization system structure for potential business interruption events according to the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific embodiments.

[0019] like Figure 1-2 As shown, this embodiment of the invention provides a method for resource provisioning and cost optimization for potential service interruption events, including the following steps: Step S1, Resource Demand Risk Quantification: Based on historical and real-time data, identify potential resource interruption risk points, and simulate multiple discrete risk scenarios with different probabilities of occurrence within a preset optimization period for each risk point; for each risk scenario, quantify the emergency resource demand it will trigger when it occurs. Step S2, Two-stage stochastic optimization decision: Establish a stochastic programming model with the objective of minimizing the total expected cost within a preset optimization period; wherein, the total expected cost includes the baseline resource pre-set cost and the pre-set resource idle penalty cost corresponding to the first stage decision, and the business interruption loss cost caused by resource shortage in each risk scenario and the incremental cost generated by triggering emergency resource scheduling corresponding to the second stage decision; The constraints of the stochastic programming model include: resource supply and demand balance constraints under each risk scenario, and total budget constraints for resource acquisition; The model is solved using a stochastic programming algorithm, which outputs a globally optimal baseline resource pre-configuration scheme and a set of optimal emergency resource scheduling strategies corresponding to multiple discrete risk scenarios. Step S3, Dynamic Response Execution: Perform initial resource deployment according to the baseline resource pre-configuration plan; monitor risk signs in real time, and when the monitoring information matches any simulated risk scenario, automatically trigger and execute the optimal emergency resource scheduling strategy corresponding to that risk scenario.

[0020] In this embodiment, step S1 aims to accurately identify potential resource interruption risk points based on historical and real-time multi-source data, simulate and generate discrete risk scenarios with different probabilities of occurrence, quantify the emergency resource demand under each scenario, and provide comprehensive and accurate risk and resource demand data support for the subsequent two-stage stochastic optimization decision-making. The detailed steps are as follows: Step S1-1: Extraction of risk-inducing factors and identification of potential risk points: Collect historical business interruption event data, covering multiple dimensions such as event occurrence time, impact scope, scale of resource supply gap, recovery time, and type of triggering factors; use statistical analysis and feature engineering methods to deeply mine the data and extract risk triggering factors directly related to resource supply, such as resource supplier capacity fluctuations, transportation link interruptions, external policy adjustments, equipment failure frequency, energy supply instability, and network link interruptions. Synchronously collect real-time business operation status data, including current business load, real-time resource consumption rate, resource inventory level, and business unit operation status; as well as external environmental monitoring data, including meteorological data, traffic operation data, industry policy dynamics, supply chain upstream and downstream operation status, and regional infrastructure operation status; utilize multi-source data fusion technology to sequentially perform data cleaning, data standardization, and feature-level fusion operations to integrate data from different sources and in different formats into a dataset with a unified structure; through correlation analysis and anomaly detection algorithms, identify abnormal features in the data related to resource supply interruptions, and then determine the set of potential risk points that may lead to resource supply interruptions within a preset optimization period; After data fusion and risk point identification are completed, the system automatically verifies the validity and relevance of the data, verifies the consistency of timestamps between real-time business data and external environment data, the causal correlation between risk triggering factors and resource supply interruption events, and the logical rationality of risk point identification results. If there are abnormal situations such as missing data, insufficient relevance, or contradictory identification results, the system will trigger a data supplementation or re-analysis process to ensure the accuracy and completeness of the potential risk point set. Step S1-2: Risk Evolution Path Construction and Discrete Risk Scenario Generation: For each identified potential risk point, a scenario-building method is used to simulate and generate multiple possible risk evolution paths, combining three core indicators: risk intensity, scope of impact, and duration. Risk intensity is calculated using the following formula: ,in, As for the level of risk, , , The weighting coefficients for the scope of impact, the proportion of resource gap, and the duration are respectively, and they satisfy the following conditions: , The proportion of business units affected by risk out of the total number of business units. The proportion of the resource supply gap caused by risks to the total normal supply. This is the ratio of the duration of the risk to the preset optimization period. Based on the calculation results of risk intensity and combined with the characteristics of the inducing factors of risk points, differentiated evolution paths are constructed. For example, a high-risk intensity path corresponds to an evolution trend with a wide impact range, a large resource gap, and a long duration; a medium-risk intensity path corresponds to an evolution trend with a moderate impact range, a moderate resource gap, and a moderate duration; and a low-risk intensity path corresponds to an evolution trend with a small impact range, a small resource gap, and a short duration. Multiple probability threshold ranges are preset, such as a high probability range of 0.7 to 1.0, a medium probability range of 0.3 to 0.7, and a low probability range of 0 to 0.3. Each risk evolution path is matched with the probability threshold range to obtain multiple discrete risk scenarios corresponding to different probability levels of occurrence. Each risk scenario clearly includes specific triggering conditions, the start time of impact (i.e., the specific moment when the risk begins to affect the business system), and the duration (i.e., the complete time span from the occurrence of the risk to its dissipation). After a scenario is generated, the system automatically verifies its rationality, checking whether the probability distribution between different scenarios generated from the same risk point conforms to statistical laws, whether the start time and duration of the scenario's impact are within the preset optimization period, and whether the correlation between the scenario's triggering conditions and the risk-inducing factors is valid. If there are unreasonable scenarios, the evolution path parameters or probability threshold range are adjusted and the scenarios are regenerated to ensure the scientificity and practicality of discrete risk scenarios. Step S1-3: Construction of resource demand forecasting model: For each discrete risk scenario, extract key parameters such as the defined triggering conditions, the start time of impact, and the duration; collect historical business load data for the same period, including the average load, peak load, and load fluctuation patterns of each business unit within the corresponding time period; and resource consumption pattern data, including the unit load consumption rate of various resources, the time distribution characteristics of resource consumption, the differences in resource consumption among different business units, and the correlation between resource consumption and business load. The historical data on business load and resource consumption patterns are preprocessed, and outlier removal, data smoothing, and time series alignment are performed in sequence. This process removes extreme outliers, eliminates random fluctuations, and ensures that the time dimensions of the two types of data are consistent, thus guaranteeing the continuity and consistency of the data. Using preprocessed historical concurrent business load data and resource consumption pattern data as input, and the resource demand baseline under risk scenarios as output, a resource demand prediction model is constructed. The model uses a time series prediction algorithm combined with business correlation analysis. By learning the correspondence between business load and resource consumption in historical data, a mapping function between the two is established, enabling the model to accurately simulate the trend of resource demand changes when risk scenarios occur based on the time parameters and business operation characteristics of risk scenarios. After the model is built, historical validation data is used to evaluate the model's performance and calculate the deviation rate between the predicted and actual values. If the deviation rate exceeds the preset threshold, the core parameters of the model are adjusted and the model is retrained until the model's prediction accuracy meets the preset requirements, ensuring that the model can reliably output the resource demand baseline under risk scenarios. Step S1-4: Calculation of emergency resource requirements: Launch the completed resource demand forecasting model, input the start time and duration of the impact of the current risk scenario, and simulate the baseline resource demand of the affected business units at each time point during the duration of the scenario. ,in, These are points in time within the duration of the scene; Based on this, add the business fluctuation coefficient caused by the interruption event. With the elasticity coefficient of resource substitution The emergency resource demand at each time point was calculated. The calculation formula is: ,in, The first time during the scene duration The amount of emergency resources needed at any given moment. For the first Baseline of resource requirements at any given time. The business volatility coefficient reflects the degree of impact of business load fluctuations caused by interruption events on resource demand. For the first The resource substitution amount at any given time is the amount of resources that need to be substituted due to the unavailability of some resources. The resource substitution elasticity coefficient reflects the effect of the ease or difficulty of resource substitution on demand adjustment. Emergency resource requirements at all times during the duration of the scenario By summing up the results, we can obtain the total demand for various emergency resources required to maintain business continuity under this risk scenario. ,Right now: ,in, This represents the total demand for emergency resources. The total number of moments within the scene's duration; Simultaneously, output the time-segmented demand at each moment. This process generates complete emergency resource demand data, including total demand and demand by time period. After the demand is calculated, the system automatically verifies the data's rationality, checking whether the total demand is within the reasonable range of demand in similar historical scenarios, whether the changing trend of demand by time period is consistent with business operation patterns, and whether the cumulative value of demand by time period is consistent with the total demand. If any anomalies are found, the model calculation process is retried to ensure the accuracy and availability of emergency resource demand data.

[0021] In this embodiment, step S2 is based on the discrete risk scenarios, the probability of occurrence of each scenario, and the emergency resource demand data generated in step S1. It constructs a two-stage stochastic programming model with the objective of minimizing the total expected cost within a preset optimization period. The model is solved using a scenario decomposition algorithm, outputting the globally optimal baseline resource pre-configuration scheme and the optimal emergency resource scheduling strategy set corresponding to each risk scenario. This provides a scientific decision-making basis for the dynamic response execution in step S3. The detailed steps are as follows: Step S2-1: Definition of decision variables for stochastic programming model: Clarify the connotation, value type, and applicable scope of the two-stage decision variables; define the first-stage decision variable as the baseline resource pre-provision quantity, using... This refers to the quantity of various basic resource reserves that need to be determined and deployed before the start of the optimization cycle and before the occurrence of risk scenarios; based on the physical characteristics of the resources, The value is a positive integer and Its physical meaning is the total amount of resources pre-configured at the resource reserve node, which needs to cover the basic resource needs that may arise in various risk scenarios during the optimization period; The second-stage decision variable is defined as the emergency resource allocation quantity, using... It means that, among them, Representing the Individual risk scenarios. , , , The total number of risk scenarios, i.e., when the first... The quantity of various resources replenished from emergency supply channels after a risk scenario actually occurs; The value type is determined based on the resource characteristics. Indivisible resources take non-negative integer values, while divisible resources take non-negative consecutive numbers. Its physical meaning is the amount of resource gap replenishment for specific risk scenarios, which is used to complement the baseline resource pre-positioned amount to meet emergency resource needs; Step S2-2: Composition of Total Expected Cost and Construction of Objective Function: The total expected cost consists of four cost components. The objective function aims to minimize this total expected cost, and its specific construction process is as follows: Category 1: Baseline resource provisioning costs; these are the fixed expenses incurred in deploying baseline resources, calculated using the following formula: ,in, Pre-configure costs for baseline resources. The pre-configured cost per unit of baseline resources. Baseline resource provisioning; Category Two: Pre-set resource idle penalty cost; this cost is the opportunity cost or maintenance cost incurred when baseline resources are not utilized, and the calculation formula is: ,in, To pre-set the penalty cost for idle resources, Pre-allocate penalty costs for idle resources to the unit. For the first Emergency resource requirements for each risk scenario For expectation operator, Indicates the first The baseline idle amount of unused resources in each scenario is expected to be represented as a probability-weighted average of the idle amount in all risk scenarios. The third category: Expected incremental cost of emergency resources; this cost is the additional expenditure incurred in scheduling emergency resources after a scenario occurs, and the unit scheduling cost is higher than the baseline preset cost. The calculation formula is as follows: ,in, To increase the cost of emergency resources, For the first The probability of occurrence of each risk scenario. For unit emergency resource dispatch cost and , For the first The emergency resource allocation amount under each risk scenario is summed to reflect the probability-weighted accumulation of incremental costs across all scenarios. Category 4: Expected business interruption loss cost; this cost represents the business loss caused by a resource supply-demand gap in the scenario, and the calculation formula is as follows: ,in, To account for the expected business interruption loss costs, The cost of penalties for unit emergency resource shortages Indicates the first The shortage of emergency resources in these scenarios still cannot be met. The shortage is zero, resulting in no loss cost. Combining the four cost components, the objective function of the stochastic programming model is: ; Substituting various cost formulas into the objective function yields the complete expression: ;in, Given a preset total expected cost over the optimization period, the objective is to adjust the decision variables... and make Reaching the minimum value; Step S2-3: Setting constraints for the stochastic programming model: To ensure the feasibility of the model solution and its adaptability to actual business operations, five types of constraints are set, as follows: First, resource supply and demand balance constraints; for each risk scenario, the sum of the baseline resource pre-provision and the emergency resource scheduling must be able to cover the emergency resource demand in that scenario to ensure continuous business operation. The constraint expression is as follows: ; This constraint ensures that resource supply is not lower than demand in each scenario, thus avoiding business interruption due to insufficient resources. Second, the total budget constraint for resource acquisition; the baseline resource provisioning cost must not exceed the preset budget limit to avoid resource provisioning expenditures exceeding the business's affordability. The constraint expression is as follows: ;in, Set the preset total budget limit for resource acquisition; Third, there is a non-negative constraint on the amount of emergency dispatch resources; the amount of emergency dispatch resources is a supplementary resource quantity, and there is no negative dispatch situation. The constraint expression is as follows: ; This constraint limits the range of non-negative values ​​for the scheduling quantity; Fourth, emergency dispatch triggering constraints; emergency resource dispatch is only initiated when the corresponding risk scenario is determined to have occurred. No emergency dispatch occurs if the scenario has not occurred. The constraint expression is: , ; in, For the first The occurrence status indicator variable of each scenario, Indicates that the scene has occurred. Indicates that the scenario has not occurred; when hour, ,when hour, The value can be freely selected according to the constraints; Fifth, emergency resource availability constraints; the amount of emergency resources allocated in each scenario must not exceed the maximum resource supply that the emergency channel can provide when the scenario is triggered. The constraint expression is as follows: , in, For the first The maximum amount of resources that can be obtained from emergency channels when a scenario is triggered is determined by pre-assessing the resource reserve capacity of emergency supply channels; Step S2-4: Solve using a stochastic programming algorithm based on scenario decomposition: The constructed stochastic programming model is transformed into a two-stage stochastic integer programming problem, with the first stage decision variables... Integers, second-stage decision variables Based on the fact that resource characteristics are integers or continuous variables, a scenario-based decomposition algorithm is used to solve the problem. The specific steps are as follows: The first step is to construct the main problem; the original problem is decomposed into a deterministic main problem and multiple scenario sub-problems. The main problem includes the decision variables for the first stage. And for all scenario-related expected cost approximations, ignoring the scenario-specific nature of the second-stage decision variables, the initial master problem is constructed as follows: ; st ; , in, The objective function value of the main problem. This is an approximation of the expected value of the idle quantity. This is an approximation of the expected incremental cost of emergency resources. This is an approximation of the expected business interruption loss cost. The set of positive integers is used. The constraints of the main problem are limited to budget constraints and variable value constraints relevant to the first-stage decision. The second step is to solve the initial main problem. An integer programming algorithm is used to solve the initial main problem, yielding the initial baseline resource provisioning scheme. and the initial value of the objective function of the main problem ; The third step is to solve the sub-problems; and to pre-configure the initial baseline resources. As input parameters, for each risk scenario Construct subproblems that focus on the optimal decision for the second stage given the decision made in the first stage, i.e., finding the optimal amount of emergency resources to allocate in this scenario. and corresponding scenario costs The objective function for each subproblem is: ; st ; ; or ; in, It is the set of non-negative real numbers, determined based on resource characteristics. The value type; solve the subproblems of all scenarios to obtain the optimal scheduling amount for each scenario. Precise scenario cost And calculate the true expected cost for all scenarios. ,in, Based on Calculate the expected actual amount of idle space; The fourth step is to generate the optimal cut and update the main problem; compare the objective function values ​​of the main problem. Compared with the actual expected cost The optimal cut is generated based on the solution results of the subproblems. The optimal cut is used to correct the approximate terms of the expected cost in the main problem, making the main problem closer to the real problem. The optimal cut is added to the constraints of the main problem to form the updated main problem. Step 5: Iterative convergence check; repeat steps 2 to 4, solving the updated principal problem in each iteration to obtain a new baseline resource provisioning scheme. Solving the corresponding subproblems yields the new true expected cost. Calculate the objective function value of the main problem. and The difference ;like , To preset the convergence threshold, it is set according to the business accuracy requirements, and is usually taken as 1% to 5% of the total budget. If the iteration is deemed to have converged, it will continue to iterate until the convergence condition is met. Step S2-5: Output and Verification of the Optimal Solution: Once the iteration converges, the solution to the current main problem is output as the globally optimal baseline resource provisioning scheme. Output the solutions to all subproblems as the optimal emergency resource scheduling strategy set corresponding to each risk scenario. Each strategy contains key information such as the type and quantity of emergency resources to be dispatched in the corresponding scenario. The feasibility and optimality of the output optimal solution are verified: Verification Meet the total budget constraint and validate each scenario. and Equal constraints; verify the total expected cost. The goal is to find the global minimum, meaning there are no other combinations of decision variables that can lower the total expected cost. If the verification fails, adjust the convergence threshold or model parameters and iterate again until the optimal solution that meets the requirements is output.

[0022] In this embodiment, step S3 is based on the globally optimal baseline resource pre-configuration scheme and the optimal emergency resource scheduling strategy set output in step S2. It completes the initial resource deployment and establishes a real-time monitoring system, activates the corresponding scheduling strategy by matching risk symptoms, initiates emergency resource allocation, and simultaneously tracks the execution process and calibrates model parameters. This achieves dynamic response to business interruption risks and continuous iteration of optimization decisions. The detailed steps are as follows: Step S3-1: Initial resource deployment and inventory generation: Based on the globally optimal baseline resource pre-configuration scheme determined in step S2, the resource deployment process is initiated at the very first moment of the preset optimization cycle. The system automatically identifies the resource reserve nodes specified in the scheme and accurately configures the corresponding type and quantity of baseline resources to each node according to the characteristics of the nodes, such as geographical distribution, storage capacity, and business coverage. During the configuration process, the system verifies the integrity and adaptability of the resources in real time to ensure that the deployed resources meet the business operation standards and emergency call requirements, and avoids affecting subsequent response efficiency due to incompatible resource types or quantity deviations. Once resource configuration is complete, an initial deployment list is automatically generated. The list includes the unique identifier of each resource reserve node, geographical location information, type, specifications, quantity, storage status, validity period of deployed baseline resources, as well as resource scheduling permissions and call priorities. The initial deployment list serves as the core basis for subsequent resource monitoring and scheduling execution and will be synchronously stored in the system database to ensure that all business processes can query resource deployment status in real time. Step S3-2: Construction of Multidimensional Monitoring System and Data Processing: Based on the initial deployment list, a multi-dimensional monitoring system covering resource reserve nodes, business operation units, and external risk sources is constructed. This system includes three types of monitoring modules: the resource reserve monitoring module is responsible for collecting real-time resource inventory data, resource depletion rate, and storage environment parameters of each node; the business operation monitoring module is responsible for collecting real-time load data, resource consumption rate, and business operation status indicators of each business unit; and the external risk monitoring module is responsible for collecting external environmental data such as meteorological data, traffic operation data, supply chain status data, and policy dynamic data. Each monitoring module continuously collects data according to a preset sampling frequency. The sampling frequency is dynamically adjusted based on the data type, with the sampling interval for resource inventory and business load data not exceeding 5 minutes, and the sampling interval for external environment data not exceeding 30 minutes. After collection, the system performs multi-source data fusion processing, sequentially executing data cleaning, data standardization, and feature extraction operations: removing outliers and missing values ​​from the data, converting data of different dimensions to a unified standard, extracting key features related to risk occurrence and resource demand, and finally generating a real-time risk symptom feature vector containing timestamps. ,in, This refers to the moment of data acquisition. Step S3-3: Risk scenario matching and scheduling strategy locking: The system will generate real-time risk symptom feature vectors The conditions for triggering multiple discrete risk scenarios generated in step S1 are synchronously compared, and the matching degree between the two is calculated using a cosine similarity algorithm. The calculation formula is: ,in, For the first Preset trigger condition feature vectors for each risk scenario The dot product of two vectors. The magnitude of the feature vector of real-time risk symptoms. For the first The magnitude of the feature vector of each scene trigger condition. The value ranges from 0 to 1, with a larger value indicating a higher degree of matching. Preset matching trigger threshold , The value is set according to the business's requirements for risk response sensitivity, and is usually between 0.8 and 0.9; when the matching degree Sim for a certain risk scenario is... When the system determines that the risk scenario has been activated, it immediately locks the optimal emergency resource scheduling strategy corresponding to the scenario in step S2, prohibits the strategy from being modified or replaced, and ensures the consistency and timeliness of the scheduling instructions. If the matching degree of multiple risk scenarios exceeds the threshold at the same time, the system sorts the scenarios according to their occurrence probability and impact range, and prioritizes activating the scheduling strategy corresponding to the scenario with a high occurrence probability and a wide impact range. Step S3-4: Generation of dispatch instructions and initiation of emergency dispatch: Based on the locked optimal emergency resource scheduling strategy, the system automatically generates detailed emergency scheduling instructions. The instructions include the type of emergency resource, the specific quantity to be scheduled, the supply path (i.e., the optimal transportation route from the resource reserve node or emergency supply channel to the affected business unit), and the time window (i.e., the latest time the resource needs to arrive and the phased arrival nodes). The planning of the supply path aims to minimize transportation time and cost, and comprehensively considers factors such as road conditions, transportation capacity, and resource preservation or quality requirements. The time window is set based on the duration of the risk scenario and the critical time for business recovery, ensuring that the resources play a role within the effective time. After the dispatch instruction is generated, the system automatically issues the instruction to the corresponding resource reserve node management system and emergency supply channel dispatch platform through the preset interface to start the emergency resource transportation process. During the transportation process, the system pushes the instruction status to the relevant execution nodes in real time, including instruction receipt confirmation, resource outbound status, on-the-way location, estimated arrival time and other information, to realize the visual tracking of the transportation process. Step S3-5: Perform state tracking and model parameter calibration: During the execution of the emergency resource allocation process, the system continuously tracks two core indicators: resource availability and business recovery status. Resource availability is assessed by collecting data on the actual quantity, arrival time, and quality of resources received, and the resource availability rate is calculated. The calculation formula is: ,in, To ensure resource availability, This refers to the actual amount of emergency resources in place. The number of emergency resources required by the dispatching instructions; the service recovery status is assessed by collecting indicators such as the operating load, processing efficiency, and service quality of the affected service units to determine whether the service has recovered to the normal operating level before the interruption; Based on the execution feedback data collected through tracking and the actual resource consumption costs, the unit scheduling cost in the stochastic programming model constructed in step S2 is analyzed. Unit shortage penalty cost Perform calibration; the calibration formula is: ; ; in, The unit scheduling cost after calibration. The actual total cost of emergency resource allocation. The model predicts the total cost of emergency resource allocation. The unit shortage penalty cost after calibration, The total cost of actual business interruption losses. The total cost of business interruption loss predicted by the model; After calibration, the system will update the parameters. and Step S2, which feeds back to subsequent optimization cycles, is used for updating the stochastic programming model, enabling the model to better fit the actual business scenario and continuously improve the accuracy and effectiveness of optimization decisions.

[0023] A resource provisioning and cost optimization system for potential business interruption events. The system is used to execute resource provisioning and cost optimization methods for potential business interruption events.

[0024] This invention relates to a resource pre-provisioning and cost optimization method and system for potential business interruption events. This system is an integrated technology platform supporting the entire process of implementing the aforementioned resource pre-provisioning and cost optimization method. Based on core technologies such as multi-source data fusion, stochastic programming optimization, and real-time monitoring and response, it achieves fully automated processing from risk identification and decision modeling to dynamic execution. The core objective is to minimize the total expected cost within a preset optimization period while ensuring business continuity in the face of potential interruption risks. The system adopts a modular architecture design, with each module functioning independently yet collaboratively. Specifically, it includes a data acquisition and preprocessing module, a risk quantification and scenario generation module, a two-stage optimization decision module, a dynamic response execution module, a data storage and management module, and an interaction and visualization module. The detailed functions and operating mechanisms of each module are as follows: Data acquisition and preprocessing module: This module is the basic data support unit for the system operation. It is responsible for the collection, integration and standardization of all data, providing high-quality data input for subsequent risk quantification and model building. The module has built-in multi-source data interfaces, compatible with the access of three core data types: historical business interruption event data, real-time business operation status data, and external environment monitoring data. Historical business interruption event data is obtained by connecting to the business management system database, covering multi-dimensional information such as event occurrence time, impact scope, resource supply gap scale, recovery time, and triggering factor type. Real-time business operation status data is collected in real time by sensors and system probes deployed at business units and resource reserve nodes, including current business load, real-time resource consumption rate, resource inventory level, and business unit operation status. External environment monitoring data is obtained by connecting to third-party interfaces such as meteorological service platforms, traffic operation monitoring systems, supply chain management platforms, and policy release channels, including meteorological data, traffic operation data, industry policy dynamics, upstream and downstream supply chain operation status, and regional infrastructure operation status. After data acquisition, the module automatically executes a preprocessing procedure: First, an outlier detection algorithm is used to remove extreme outliers from the data, and interpolation is used to supplement missing data to ensure data integrity. Then, a data standardization algorithm is used to convert data of different dimensions and formats into structured data of a unified standard, eliminating the impact of dimension differences on subsequent analysis. Finally, multi-source data fusion technology is used to integrate the processed data into a unified dataset and perform data validity verification to verify the consistency of timestamps between real-time and historical data, the completeness of data fields, and the rationality of data logic. If data anomalies are found, an alarm is triggered and a re-acquisition process is started to ensure that the output dataset meets the processing requirements of subsequent modules. Risk Quantification and Scenario Generation Module: This module takes over the output of the data acquisition and preprocessing module. Its core function is to identify potential resource interruption risk points, generate discrete risk scenarios, and quantify emergency resource demand, which corresponds to step S1 in the method. Risk point identification unit: The unit performs multi-dimensional feature analysis on preprocessed historical business interruption event data, extracts risk-inducing factors directly related to resource supply through feature engineering algorithms, such as resource supplier capacity fluctuations, transportation link interruptions, external policy adjustments, and equipment failure frequency; then, combined with real-time business operation status data and external environment monitoring data, it uses correlation analysis algorithms in multi-source data fusion technology to identify a set of potential risk points that may lead to resource supply interruptions within the optimization cycle, and assigns a unique identifier and attribute label to each risk point, including the type of risk-inducing factor, the initial scope of impact, and the potential occurrence period; Risk scenario generation unit: For each identified risk point, the unit uses a scenario-building method to simulate and generate multiple risk evolution paths, combining three core indicators: risk intensity, impact scope, and duration. Risk intensity is calculated using a formula: ; Based on the risk intensity calculation results and the characteristics of the triggering factors of the risk point, three differentiated evolution paths of high, medium and low are constructed. Each evolution path is then matched with a preset probability threshold range to obtain multiple discrete risk scenarios corresponding to different probability levels of occurrence. Each scenario clearly includes key information such as triggering conditions, impact start time, and duration. After the scenario is generated, the unit automatically verifies the rationality of the scenario, verifies whether the probability distribution between different scenarios generated from the same risk point conforms to statistical laws, and whether the time parameters of the scenario are within the optimization period, to ensure the scientificity and practicality of the scenario. Unit for quantifying resource demand: For each discrete risk scenario, the unit extracts parameters such as triggering conditions, impact start time, and duration, and calls historical business load data and resource consumption pattern data from the same period to build a resource demand prediction model. The model uses a time series prediction algorithm combined with business correlation analysis to learn the correspondence between business load and resource consumption in historical data, and simulates the baseline of resource demand of the affected business units at each moment during the duration when the scenario occurs. Based on this, by overlaying the business volatility coefficient and the resource substitution elasticity coefficient, the emergency resource demand and total demand at each moment are calculated using the following formula: ; ; After the calculation is completed, the unit outputs complete data containing the total demand for each scenario and the demand for each time period, and verifies the rationality of the data to ensure that the data meets the input requirements of the subsequent optimization decision module. Two-stage optimization decision-making module: This module is the core decision-making unit of the system, corresponding to step S2 in the method. It is responsible for constructing and solving a two-stage stochastic programming model, and outputting the globally optimal baseline resource pre-positioning scheme and the optimal emergency resource scheduling strategy set corresponding to each risk scenario. Model building unit: The unit first defines the two-stage decision variables: the first-stage decision variable is the baseline resource provision. The first variable is the amount of basic resource reserves deployed before the start of the optimization cycle, and its value is a non-negative integer; the second-stage decision variable is the amount of emergency resource allocation. ,in, Representing the Individual risk scenarios. , , , , This represents the total number of risk scenarios, and its value is a non-negative integer or a non-negative consecutive number depending on the resource characteristics. Subsequently, an objective function is constructed to minimize the total expected cost, which includes four categories: baseline resource provisioning cost, provisioning resource idle penalty cost, expected emergency resource incremental cost, and expected business interruption loss cost. The objective function expression is as follows: ; Meanwhile, five types of constraints are set for each element to ensure the feasibility of the model solution: Resource supply and demand balance constraints: For all ; Total budget constraint for resource acquisition: in Set the preset total budget limit for resource acquisition; Non-negativity constraint on emergency dispatch resources: For all ; Emergency dispatch triggering constraints: For all ;in For the first The occurrence status indicator variable of each scenario, Indicates that the scene has occurred. This indicates that the scenario did not occur; Constraints on the availability of emergency resources: For all in For the first The maximum amount of resources available from emergency channels when a scenario is triggered; Model Solving Unit: The unit transforms the constructed stochastic programming model into a two-stage stochastic integer programming problem and solves it using a scenario decomposition-based algorithm. First, the original problem is decomposed into a deterministic main problem and multiple scenario subproblems. The main problem includes the first-stage decision variables and expected cost approximations, with constraints of budget constraints and variable value constraints. Each subproblem targets a risk scenario and focuses on solving the optimal second-stage decision given the first-stage decision. The solution process proceeds iteratively: the first step is to solve the initial master problem to obtain the initial baseline resource provisioning scheme. and the initial value of the objective function of the main problem The second step will Using these as input parameters, we solve all scenario subproblems to obtain the optimal emergency resource allocation for each scenario. Precise scenario cost and calculate the actual expected cost. Third step comparison and Generate an optimal cut based on the results of the subproblems, and add it to the constraints of the main problem to update the main problem; repeat the above steps until the difference between the objective function value of the main problem and the actual expected cost is reached. , The preset convergence threshold is typically set to 1% to 5% of the total budget to determine if the iteration has converged. After convergence, the unit outputs the globally optimal baseline resource pre-configuration scheme. and the optimal emergency resource scheduling strategy set corresponding to each risk scenario. And perform solution verification to ensure that the solution meets all constraints and the total expected cost is optimal; Dynamic response execution module: This module is the system's implementation unit, corresponding to step S3 in the method. It is responsible for transforming the optimization decision results into actual operations, realizing the dynamic execution of the entire process of resource deployment, risk monitoring, emergency dispatch, and parameter calibration. Initial resource deployment unit: The unit is based on the optimal baseline resource pre-configuration scheme. At the start of the optimization cycle, the resource deployment process is initiated; by connecting to the resource reserve node management system, the characteristics of each node, such as storage capacity, geographical distribution, and business coverage, are automatically identified, and resource configuration instructions are accurately issued to each node, specifying requirements such as resource type, configuration quantity, and storage location; during the configuration process, the integrity and adaptability of resources are verified in real time to ensure that the deployed resources meet business standards and emergency call requirements; After resource configuration is completed, an initial deployment list is automatically generated. The list includes information such as the unique identifier of each resource reserve node, geographical location, type, specifications, quantity, storage status, validity period, and scheduling permissions of the deployed resources. It is also stored in the system database for subsequent modules to query and call. Real-time monitoring unit: Based on the initial deployment list, a multi-dimensional monitoring system covering resource reserve nodes, business operation units, and external risk sources is constructed. The system includes three types of monitoring sub-modules: the resource reserve monitoring sub-module collects resource inventory data, resource consumption rate, and storage environment parameters of each node in real time; the business operation monitoring sub-module collects load data, resource consumption rate, and operation status indicators of each business unit in real time; and the external risk monitoring sub-module collects external environmental data such as meteorology, transportation, supply chain, and policies in real time. Each submodule collects data at a dynamically adjusted sampling frequency, with a sampling interval of no more than 5 minutes for resource and business-related data and no more than 30 minutes for external environment data. After collection, the data is fused and processed to generate a real-time risk symptom feature vector containing timestamps. ,in, Provide data support for data collection and risk scenario matching; Risk matching and scheduling activation unit: The unit will use real-time risk symptom feature vectors Feature vectors of preset triggering conditions for each discrete risk scenario Simultaneous comparison is performed, and the matching degree is calculated using the cosine similarity algorithm. : Preset matching trigger threshold The value is typically between 0.8 and 0.9. When the Sim value for a given scenario... When a scenario is activated, the corresponding optimal emergency resource scheduling strategy is immediately locked. If multiple scenarios meet the matching conditions at the same time, they are sorted by the probability of occurrence and the scope of impact, and the scenario strategy with the higher priority is activated first. Once the strategy is locked, detailed dispatch instructions are automatically generated, including information such as emergency resource type, dispatch quantity, supply route, and time window. The supply route is planned with the goal of minimizing transportation time and cost, and the time window is set based on the duration of the risk scenario and the critical time for business recovery. The instructions are sent to the resource reserve node management system and the emergency supply channel dispatch platform through preset interfaces to initiate the emergency resource transportation process and track information such as instruction reception status, resource outbound status, on-the-way location, and estimated arrival time in real time. Status tracking and parameter calibration unit: During emergency resource allocation, the arrival of resources and the status of business recovery are continuously tracked; resource arrival rate is calculated by collecting data on the quantity, timing, and quality of resources actually arriving. : The service recovery status is assessed by collecting metrics such as the operating load, processing efficiency, and service quality of the service units; based on execution feedback data and actual resource consumption costs, the unit scheduling cost in the stochastic programming model is evaluated. Unit shortage penalty cost Perform calibration: ; ; The calibrated parameters are fed back to the two-stage optimization decision module in subsequent optimization cycles for model iterative updates; Data storage and management module: This module is responsible for the storage, management and maintenance of data throughout the entire system process, providing data access support for various functional modules, and ensuring the security, integrity and traceability of the data; The module adopts a distributed database architecture, dividing the data into multiple data storage partitions: the historical data partition stores historical business interruption event data, historical concurrent business load data, historical resource consumption pattern data, etc.; the real-time data partition stores collected real-time business operation data, external environment monitoring data, and real-time risk symptom feature vectors. The model data is partitioned to store parameters, constraints, and iterative process data of the stochastic programming model; the decision data is partitioned to store baseline resource pre-configuration schemes. Emergency resource dispatch strategy set The execution data partition stores the initial deployment list, scheduling instructions, execution feedback data, and calibrated parameters. and wait; The module has a built-in data access control mechanism that assigns differentiated access permissions to different users and functional modules to ensure data security; it also has data backup and recovery functions, automatically backing up critical data on a regular basis and supporting data recovery in case of anomalies; it also provides data lifecycle management functions, cleaning up expired and invalid data according to preset rules and optimizing storage resource usage; Interaction and Visualization Module: This module provides users with a human-computer interaction interface to realize functions such as system operation, data viewing, and result display, thereby improving the ease of use and operability of the system; The module provides a visual user interface, allowing users to configure system parameters such as optimization cycle and budget limit. Convergence threshold Matching trigger threshold Wait, start or pause system processes, and check the running status of each module; display key information in a graphical way: visualize the probability of occurrence of each risk scenario. The impact scope and duration; resource deployment visualization showing the resource configuration and resource inventory changes of each node; optimization results visualization showing the total expected cost. Composition and baseline resource pre-configuration scheme Scheduling strategies for various scenarios The execution process is visualized, showing the trajectory of emergency resource deployment, the progress of resource arrival, and the status of business recovery. Meanwhile, the module supports generating detailed system operation reports, including data collection status, risk quantification results, optimization decision-making process, execution feedback data, parameter calibration results, etc. These reports can be exported for users to archive and analyze, and also support users to make manual interventions and adjustments based on the reports, thereby improving the system's flexibility.

[0025] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for resource provisioning and cost optimization in response to potential business interruption events, characterized by: Includes the following steps: Step S1, Resource Demand Risk Quantification: Based on historical and real-time data, identify potential resource interruption risk points, and simulate and generate multiple discrete risk scenarios with different probabilities of occurrence within a preset optimization period for each risk point. For each risk scenario, quantify the amount of emergency resources required when it occurs; Step S2, Two-stage stochastic optimization decision: Establish a stochastic programming model with the objective of minimizing the total expected cost within a preset optimization period; wherein, the total expected cost includes the baseline resource pre-set cost and the pre-set resource idle penalty cost corresponding to the first stage decision, and the business interruption loss cost caused by resource shortage in each risk scenario and the incremental cost generated by triggering emergency resource scheduling corresponding to the second stage decision; The constraints of the stochastic programming model include: resource supply and demand balance constraints under each risk scenario, and total budget constraints for resource acquisition; The model is solved using a stochastic programming algorithm, which outputs a globally optimal baseline resource pre-configuration scheme and a set of optimal emergency resource scheduling strategies corresponding to multiple discrete risk scenarios. Step S3, Dynamic Response Execution: Perform initial resource deployment according to the baseline resource pre-configuration plan; monitor risk signs in real time, and when the monitoring information matches any simulated risk scenario, automatically trigger and execute the optimal emergency resource scheduling strategy corresponding to that risk scenario.

2. The resource provisioning and cost optimization method for potential business interruption events according to claim 1, characterized in that: Based on historical and real-time data, potential resource interruption risk points are identified, and for each risk point, multiple discrete risk scenarios with different probabilities of occurrence within a preset future optimization period are simulated and generated; including: Perform multi-dimensional feature analysis on historical business interruption event data to extract risk triggering factors associated with resource supply; combine real-time business operation status data and external environment monitoring data, and use multi-source data fusion technology to identify a set of potential risk points that may lead to resource supply interruption during the optimization cycle. For each identified risk point, a scenario building method is used to generate multiple possible risk evolution paths based on its risk intensity, scope of impact and duration. Each evolution path is matched with a preset probability threshold range to obtain multiple discrete risk scenarios corresponding to different probability levels of occurrence. Each risk scenario includes specific triggering conditions, impact start time and duration.

3. The resource provisioning and cost optimization method for potential business interruption events according to claim 2, characterized in that: For each risk scenario, quantify the emergency resource requirements it would trigger upon occurrence; including: Based on the triggering conditions, impact start time and duration defined for each risk scenario, and combined with historical business load data and resource consumption patterns, a resource demand prediction model is constructed. The resource demand forecasting model is used to simulate the baseline resource demand of the affected business units at each moment during the duration of the scenario when the risk scenario occurs. The business fluctuation coefficient and resource substitution elasticity coefficient caused by the interruption event are further superimposed to calculate the total demand and time-period demand of various emergency resources required to maintain continuous business operation.

4. The resource provisioning and cost optimization method for potential business interruption events according to claim 1, characterized in that: Establish a stochastic programming model with the objective of minimizing the total expected cost within a preset optimization period, including: The first-stage decision is defined as the baseline resource provision that needs to be determined at the beginning of the optimization cycle and before the occurrence of risk scenarios; The second-stage decision is defined as the amount of emergency resources that need to be allocated when any risk scenario actually occurs within the optimization cycle. A target function is constructed using the baseline resource pre-configuration amount as the first variable and the emergency resource scheduling amount under each risk scenario as the second variable. The objective function is to minimize the total expected cost consisting of the following components: Baseline resource pre-configuration cost determined based on baseline resource pre-configuration quantity and unit pre-configuration cost; The pre-set resource idle penalty cost is determined based on the baseline resource pre-set amount that may be idle and the unit idle penalty cost; Based on the probability of occurrence of each risk scenario, the amount of emergency resources required for that risk scenario and the unit scheduling cost that is higher than the baseline preset cost, the expected incremental cost of emergency resources for all risk scenarios is determined. Based on the probability of occurrence of each risk scenario, the amount of emergency resource shortage that cannot be met under that risk scenario, and the unit shortage penalty cost, the expected business interruption loss cost under all risk scenarios is determined.

5. The resource provisioning and cost optimization method for potential business interruption events according to claim 4, characterized in that: The constraints of the stochastic programming model specifically include: For each risk scenario, a resource supply and demand balance constraint is defined. This constraint requires that, under the risk scenario, the sum of the baseline resource pre-positioned quantity and the corresponding emergency resource scheduling quantity should not be less than the emergency resource demand caused by the risk scenario. Define a total budget constraint for resource acquisition, which requires that the baseline resource provisioning cost must not exceed a preset budget limit; Define a non-negativity constraint for emergency dispatch resources; It also includes emergency dispatch trigger constraints: for any risk scenario, the corresponding emergency resource dispatch quantity can only be greater than zero when the scenario is determined to have occurred; otherwise, the dispatch quantity is zero. In addition, there are constraints on the availability of emergency resources: for any risk scenario, the corresponding emergency resource allocation shall not exceed the maximum resource supply that can be obtained from emergency channels in advance when the scenario is triggered.

6. The resource provisioning and cost optimization method for potential business interruption events according to claim 5, characterized in that: The model is solved using a stochastic programming algorithm, specifically including: The stochastic programming model is constructed as a two-stage stochastic integer programming problem, where the decision variables in the first stage are integers, and the decision variables in the second stage are integers or continuous variables depending on the resource characteristics. The solution is obtained using a scene decomposition-based algorithm, which specifically includes: The original problem is decomposed into a deterministic main problem and multiple sub-problems corresponding to different risk scenarios. The main problem includes the first-stage decision variables and the expected cost approximation terms related to all scenarios. Each sub-problem is for a specific risk scenario and is used to evaluate the optimal second-stage decision and corresponding cost in that scenario given the first-stage decision scheme. Approximation is achieved by iteratively solving the main problem and sub-problems: First, the main problem is solved to obtain an initial baseline resource pre-configuration scheme; then, this scheme is used as input parameters to solve the sub-problems corresponding to all risk scenarios, obtaining the optimal scheduling strategy and accurate scenario cost for each scenario; then, the optimality cut is generated based on the sub-problem solution results, and this cut is added to the constraints of the main problem to correct the approximation of the expected cost in the main problem; Repeat the above iterative process until the difference between the objective function value of the main problem and the actual expected cost obtained from the subproblems is less than the preset convergence threshold. At this point, output the current solution to the main problem as the globally optimal baseline resource provisioning scheme, and output the solutions to all subproblems as the optimal emergency resource scheduling strategy set.

7. The resource provisioning and cost optimization method for potential business interruption events according to claim 1, characterized in that: Step S3 specifically includes: Based on the globally optimal baseline resource provisioning scheme, at the beginning of the optimization cycle, the corresponding type and quantity of baseline resources are configured to the designated resource reserve nodes to complete the initial resource deployment and generate an initial deployment list containing resource locations and statuses. Based on the initial deployment list, a multi-dimensional monitoring system covering all resource reserve nodes, business operation units and external risk sources is established to collect resource inventory data, business load data and external environment data in real time; the collected data is then fused and processed using the multi-dimensional monitoring system to generate real-time risk symptom feature vectors containing timestamps. The real-time risk symptom feature vector is synchronously compared and similarity is calculated with the preset trigger conditions of each risk scenario in multiple discrete risk scenarios; when the matching degree of a certain risk scenario exceeds the preset trigger threshold, the risk scenario is determined to be activated, and the corresponding optimal emergency resource scheduling strategy is locked. Based on the locked optimal emergency resource dispatch strategy, generate detailed dispatch instructions including emergency resource type, dispatch quantity, supply path and time window; automatically issue dispatch instructions to resource reserve nodes and emergency supply channels to initiate the emergency resource transportation process; During the execution of the emergency resource allocation process, the availability of resources and the status of business recovery are continuously tracked to generate execution feedback data. Based on the execution feedback data and the actual resource consumption cost, the unit scheduling cost and unit shortage penalty cost parameters in the stochastic programming model are calibrated, and the calibrated parameters are fed back to step S2 in the subsequent optimization cycle for iterative updates of the model.

8. The resource provisioning and cost optimization system for potential business interruption events according to claim 1, characterized in that: The system is used to perform the method according to any one of claims 1-7, the system comprising: The data acquisition and preprocessing module is used to collect and integrate historical business interruption event data, real-time business operation status data, and external environment monitoring data to generate structured datasets. The risk quantification and scenario generation module, connected to the data acquisition and preprocessing module, is used to identify potential resource interruption risk points based on structured datasets, generate multiple discrete risk scenarios with different probabilities of occurrence for each risk point, and quantify the emergency resource requirements for each risk scenario. The two-stage optimization decision module, connected to the risk quantification and scenario generation module, is used to construct a two-stage stochastic programming model with the goal of minimizing the total expected cost. It is solved using a scenario decomposition-based algorithm and outputs the globally optimal baseline resource pre-positioning scheme and the optimal emergency resource scheduling strategy set corresponding to each risk scenario. The dynamic response execution module, connected to the two-stage optimization decision module, is used to execute the initial resource deployment based on the baseline resource pre-configuration plan, establish a multi-dimensional monitoring system to collect risk signs in real time, and automatically trigger and execute the corresponding optimal emergency resource scheduling strategy when the monitoring information matches any simulated risk scenario. The data storage and management module is connected to the data acquisition and preprocessing module, the risk quantification and scenario generation module, the two-stage optimization decision-making module, and the dynamic response execution module, respectively, and is used to store and provide the data required by each link of the system. The interaction and visualization module, connected to the data storage and management module and the aforementioned functional modules, provides a parameter configuration and process control interface, and visualizes risk scenarios, resource deployment, optimization results, and execution processes.

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