Stopping loss plan drill and evaluation method and device, electronic equipment and storage medium

Abstract

The invention provides a stop loss plan drill and evaluation method and device, electronic equipment and a storage medium, and belongs to the technical field of operation and maintenance. The method comprises the following steps: constructing a simulation environment for a target loss stop plan; the target loss stop plan is executed in the simulation environment, reference index data are collected in the execution process, and the reference index data comprise system index data, service index data and execution process data; executing an association operation on the reference index data, so that each plan step in the execution process is associated with a system index and a service index which are influenced by the plan step; and generating a quantitative evaluation result based on the associated reference index data. According to the invention, the effectiveness of the stop loss plan can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of operation and maintenance, and in particular to a loss-prevention plan drilling and evaluation method and device, electronic equipment and storage medium. BACKGROUND

[0002] The loss-prevention plan refers to a series of automatic or manual operations set in advance, which are executed when the information system encounters a serious failure to quickly restore core business, including service degradation, traffic switching, version rollback and other key steps.

[0003] Currently, the verification and management of information system loss-prevention plans mainly rely on the combination of the following two traditional technical forms: one is manual document drilling (also known as "paper tiger" type deduction), that is, the operation and maintenance team manually deduces the logic by comparing the text documents and flowcharts through meetings; the second is manual plan execution during non-core business hours, that is, the operation and maintenance personnel manually execute the commands or operation steps in the plan according to the document guidance in the production environment or the simple test environment. In addition, some enterprises may supplement some isolated and hard-coded automation scripts for executing individual repetitive tasks.

[0004] For the financial industry, system stability and business continuity have extremely close relevance, and any small system failure risk may trigger a chain reaction, which may cause some loss to the business, and even may trigger negative public opinion, leading to difficulties in business operation. Therefore, there is an urgent need for a loss-prevention plan drilling and evaluation method to improve the effectiveness of the loss-prevention plan. SUMMARY

[0005] Therefore, the embodiments of the present application provide a loss-prevention plan drilling and evaluation method, device, electronic equipment and storage medium, which can improve the effectiveness of the loss-prevention plan.

[0006] According to an aspect of the present application, a loss-prevention plan drilling and evaluation method is provided, which comprises: constructing a simulation environment for a target loss-prevention plan; executing the target loss-prevention plan in the simulation environment, and collecting reference index data in the execution process, the reference index data including system index data, business index data and execution process data; performing correlation operations on the reference index data to associate each plan step in the execution process with the system index and business index affected thereby; generating a quantitative evaluation result based on the correlated reference index data.

[0007] According to another aspect of the present application, a loss-prevention plan drilling and evaluation device is provided, which comprises: An environment construction unit is configured to construct a simulation environment for a target loss stop plan; A plan execution unit is configured to execute the target loss stop plan in the simulation environment and collect reference index data during the execution, the reference index data including system index data, business index data and execution process data; A drill evaluation unit is configured to perform association operations on the reference index data to associate each plan step in the execution process with the system index and business index affected thereby, and generate a quantitative evaluation result based on the associated reference index data.

[0008] According to another aspect of the present application, an electronic device is provided, comprising: a processor; and a memory storing a program, wherein the program includes instructions that, when executed by the processor, cause the processor to perform the drill and evaluation method of the loss stop plan.

[0009] According to another aspect of the present application, a non-transitory computer readable storage medium storing computer instructions is provided, wherein the computer instructions are configured to cause a computer to perform the drill and evaluation method of the loss stop plan.

[0010] In the present application, the target loss stop plan is executed in a simulation environment, and reference index data is collected during the execution, the reference index data including system index data, business index data and execution process data. Association operations are performed on the reference index data to associate each plan step in the execution process with the system index and business index affected thereby, and a quantitative evaluation result is generated based on the associated reference index data. The quantitative evaluation result can be used to evaluate the actual execution effect of the target loss stop plan to guide the operation and maintenance personnel to accurately improve the plan, thereby forming a complete continuous improvement closed loop of “drill-evaluation-optimization-re-drill”, ensuring that the loss stop plan can continuously evolve with the system changes, and effectively improving the business resilience. BRIEF DESCRIPTION OF DRAWINGS

[0011] More details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A flowchart of a drill and evaluation method of a loss stop plan according to an exemplary embodiment of the present application is shown; Figure 2 A flowchart of a simulation environment construction according to an exemplary embodiment of the present application is shown; Figure 3 A flowchart of a plan execution in a simulation environment according to an exemplary embodiment of the present application is shown; Figure 4 A rehearsal evaluation flowchart is shown according to an exemplary embodiment of the present application; Figure 5 A schematic block diagram of a loss-prevention plan rehearsal and evaluation apparatus is shown according to an exemplary embodiment of the present application; Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present application is shown. DETAILED DESCRIPTION

[0012] Embodiments of the present application will be described in more detail with reference to the drawings. While several embodiments of the present application are illustrated, it is understood that the present application can be embodied in various forms and should not be interpreted in limited to the embodiments set forth herein, but rather these embodiments are provided for thorough and complete comprehension of the present application. It is understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of the present application.

[0013] It should be understood that each of the steps in the method embodiments of the present application can be performed in a different order and / or in parallel. Additionally, the method embodiments can include additional steps and / or omit performing the steps shown. The scope of the present application is not limited in this regard.

[0014] The term "comprising" and variations thereof as used herein are used inclusively, i.e., "comprising but not limited to." The term "based on" is "based at least in part on." The term "one embodiment" means "at least one embodiment." The term "another embodiment" means "at least one additional embodiment." The term "some embodiments" means "at least some embodiments." Related definitions are given throughout the description below. It should be noted that reference to a "first" "second" or the like in the present disclosure indicates that different apparatuses, modules or units are being referred to, and not that the functions performed by these apparatuses, modules or units are functionally dependent or independent.

[0015] It should be noted that the terms "one" and "a" or "multiple" as used herein are illustrative and not limiting, and it should be understood that, unless the context clearly indicates otherwise, "one" or "a" or "multiple" is to be interpreted as "one or more."

[0016] The names of the messages or information exchanged between the various apparatuses in the embodiments of the present application are used for illustrative purposes only and are not intended to limit the scope of the messages or information.

[0017] The application provides a loss-prevention plan rehearsal and evaluation method, which can be completed by a terminal, a server and / or other devices with processing capability. The method provided by the embodiment of the application can be completed by any of the above devices or by multiple devices together, and the application does not limit this.

[0018] The loss-prevention plan rehearsal and evaluation method will be described below with reference to Figure 1 A flowchart of a loss-prevention plan rehearsal and evaluation method is shown, and the method will be introduced. The method includes the following steps 101-104.

[0019] Step 101: A simulation environment is constructed for a target loss-prevention plan.

[0020] The module corresponding to step 101 is referred to as a simulation environment construction and management module in this embodiment. The simulation environment construction and management module is the technical cornerstone of the application, and its core purpose is to solve the fundamental problem of the lack of authenticity, high construction cost and operation risk of the traditional rehearsal environment. Through innovative resource tagging management and flow control technology, the module can quickly and automatically construct a logically isolated, data-lossless high-fidelity rehearsal environment on the premise of sharing the underlying infrastructure with the production environment, providing a safe and realistic test field for the subsequent automatic execution of the plan.

[0021] In a possible implementation, before the rehearsal of the target loss-prevention plan is performed, the services and resources involved in the target loss-prevention plan can be identified, and corresponding simulation environments are constructed for these services and resources to ensure that the system behavior in the rehearsal process is as consistent as possible with the real production environment.

[0022] In order to improve the authenticity of the simulation environment, reduce the construction cost and operation risk, a logically isolated, data-lossless high-fidelity rehearsal environment can be constructed on the basis of sharing the underlying infrastructure with the production environment. The corresponding processing can include: Whenever the rehearsal task of the target loss-prevention plan is triggered, the resource objects to be used by the target loss-prevention plan are determined, and the target rehearsal identifier is added to the resource objects; Rehearsal flow data is constructed, and the target rehearsal identifier is added to the rehearsal flow data, wherein the rehearsal flow data includes replicated flow data based on real-time flow data and / or simulated flow data based on historical flow data; Based on the resource objects and the rehearsal flow data, a simulation environment is constructed for the target loss-prevention plan.

[0023] In a possible implementation, the simulation environment is constructed for the target loss-prevention plan by referring to Figure 2The simulation environment construction flowchart is shown. After receiving the drill task of the target stop-loss plan, the system can automatically analyze the plan, accurately identify the required application services, server instances, databases, network components and other resource objects. Then, the system will dynamically label these resource objects with a unique drill identification tag (such as drill-id: xxxx). This tag is like a "special pass" for drill resources, so that all subsequent operations can accurately hit these marked resources, thereby achieving logical isolation from production resources, without the need for repeated physical resource construction, greatly saving costs and improving environment preparation efficiency.

[0024] In constructing drill traffic data, the system can replicate the real traffic of the production environment with low delay, thereby obtaining replicated traffic data based on real-time traffic data; and / or, when there is no real traffic, intelligently simulate traffic based on historical traffic data, thereby obtaining simulated traffic data based on historical traffic data. The key is that whether the traffic is replicated or simulated, it will be implanted with a specific identification consistent with the drill identification of the above resource objects before being sent out (i.e. "traffic dyeing"). All dyed drill traffic will only be routed to drill service instances with the drill identification, and all data read and write operations produced by these instances will be automatically redirected to a separate "shadow storage" system, thereby completely eliminating any pollution and damage to real data in the production environment by drill operations, and achieving truly safe and worry-free drilling.

[0025] Among them, traffic dyeing refers to implanting specific identification information (i.e. target drill identification) in the request data packets flowing into the system. The system routes and controls the processing path and data flow of the request according to this identification, to achieve the differentiation and isolation of drill traffic and production traffic. Shadow storage refers to a data storage system independently configured for drill purposes. All data read and write operations generated by requests with the target drill identification are directed to this system, thereby achieving physical or logical isolation from production storage. Through traffic dyeing and shadow storage technology, it can be ensured that drilling is lossless to the real production environment.

[0026] At the same time, the simulation environment construction and management module realizes the centralized arrangement and life cycle management of the entire simulation environment. Users do not need to pay attention to the complex resource scheduling and network configuration details at the bottom layer. Through a unified control interface, the drill environment can be started and stopped with one key. After the simulation environment is initialized, the module will automatically verify the readiness of the environment, including network connectivity, service health, and whether the data link is correctly directed to the shadow storage, to ensure that the drill can be carried out in a highly simulated and reliable environment. After the drill is completed, the module can automatically clean up the environment, release the label and recycle the resources, to ensure efficient use of infrastructure.

[0027] Step 102, executing the target stop-loss plan in the simulation environment, and collecting reference index data in the execution process, the reference index data including system index data, business index data and execution process data.

[0028] The module corresponding to step 102 in the embodiment is called a plan intelligent arrangement and execution module. The plan intelligent arrangement and execution module is the core control hub of the application, and its original intention is to completely change the traditional inefficient and high-risk exercise mode that relies on manual interpretation of documents and manual execution. The module converts static text plans into dynamic intelligent workflows that can be directly parsed and driven by machines, and gives them state perception and autonomous decision-making capabilities, thereby achieving high automation, precision and reliability of the plan execution process, and ensuring that each exercise is as efficient and orderly as real fault response.

[0029] Based on this, before executing the target stop-loss plan in the simulation environment, the following processing can also be included: Obtaining a graphical process of the target stop-loss plan edited based on a graphical component; Compiling the graphical process of the target stop-loss plan into a structured standard file.

[0030] The standard file can include plan steps of the target stop-loss plan.

[0031] In one possible implementation, when editing the target stop-loss plan, the user defines the operation type, execution condition, timeout strategy and exception handling mechanism of each step by dragging graphical components (such as start, execute command, judge, wait, rollback, etc.) in the visual arrangement interface, and real-time previews the process logic and dependency relationship of the plan, such as serial steps, parallel tasks, conditional branches and loop judgments. Further, the system can automatically compile the graphical process into a structured standard file, such as an executable workflow definition file in YAML or JSON format, so that the system can parse and execute each step instruction in the plan, ensuring that each node flows according to the preset logic.

[0032] In the plan intelligent arrangement and execution module, a visual arrangement interface is provided, realizing the upgrade of the plan from "documented" to "coded" and then to "serviced". The operation and maintenance personnel do not need to write complex scripts, but can flexibly define the complete plan process logic by simply dragging graphical components. This not only makes the plan clear, easy to understand, maintain and version management, but also makes it a standardized "service asset" that can be directly scheduled by the system, laying a solid foundation for automated execution.

[0033] Thereafter, the target stop-loss plan can be executed in the simulation environment through the following processing: When executing each step of the target loss mitigation plan in the simulation environment, determine the plan operation indicator data corresponding to the current plan step. The plan operation indicator data includes any one or more of the following: service port status, interface response time, and business error rate. Compare the operational indicator data of the contingency plan with the corresponding expected operational indicator data in the target stop-loss plan; If the operational indicators of the contingency plan meet the expected operational indicators, then the target stop-loss contingency plan will continue to be implemented. If the operational indicators of the contingency plan do not meet the expected operational indicators, the pre-set error response strategy for the exercise will be triggered. The process continues until each step of the target stop-loss plan is completed, or until the target stop-loss plan fails to be executed.

[0034] The error response strategies practiced may include rollback operations, alarm operations, or jumping to different process branches.

[0035] In one possible implementation, refer to Figure 3 The flowchart of the contingency plan execution in the simulation environment is shown. The contingency plan intelligent orchestration and execution module can have an adaptive execution engine built in. This engine can automatically trigger the detection and verification of the contingency plan operation index data before and after executing each contingency plan step, and compare the results with the expected state (i.e. expected operation index data) defined in the contingency plan.

[0036] If the actual operational indicators of the contingency plan match the expectations, it can be determined whether this is the final step of the plan. If the current step is the final step, the contingency plan is considered successfully executed and the process ends. If it is not the final step, the next step is automatically scheduled, and the above-mentioned detection, verification, and comparison of the operational indicators of the contingency plan are repeated.

[0037] If the actual operational metrics of the contingency plan do not match expectations, predefined error response strategies for the current step can be triggered, such as rolling back to the previous stable state, sending an alarm notification to the operations and maintenance platform, or jumping to other emergency handling branches based on preset conditions. If the contingency plan exercise is determined to have failed, the failure status and corresponding context information can be recorded, and the execution of the contingency plan can be terminated.

[0038] The aforementioned adaptive execution engine serves as the "brain" of automated execution, enabling the intelligent orchestration and execution module to possess superior anomaly handling and process control capabilities. Based on a real-time feedback verification mechanism, the drill process is no longer a mechanical sequential execution, but a dynamic process imbued with intelligent judgment. When status detection reveals results inconsistent with expectations (such as a service remaining unhealthy after a restart), the engine will not blindly execute subsequent steps, but will automatically trigger predefined rollback operations, issue alarms, or perform process branch jumps according to pre-arranged strategies. This closed-loop control mechanism of "perception-decision-execution" significantly reduces reliance on manual intervention, effectively prevents secondary accidents caused by drill errors, minimizes drill risks, ensures the reliability and safety of the drill process, and truly achieves intelligent unattended operation.

[0039] After the exercise is completed, the effectiveness of the contingency plan can be quantitatively evaluated in steps 103-104 using the collected reference indicator data. In this embodiment, the module corresponding to steps 103-104 is referred to as the Exercise Evaluation and Optimization Recommendation Module. This module is the core of the invention's value enhancement and closed-loop control, fundamentally solving the pain point of traditional exercises that are "executed without measurement." This module automatically collects multi-dimensional data throughout the entire exercise cycle and performs in-depth analysis based on a built-in quantitative evaluation model, ultimately generating an objective evaluation report and precise optimization suggestions. This transforms a one-off exercise into a data-driven closed loop that drives continuous iteration and optimization of the contingency plan, ensuring that loss mitigation capabilities continuously evolve.

[0040] Step 103: Perform correlation operations on the reference indicator data to associate each contingency plan step in the execution process with the system indicators and business indicators it affects.

[0041] In one possible implementation, refer to Figure 4 The exercise evaluation flowchart shown can align the reference indicator data with the timeline of the execution process by timestamp, obtain the timestamp information corresponding to the reference indicator data, and use this as a benchmark to accurately match the execution time of each contingency plan step with key indicators such as system status changes and business indicator fluctuations, forming a complete causal chain.

[0042] This process goes beyond simply collecting logs. Instead, it synchronizes and correlates three main categories of data across the entire timeline of the contingency plan's execution: system metrics at the infrastructure layer (such as CPU, memory, and network I / O), business metrics at the application layer (such as transaction success rate and response time), and process data from the plan's execution itself (such as the start / end time of each step and the output results). Through precise timestamp alignment, the system can establish a causal relationship between an operation and the resulting changes in system state and fluctuations in business metrics, building a complete and reliable data foundation for subsequent in-depth analysis.

[0043] Step 104: Generate quantitative evaluation results based on the correlated reference indicator data.

[0044] The specific steps can be as follows: Analyze the associated reference indicator data and calculate the quantitative data of the evaluation indicators. The evaluation indicators include any one or more of the following: key indicator fidelity, contingency plan execution efficiency, business impact, and overall success rate. Based on the quantitative data of the evaluation indicators, structured quantitative evaluation results are generated.

[0045] In one possible implementation, the exercise evaluation and optimization recommendation module can incorporate a quantitative evaluation model that transforms the vague "exercise effect" into a series of measurable objective indicators. For the key indicator fidelity, the system can statistically analyze whether the execution steps corresponding to each key indicator are consistent with the plan, thereby calculating the key indicator fidelity. For contingency plan execution efficiency, the achievement of the contingency plan execution efficiency can be evaluated by calculating the gap between the actual recovery time objective (RTO) and the target RTO. For business impact, it can be calculated based on the fluctuation range of business indicators during the contingency plan execution period. For overall success rate, it can be comprehensively calculated based on the achievement of each key indicator during the recovery process. The calculation methods for the above evaluation indicators are exemplary methods provided in this embodiment. In actual applications, they can be set according to the actual business scenario. This embodiment does not limit the specific calculation method.

[0046] After obtaining the quantitative data for each of the aforementioned evaluation indicators, the system integrates the quantitative data into a structured evaluation report template, thereby generating and outputting a structured evaluation report. This report uses data to speak for itself, clearly and objectively demonstrating the effectiveness, efficiency, and stability of the plan, completely changing the previous evaluation model that relied on subjective experience.

[0047] Furthermore, in order to continuously improve the effectiveness of the stop-loss plan, the following steps can be taken: Based on the quantitative evaluation results, analyze and identify abnormal data, including abnormal contingency plan steps or performance bottlenecks. Obtain reference indicator data associated with abnormal data as target analysis data; Based on the correlation analysis algorithm, root cause analysis is performed on the target analysis data to determine the cause of anomalies; Based on the cause of the anomaly, an optimization plan for the abnormal data is generated.

[0048] In one possible implementation, by analyzing the above-mentioned quantitative evaluation results and reference indicator data, the system can automatically identify anomalies or failures in the exercise process, such as a key business indicator failing to recover on time or a delay in resource allocation.

[0049] The system can automatically extract relevant reference indicator data, and the corresponding processing can be as follows: Determine the abnormal timestamp information corresponding to the abnormal data; Identify the target timestamp information associated with the abnormal timestamp information and obtain the reference indicator data corresponding to the target timestamp information as target analysis data.

[0050] The system can automatically associate reference indicator data (i.e., target timestamp information) with the anomaly or failure point based on the anomaly timestamp information corresponding to that point, using this data as target analysis data. Through correlation analysis algorithms, the system performs multi-dimensional correlation mining on the target analysis data to identify the root cause of the anomaly, such as a timeout in a specific database query.

[0051] Based on this, specific and feasible optimization suggestions can be provided for the aforementioned causes of anomalies. For example, for a specific database query timeout, the query can be optimized, the step timeout threshold can be adjusted, or a dependency check can be added. Furthermore, the analyzed causes of anomalies and their corresponding optimization suggestions can be integrated into the evaluation report.

[0052] Based on this, the exercise evaluation and optimization recommendation module finally realizes intelligent root cause analysis and optimization recommendation, completing the value leap from "diagnosis" to "prescription", going beyond simple report generation, directly guiding operation and maintenance personnel to make precise improvements to the contingency plan, thus forming a complete continuous improvement closed loop of "exercise-evaluation-optimization-re-exercise".

[0053] This embodiment can achieve the following beneficial effects: (1) Execute the target loss prevention plan in a simulation environment and collect reference indicator data during the execution process. The reference indicator data includes system indicator data, business indicator data, and execution process data. Perform correlation operations on the reference indicator data to associate each step of the plan with the system and business indicators it affects. Based on the correlated reference indicator data, a quantitative evaluation result is generated. This quantitative evaluation result can be used to evaluate the actual execution effect of the target loss prevention plan to guide maintenance personnel to make precise improvements to the plan, thereby forming a complete continuous improvement closed loop of "exercise-evaluation-optimization-re-exercise", ensuring that the loss prevention plan can continuously evolve with system changes and effectively improve the resilience of the business.

[0054] (2) By combining flow coloring and shadow storage, precise isolation between exercise flow and production flow and lossless reading and writing of exercise data are achieved. This completely solves the core pain points of traditional exercise environment construction, such as high cost, poor realism and potential contamination of production data, and provides a safe and reliable foundation for automated exercises.

[0055] (3) By introducing an intelligent execution engine for contingency plans with status awareness and decision-making capabilities, it can not only automatically execute contingency plan steps, but also monitor the status of system and business indicators in real time, and make adaptive decisions based on predefined rules (such as automatic rollback, jump or alarm), thereby transforming contingency plans from static documents into dynamically adjustable intelligent workflows. This effectively overcomes the huge risks of low efficiency, poor fault tolerance and reliance on human intervention inherent in traditional manual execution and simple script automation.

[0056] Based on the same inventive concept, embodiments of the present invention provide a device for practicing and evaluating loss prevention plans, which is used to implement the aforementioned method for practicing and evaluating loss prevention plans. Figure 5 As shown, a loss mitigation plan exercise and evaluation device 500 includes: an environment construction unit 501, a plan execution unit 502, and an exercise evaluation unit 503.

[0057] Environment building unit 501 is used to build a simulation environment for the target loss mitigation plan; The contingency plan execution unit 502 is used to execute the target loss prevention plan in the simulation environment and collect reference indicator data during the execution process. The reference indicator data includes system indicator data, business indicator data and execution process data. The exercise evaluation unit 503 is used to perform correlation operations on the reference indicator data so that each contingency plan step in the execution process is associated with the system indicators and business indicators it affects; and generates a quantitative evaluation result based on the correlated reference indicator data.

[0058] Optionally, the exercise evaluation unit 503 is used for: The reference indicator data after association is analyzed to calculate the quantitative data of the evaluation indicators, which include any one or more of the following: key indicator restoration degree, contingency plan execution efficiency, business impact degree, and overall success rate. Based on the quantitative data of the aforementioned evaluation indicators, a structured quantitative evaluation result is generated.

[0059] Optionally, the exercise evaluation unit 503 is further configured to: Based on the quantitative evaluation results, anomalies are identified, including contingency plan steps or performance bottlenecks. Obtain the reference indicator data associated with the abnormal data as target analysis data; Based on the correlation analysis algorithm, root cause analysis is performed on the target analysis data to determine the cause of the anomaly; Based on the cause of the anomaly, an optimization scheme for the abnormal data is generated.

[0060] Optionally, the exercise evaluation unit 503 is used for: Align the reference indicator data with the timeline of the execution process to obtain the timestamp information corresponding to the reference indicator data; Determine the abnormal timestamp information corresponding to the abnormal data; Determine the target timestamp information associated with the abnormal timestamp information, and obtain the reference indicator data corresponding to the target timestamp information as target analysis data.

[0061] Optionally, the environment construction unit 501 is used for: Whenever a drill task of the target stop loss plan is triggered, the resource object to be used by the target stop loss plan is determined, and a target drill identifier is added to the resource object; Construct exercise traffic data and add the target exercise identifier to the exercise traffic data, wherein the exercise traffic data includes replicated traffic data based on real-time traffic data and / or simulated traffic data based on historical traffic data; Based on the resource objects and the exercise traffic data, a simulation environment is constructed for the target loss mitigation plan.

[0062] Optionally, the contingency plan execution unit 502 is used for: When executing each step of the target loss mitigation plan in the simulation environment, the plan operation indicator data corresponding to the current plan step is determined. The plan operation indicator data includes any one or more of the following: service port status, interface response time, and business error rate. Compare the operational indicator data of the contingency plan with the corresponding expected operational indicator data in the target stop-loss contingency plan; If the operational indicator data of the plan meets the expected operational indicator data, then the target stop-loss plan will continue to be executed; If the operational indicator data of the contingency plan does not meet the expected operational indicator data, the pre-set exercise error response strategy is triggered. The exercise error response strategy includes rollback operation, alarm operation, or jump process branch. The process continues until each step of the target stop-loss plan is completed, or until the target stop-loss plan fails to execute and terminates.

[0063] Optionally, the contingency plan execution unit 502 is further configured to: A graphical process for obtaining the target stop-loss plan edited based on graphical components; The graphical process of the target stop-loss plan is compiled into a structured standard document, which includes the plan steps of the target stop-loss plan.

[0064] This embodiment can achieve the following beneficial effects: In this embodiment, the target loss mitigation plan is executed in a simulation environment, and reference indicator data is collected during the execution process. This reference indicator data includes system indicator data, business indicator data, and execution process data. A correlation operation is performed on the reference indicator data to associate each step of the plan with its affected system and business indicators. Based on the correlated reference indicator data, a quantitative evaluation result is generated. This quantitative evaluation result can be used to assess the actual execution effect of the target loss mitigation plan, guiding operations personnel to make precise improvements to the plan. This forms a complete continuous improvement closed loop of "exercise-evaluation-optimization-re-exercise," ensuring that the loss mitigation plan can continuously evolve with system changes and effectively improve business resilience.

[0065] An exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of the present invention.

[0066] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0067] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0068] refer to Figure 6 The present invention will now be described in the form of a structural block diagram of an electronic device 600 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0069] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0070] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or text information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 607 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 may include, but is not limited to, disks and optical discs. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth devices, Wi-Fi devices, WiMax devices, cellular communication devices, and / or the like.

[0071] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the above-described methods for rehearsing and evaluating loss prevention plans can be implemented as computer software programs tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. In some embodiments, the computing unit 601 can be configured to perform the above-described methods for rehearsing and evaluating loss prevention plans by any other suitable means (e.g., by means of firmware).

[0072] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0073] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0074] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0075] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0076] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0077] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A method for rehearsing and evaluating a loss prevention plan, characterized in that, The method comprises: constructing a simulation environment for a target loss stop plan; executing the target loss stop plan in the simulation environment and collecting reference index data during execution, the reference index data comprising system index data, business index data and execution process data; performing correlation operations on the reference index data to associate each plan step in the execution process with the system index and business index affected thereby; generating a quantitative evaluation result based on the correlated reference index data.

2. The method of claim 1, wherein, The generation of a quantitative evaluation result based on the correlated reference index data comprises: analyzing the correlated reference index data to calculate quantitative data of evaluation indexes, the evaluation indexes comprising any one or more of the following: key index restoration degree, plan execution efficiency, business impact degree, overall success rate; generating a structured quantitative evaluation result based on the quantitative data of the evaluation indexes.

3. The method of claim 1, wherein, The method further comprises: analyzing the quantitative evaluation result to identify abnormal data, the abnormal data comprising abnormal plan steps or performance bottlenecks; acquiring the reference index data associated with the abnormal data as target analysis data; performing root cause analysis on the target analysis data based on a correlation analysis algorithm to determine the cause of the abnormality; generating an optimization scheme for the abnormal data based on the cause of the abnormality.

4. The method of claim 3, wherein, The correlation operations performed on the reference index data comprise timestamp alignment of the reference index data with a time axis of the execution process to obtain timestamp information corresponding to the reference index data. The acquisition of the reference index data associated with the abnormal data as target analysis data comprises: determining abnormal timestamp information corresponding to the abnormal data; determining target timestamp information associated with the abnormal timestamp information and acquiring the reference index data corresponding to the target timestamp information as target analysis data.

5. The method of claim 1, wherein, The construction of a simulation environment for a target loss stop plan comprises: whenever a rehearsal task of a target loss stop plan is triggered, determining resource objects to be used by the target loss stop plan and adding a target rehearsal identifier to the resource objects; constructing rehearsal traffic data and adding the target rehearsal identifier to the rehearsal traffic data, wherein the rehearsal traffic data comprises replicated traffic data based on real-time traffic data and / or simulated traffic data based on historical traffic data; constructing a simulation environment for a target loss stop plan based on the resource objects and the rehearsal traffic data.

6. The method of claim 1, wherein, The execution of the target loss stop plan in the simulation environment comprises: when each plan step of the target loss stop plan is executed in the simulation environment, determining plan running index data corresponding to the current plan step, the plan running index data comprising any one or more of the following: service port state, interface response time, business error rate; comparing the plan running index data with corresponding expected running index data in the target loss stop plan; if the plan running index data conforms to the expected running index data, continuing to execute the target loss stop plan; If the pre-arranged operation index data does not conform to the expected operation index data, a pre-set drill error coping strategy is triggered, the drill error coping strategy including a rollback operation, an alarm operation or a jump flow branch; Until each pre-arranged step of the target stop-loss pre-arrangement is executed or the target stop-loss pre-arrangement is terminated due to execution failure.

7. The method of claim 1, wherein, Before the target stop-loss pre-arrangement is executed in the simulation environment, the method further includes: obtaining a graphical flow of the target stop-loss pre-arrangement edited based on a graphical component; compiling the graphical flow of the target stop-loss pre-arrangement into a structured standard file, the standard file including pre-arranged steps of the target stop-loss pre-arrangement.

8. A device for practicing and evaluating a loss-prevention plan, characterized by The device includes: an environment construction unit configured to construct a simulation environment for a target stop-loss pre-arrangement; a pre-arrangement execution unit configured to execute the target stop-loss pre-arrangement in the simulation environment and collect reference index data during execution, the reference index data including system index data, business index data and execution process data; a drill evaluation unit configured to perform an association operation on the reference index data to associate each pre-arranged step in the execution process with system index and business index affected thereby; and generate a quantitative evaluation result based on the associated reference index data. 9.An electronic device, comprising: a processor; and a memory storing programs, wherein the programs include instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-7.

10. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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