Operational event response method, system, device and medium for differentiated needs

Abstract

The application discloses a kind of operation and maintenance event response methods, systems, equipment and medium for differentiation needs, construct by priority parameter, response time limit requirement and event type consist of differentiation needs portrait;Based on differentiation needs portrait, the risk of historical event is analyzed by calculating risk value, the risk level of target operation and maintenance event is determined by combining the difference rate of target event and historical event;According to differentiation needs portrait and risk level, hardware resources are divided into core, general, shared three-level resource pool, and the resource allocation proportion is dynamically adjusted;Call the resource execution event response after scheduling, when response length reaches the preset proportion of response time limit requirement, trigger resource expansion mechanism.Three-level resource pool is used, resource allocation proportion is dynamically adjusted in combination with event attribute, and by monitoring response length, a closed loop of collection-portrait-classification-scheduling-monitoring-optimization is formed, to ensure response completion rate, and resource expansion is only for overtime risk event, to avoid excessive consumption of resources, and response effect is controllable.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, and specifically relates to a method, system, device and medium for responding to operation and maintenance events with differentiated needs. Background Technology

[0002] In the current digital operations and maintenance scenario, the complexity of enterprise IT architecture is constantly increasing, and operations and maintenance events occur frequently and show significant differences in scenarios. Although existing event response-based technical solutions have accumulated certain technical expertise in resource allocation, security event analysis, request and response determination, and storage resource utilization, they are still difficult to meet the differentiated processing needs of operations and maintenance events.

[0003] Patent document CN119759586A proposes an event-response-based resource allocation method, which determines the resource allocation method and optimization strategy by detecting the number of request events, historical CPU utilization, and system load power, thereby achieving resource allocation adjustment; CN112583847A focuses on the analysis of network security incidents in small and medium-sized enterprises, and uses a combination of nonlinear recursive filtering algorithm with spherical k-means clustering and radial kernel single-class SVM to achieve network attack detection and ranking; CN115115466A addresses event request response by analyzing user historical event information and outliers to determine user categories and block malicious user requests; CN116414826A improves storage resource utilization by reusing the original operation flow table of the switching equipment to store target event operation entries.

[0004] However, existing technologies have the following problems in application: First, they are not adapted to differentiated needs. CN119759586A allocates resources only based on hardware resource parameters without considering the differences in business attributes of operation and maintenance events. CN116414826A does not design resource calling logic in combination with the differentiated needs of events, resulting in delays in response to core events and resource redundancy for ordinary events. Second, there is a lack of linkage between risk and response. CN112583847A only sorts network attack detection and does not cover the risk classification of operation and maintenance events. CN115115466A's risk assessment does not extend to the operation and maintenance scenario and cannot dynamically adjust the response strategy according to the event risk level. This easily leads to problems such as untimely handling of high-risk events and excessive resource consumption of low-risk events. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and medium for responding to operation and maintenance events with differentiated needs, so as to solve the problems of low efficiency in responding to core business events and waste of resources for ordinary events in the existing technology.

[0006] This invention is achieved through the following technical solution: The operational incident response method for differentiated requirements includes the following steps: Collect basic event information and business-related information; Based on the collected information, the event priority parameters are determined by calculating the event priority, and the event response timeliness requirements are determined by combining the business interruption loss coefficient. A differentiated requirement profile consisting of priority parameters, response timeliness requirements and event types is constructed. Based on the differentiated demand profile, the historical operation and maintenance data matching algorithm is called to extract similar historical events. The risk of historical events is analyzed by calculating risk values. Combined with the difference rate between the target event and historical events, the risk level of the target operation and maintenance event is determined. Based on differentiated demand profiles and risk levels, a resource pool hierarchical scheduling algorithm is adopted to divide hardware resources into three levels: core, general, and shared resource pools. Resources from corresponding resource pools are allocated to events with different priorities and risk levels, and the resource allocation ratio is dynamically adjusted. The scheduled resources are invoked to execute event responses. The response process is monitored in real time through a response duration monitoring algorithm. When the response duration reaches a preset proportion of the response time requirement, the resource expansion mechanism is triggered until the event response is completed.

[0007] In some embodiments, the basic event information includes the event type, trigger source identifier, and event frequency within a first preset duration; the business association information includes the business system level corresponding to the event, the scope of business impact, and the business interruption loss coefficient.

[0008] In some embodiments, the event priority parameter is determined by calculating the event priority using the following formula: P = α × S + β × R + γ × F; Wherein, P: event priority parameter, with a value range of 0-1, the larger the value, the higher the priority; α: business system level weight coefficient; S: business system level coefficient; β: business impact scope weight coefficient; R: business impact scope coefficient; γ: event frequency weight coefficient; F: event frequency normalization value within the first preset duration, which is the actual frequency / maximum allowed frequency.

[0009] In some embodiments, the formula used to analyze the risk of historical events through the risk value calculation algorithm is as follows: ; Among them, Risk hist Risk value for a single historical event, ranging from 0 to 2, with higher values ​​indicating higher risk; L act Actual losses resulting from failure to respond promptly to historical events; L coef : Business interruption loss coefficient corresponding to historical events; D: Event duration impact coefficient, which is the actual duration / preset tolerance duration.

[0010] In some embodiments, the Risk of calculating historical eventshist The average value, based on historical events' Risk hist The risk level of the target event is calculated by averaging the values.

[0011] On the other hand, the present invention also provides an operation and maintenance event response system for differentiated needs, used to implement the operation and maintenance event response method for differentiated needs, including: The event collection module responds to target operation and maintenance events in operation and maintenance scenarios and is used to collect basic event information and business-related information. The demand profile building module, based on the collected information, determines the event priority parameters by calculating the event priority, and determines the event response timeliness requirements by combining the business interruption loss coefficient, and builds a differentiated demand profile composed of priority parameters, response timeliness requirements and event types. The risk grading module, based on differentiated demand profiles, calls historical operation and maintenance data matching algorithms to extract similar historical events, analyzes the risk of historical events by calculating risk values, and determines the risk level of the target operation and maintenance event by combining the difference rate between the target event and historical events. The resource scheduling module, based on differentiated demand profiles and risk levels, adopts a hierarchical resource pool scheduling algorithm to divide hardware resources into three levels: core, general, and shared resource pools. It allocates corresponding resource pool resources to events with different priorities and risk levels and dynamically adjusts the resource allocation ratio. The response execution module calls the scheduled resources to execute the event response. It monitors the response process in real time through a response duration monitoring algorithm. When the response duration reaches the preset ratio of the response time requirement, it triggers the resource expansion mechanism until the event response is completed.

[0012] On the other hand, the present invention also provides an electronic device, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to execute an operational event response method for differentiated needs by executing the executable instructions.

[0013] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an operation and maintenance event response method for differentiated needs.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: The method of this invention constructs differentiated demand profiles by adding business dimensions such as business system level and business interruption loss, and achieves accurate classification of operation and maintenance events. It can prioritize the protection of core business events, and the demand analysis dimensions are more comprehensive, avoiding delays in core events caused by applying hardware parameters in a one-size-fits-all manner.

[0015] By binding historical operation and maintenance data with target event risks, quantifying event risk levels by calculating risk values, and directly linking them to resource pool allocation strategies, the problem of disconnect between risk assessment and response in existing technologies is solved.

[0016] A three-tiered resource pool is adopted, and the resource allocation ratio is dynamically adjusted based on event attributes. At the same time, by monitoring the response time, expansion is triggered when the response time reaches the set value of the timeliness requirement, forming a closed loop of collection-profiling-classification-scheduling-monitoring-optimization, which ensures the response completion rate. Moreover, resource expansion is only for risk events with excessive timeout, avoiding excessive consumption of resources and making the response effect controllable.

[0017] The method and system of this invention are applicable to operation and maintenance event response with differentiated needs in multiple business scenarios. They can be widely used in IT operation and maintenance management in industries such as finance, manufacturing, and the Internet, enabling priority response to core business operation and maintenance events and efficient resource scheduling. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the operation and maintenance event response method and system for differentiated needs according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0021] To address the problem that existing technologies cannot iteratively optimize resource allocation strategies and risk assessment models based on historical response data, leading to a decline in response efficiency over long-term use, some embodiments of the present invention provide an operation and maintenance event response system for differentiated needs, comprising: The event acquisition module responds to target operation and maintenance events in operation and maintenance scenarios and is used to collect basic event information and business-related information. The basic event information includes the event type, trigger source identifier, and event frequency within a first preset duration. The business-related information includes the business system level corresponding to the event, the scope of business impact, and the business interruption loss coefficient. The demand profile building module, based on the collected information, determines the event priority parameters through an event priority calculation algorithm, and determines the event response timeliness requirements by combining the business interruption loss coefficient, thereby constructing a differentiated demand profile composed of priority parameters, response timeliness requirements, and event types. The risk grading module, based on differentiated demand profiles, calls historical operation and maintenance data matching algorithms to extract similar historical events, analyzes the risks of historical events through risk value calculation algorithms, and determines the risk level of the target operation and maintenance event by combining the difference rate between the target event and historical events. The resource scheduling module, based on differentiated demand profiles and risk levels, adopts a hierarchical resource pool scheduling algorithm to divide hardware resources into three levels: core, general, and shared resource pools. It allocates corresponding resource pool resources to events with different priorities and risk levels and dynamically adjusts the resource allocation ratio. The response execution module calls the scheduled resources to execute the event response. It monitors the response process in real time through a response duration monitoring algorithm. When the response duration reaches the preset ratio of the response time requirement, it triggers the resource expansion mechanism until the event response is completed.

[0022] The method of this invention constructs differentiated demand profiles by adding business dimensions such as business system level and business interruption loss, and achieves accurate classification of operation and maintenance events. It can prioritize the protection of core business events, and the demand analysis dimensions are more comprehensive, avoiding delays in core events caused by applying hardware parameters in a one-size-fits-all manner.

[0023] By binding historical operation and maintenance data with target event risks, quantifying event risk levels by calculating risk values, and directly linking them to resource pool allocation strategies, the problem of disconnect between risk assessment and response in existing technologies is solved.

[0024] A three-tiered resource pool is adopted, and the resource allocation ratio is dynamically adjusted based on event attributes. At the same time, by monitoring the response time, expansion is triggered when the response time reaches 90% of the timeliness requirement, forming a closed loop of collection-profiling-classification-scheduling-monitoring-optimization, ensuring a 100% response completion rate. Moreover, resource expansion is only for risk events with excessive timeout, avoiding excessive resource consumption and ensuring controllable response results.

[0025] By flexibly adjusting the algorithm parameters and adapting them to the hardware parameters of the resource pool, it can cover multiple industry scenarios and has better cross-scenario adaptability.

[0026] The operation and maintenance event response method and system of the present invention will be described in detail below with reference to specific embodiments.

[0027] Example 1: This embodiment takes the transaction link interruption operation and maintenance event of a transaction system (such as a transfer settlement system) as an example to illustrate the specific application of the technical solution in detail. In this scenario, core events need to meet the requirements of millisecond-level response and zero data loss, while ordinary events (such as back-end reconciliation system failures) can accept minute-level response.

[0028] The process for handling operation and maintenance incidents is as follows: S1. Event Acquisition Module Execution Process In response to a target maintenance event indicating an interruption in the transaction link of the transfer and settlement system, collect the following information: Basic information: The event type is transaction link failure, the trigger source identifier is transfer settlement system node A (IP: 192.168.1.101), and the event frequency is 5 times within the first preset time period (3 minutes, based on the optimal value of event frequency statistics in the financial industry) (5 consecutive interruption alarms triggered within 5 minutes). Business-related information: The business system level is a core business system (core transaction systems are uniformly classified as core level), the scope of business impact is all users (this system serves all individual and enterprise users), and the business interruption loss coefficient is 2,000 yuan / minute (calculated based on historical data: the average daily transfer transaction volume is 500,000 transactions, the average amount of each transaction is 10,000 yuan, and the loss for 1 minute of interruption is about 2,000 yuan. It can also be set according to the actual situation).

[0029] S2, Execution process of the requirement profiling construction module Step 1: Determine the priority parameter P using an event priority calculation algorithm. The algorithm formula is as follows: P = α × S + β × R + γ × F; in: P: Event priority parameter, with a value range of 0-1. The larger the value, the higher the priority. α: Business system priority weight coefficient, with a value of 0.5 (core business systems need to be prioritized, so the highest weight is set). S: Business system level coefficient, with a value of 1.0 for core business systems and 0.5 for non-core business systems. In this embodiment, S=1.0. β: Business impact scope weighting coefficient, with a value of 0.3 (the user impact scope is directly related to the loss scale, and its weight is secondary). R: Business impact range coefficient, with a value of 1.0 for all users and a linear mapping of user percentage × 1.0 for local users (e.g., a value of 0.1 for affecting 10% of users). In this embodiment, R = 1.0. γ: Event frequency weighting coefficient, with a value of 0.2 (frequency reflects the urgency of the event, and has the lowest weight). F: Normalized value of event frequency within the first preset duration, calculated as actual frequency / maximum allowed frequency. In this embodiment, F = 0.5.

[0030] Substituting the values ​​into the calculation, we can see that the event priority is high.

[0031] Step 2: Determine the response time requirement T based on the business interruption loss coefficient: According to the rules, the loss coefficient in this embodiment is assumed to be 2000 yuan / minute, and T=80ms (20ms redundancy is reserved to avoid the impact of sudden resource fluctuations).

[0032] Step 3: Construct a differentiated demand profile (event type = transaction link failure).

[0033] S3, Risk Classification Module Execution Process Step 1: Call the historical operation and maintenance data matching algorithm to extract similar historical events with event type = transaction link failure, P∈[0.8,1.0], T∈[70ms,90ms] from the historical data. A total of 12 historical records were matched. Step 2: Analyze the risk of historical events using the Risk Value Calculation Algorithm, as shown in the following formula: ; in: Risk hist Risk value for a single historical event, ranging from 0 to 2, with higher values ​​indicating higher risk; L act Actual losses caused by failure to respond to historical events in a timely manner, such as 5 transactions failing due to a 100ms response delay to a certain historical event, resulting in an actual loss of 1,000 yuan; L coef The business interruption loss coefficient corresponding to historical events is calculated using the same logic as that for current events. D: Event duration impact coefficient, which is the actual duration / preset tolerance duration (the preset tolerance duration for core trading system failures is 5 minutes). For example, if a historical event lasts for 8 minutes, D is approximately 1.6.

[0034] Calculate the risk of 12 historical events hist Average value: Avg(Risk) hist =1.3; Step 3: Calculate the target event risk level (Risk)target The formula is as follows: Risk target =Avg(Risk hist )×(1+0.2×δ); in: Risk target Risk level of the target event, ranging from 0 to 2, with a value > 1.2 indicating high risk; δ: The difference rate between the business impact scope of the target event and historical events, δ=(number of users affected by the target event - average number of users affected by historical events) / average number of users affected by historical events. In this embodiment, both the target event and historical events are for all users, so δ=0.

[0035] Substituting into the calculation, we get: Risk target =1.3×(1+0.2×0)=1.3, which is considered high risk.

[0036] S4, Resource Scheduling Module Execution Process A resource pool hierarchical scheduling algorithm is adopted: Step 1: Resource pool division: Core resource pool (CPU frequency 3.2GHz, memory bandwidth 45GB / s, dedicated to core business events), general resource pool (CPU frequency 2.8GHz, memory bandwidth 30GB / s, serving medium-priority events), shared resource pool (CPU frequency 2.4GHz, memory bandwidth 20GB / s, serving low-priority events). Step 2: Resource Allocation: The target event is of high priority and high risk. Allocate resources from the core resource pool. The resource allocation ratio = idle resources in the core resource pool × 60% (a higher resource ratio is reserved for high-risk events to avoid insufficient resources). If the current idle CPU in the core resource pool is 8 cores and memory is 64GB, then allocate 4.8 CPU cores and 38.4GB memory. Step 3: Dynamic adjustment: Monitor the utilization rate of the core resource pool in real time. If the utilization rate is >80%, temporarily schedule 10% of the idle resources in the general resource pool to supplement it (such as scheduling 1 CPU core and 8GB of memory).

[0037] S5, Response Execution Module Execution Process The allocated 4.8-core CPU and 38.4GB of memory are used to initiate the transaction link reconstruction response program; The response process is monitored in real time using a response duration monitoring algorithm: resource expansion is triggered when the response duration reaches 90% of T (i.e., 72ms). During the actual response process, at 65ms, it was detected that the response progress was only 70% complete and the expected timeout period was triggered. The expansion mechanism was activated, and an additional CPU core and 8GB of memory were allocated from the core resource pool. The transaction link was finally rebuilt within 78ms. The response time did not exceed T=80ms and no transaction data was lost.

[0038] This embodiment adopts a three-dimensional linkage of priority, risk, and resources, which not only achieves millisecond-level response to core events (meeting the compliance requirements of the financial industry), but also avoids excessive occupation of the core resource pool through dynamic resource adjustment. In this embodiment, the final utilization rate of the core resource pool is controlled within a preset range, and the remaining resources can cope with other sudden core events, so that a single resource scheduling method can achieve the dual effects of response time guarantee and resource redundancy reservation. The risk value calculation algorithm introduces a duration impact coefficient D, which reflects the impact of event duration on risk and indirectly provides a reference for subsequent response duration settings (e.g., events with high D values ​​need to reserve more response redundancy), thus achieving linkage optimization between risk assessment and response timeliness optimization.

[0039] Example 2: This embodiment takes the failure maintenance event of a welding robot in an automobile factory as an example. In this scenario, the failure of production equipment should prioritize ensuring that the production line is not interrupted (high-risk event), while the daily inspection alarm of the equipment (low-risk event) can be delayed. The difference from the first embodiment is that the business attribute dimension is adjusted, the algorithm parameters are adapted to the manufacturing scenario, and the resource pool is divided in combination with the characteristics of the production equipment.

[0040] The process for handling operation and maintenance incidents is as follows: S1. In response to a welding robot malfunction maintenance event, collect the following information: Basic information: The event type is production equipment failure, the trigger source is welding workshop robot B (equipment number: XXXX), and the event frequency is 1 time within the first preset time period (5 minutes, the frequency of manufacturing equipment failure is relatively low, so a longer time period is set). Business-related information: The business system level is the production core system (equipment that directly affects the production line is classified as the production core level), the business impact scope is local users (this robot only serves one production line in the welding workshop, involving 20 workers), and the business interruption loss coefficient is 1500 yuan / minute (calculated based on factory data; can be set according to actual conditions).

[0041] S2, Execution process of the requirement profiling construction module Step 1: Determine the priority parameter P using the event priority calculation algorithm. The formula is the same as in Example 1, but the parameters are adapted to the manufacturing scenario. P = α × S + β × R + γ × F; Parameter adjustment: α=0.45 (The weight of the core production system is slightly lower than that of the core financial system, because a failure in manufacturing equipment can be temporarily replaced by backup equipment). S=1.0 (Production core system coefficient). β=0.35 (In manufacturing scenarios, the influence range of the production line is directly related to the production capacity, so the weight is increased). R=0.2 (This robot serves 1 production line, accounting for 5% of the total number of production lines in the factory, hence R=0.2). γ=0.2 (frequency weight remains unchanged); F=0.1 (the frequency is once within the first preset duration of 5 minutes, and the maximum allowed frequency is 10 times / 5 minutes, set to 0.1).

[0042] Substituting the values ​​into the calculation, the event priority is set to medium priority.

[0043] Step 2: Determine the response time requirement T based on the business interruption loss coefficient: According to the rule that when 500 yuan / minute < loss coefficient ≤ 1000 yuan / minute, 100ms < T ≤ 500ms, in this embodiment the loss coefficient = 1500 yuan / minute, so T = 300ms (the failure response of manufacturing equipment does not need to be in the financial grade millisecond level, 300ms can balance response efficiency and resource cost).

[0044] Step 3: Construct a differentiated demand profile (P=0.54, T=300ms, event type=production equipment failure).

[0045] S3, Risk Classification Module Execution Process Step 1: Call the historical operation and maintenance data matching algorithm to extract similar historical events in the historical data with event type = production equipment failure, P∈[0.5,0.6], T∈[250ms,350ms], and a total of 8 historical records were matched; Step 2: Analyze the risk of historical events using a risk value calculation algorithm. The formula is the same as in Example 1, but the parameters are adapted to the manufacturing scenario. ; in: L act Actual losses from historical events, such as a production line shutdown of 10 minutes due to a response delay in a historical event, resulting in a loss of 15,000 yuan; L coef The historical event business interruption loss coefficient is calculated using the same logic as the current event. D: In the manufacturing scenario, it is adjusted to (actual downtime / preset downtime tolerance). The preset downtime tolerance is 15 minutes (the manufacturing plant has backup equipment and can tolerate shorter downtime). If a historical event causes an 8-minute downtime, D = 8 / 15 ≈ 0.53.

[0046] Calculate the Avg(Risk) of 8 historical events hist =0.9; Step 3: Calculate the target event risk level (Risk) target The formula is the same as in Example 1: Risk target =Avg(Risk hist )×(1+0.2×δ); in: δ: In the manufacturing scenario, it is (the target event's impact on the production line capacity - the average impact of historical events on the production line capacity) / the average impact of historical events on the production line capacity. In this embodiment, both the target event and the historical events affect 1 production line, so δ=0.

[0047] Assuming we substitute the values ​​into the calculation, Risk target Medium risk (the risk level is set according to the actual situation; the result of the risk level is shown here).

[0048] Resource scheduling module execution process A resource pool hierarchical scheduling algorithm is adopted, and the resource pool division is adjusted according to the manufacturing scenario: Step 1: Resource pool division: Core resource pool (industrial-grade CPU with a main frequency of 3.0GHz, real-time communication module adapted to production equipment, dedicated to core production events), general resource pool (industrial-grade CPU with a main frequency of 2.6GHz, ordinary communication module, serving medium-priority events), shared resource pool (industrial-grade CPU with a main frequency of 2.2GHz, non-real-time communication module, serving low-priority events). Step 2: Resource Allocation: The target event is of medium priority and medium risk. Allocate resources from the general resource pool. The resource allocation ratio = idle resources in the general resource pool × 40% (medium risk events do not require a high percentage of resources). If the general resource pool currently has 6 idle CPU cores and 48GB of memory, then allocate 2.4 CPU cores and 19.2GB of memory. Step 3: Dynamic adjustment: If the utilization rate of the general resource pool is >90%, then 15% of the idle resources in the shared resource pool will be scheduled to supplement the resource pool, and at the same time, the backup equipment start command will be triggered (specific to the manufacturing scenario: start the backup welding robot WR-20240502 to temporarily replace the faulty equipment, and achieve dual protection of resource scheduling and equipment redundancy).

[0049] Response Execution Module Execution Process The allocated 2.4-core CPU and 19.2GB memory are used to start the welding robot fault diagnosis and repair response program, and the backup robot is started at the same time; The response process is monitored through a response time monitoring algorithm: when the response time reaches 90% of T (i.e., 270ms), capacity expansion is triggered. During the actual response, the fault repair progress was monitored to be 80% complete at 250ms. It was expected to be completed in 290ms, which did not reach the threshold for triggering capacity expansion. Finally, the fault repair was completed within 285ms. Meanwhile, the backup robot only started for 30ms and then stopped (without any loss of production capacity).

[0050] This embodiment uses a combination of resource scheduling and backup equipment linkage to not only achieve timely response to medium-priority events (repair completed in 285ms, lower than T=300ms), but also avoid production line shutdowns by temporarily starting backup equipment. Even if resource fluctuations occur during the response process, backup equipment can ensure zero capacity loss, achieving the dual effect of timely response and capacity guarantee for a single event, thus solving the pain point of capacity loss due to equipment failure in the manufacturing industry.

[0051] In the event priority calculation algorithm, β (business impact range weight) is adjusted to 0.35, which not only adapts to the demand for production capacity priority in manufacturing scenarios, but also indirectly reduces the resource occupation of non-core impact range events. For example, the priority parameter of workshop lighting system failure (low impact range coefficient) is only 0.2. It automatically allocates resources from the shared resource pool to avoid competing for resources with production equipment events, and realizes the linkage optimization of parameter adaptation and resource peak shifting.

[0052] On the other hand, the present invention also provides an electronic device, comprising: Processor; and Memory for storing the executable instructions of the processor; The processor is configured to execute an operational event response method for differentiated needs by executing the executable instructions.

[0053] On the other hand, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements an operation and maintenance event response method for differentiated needs.

[0054] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for responding to operational events with differentiated requirements, characterized in that, Includes the following steps: Collect basic event information and business-related information; Based on the collected information, the event priority parameters are determined by calculating the event priority, and the event response timeliness requirements are determined by combining the business interruption loss coefficient. A differentiated requirement profile consisting of priority parameters, response timeliness requirements and event types is constructed. Based on the differentiated demand profile, the historical operation and maintenance data matching algorithm is called to extract similar historical events. The risk of historical events is analyzed by calculating risk values. Combined with the difference rate between the target event and historical events, the risk level of the target operation and maintenance event is determined. Based on differentiated demand profiles and risk levels, a resource pool hierarchical scheduling algorithm is adopted to divide hardware resources into three levels: core, general, and shared resource pools. Resources from corresponding resource pools are allocated to events with different priorities and risk levels, and the resource allocation ratio is dynamically adjusted. The scheduled resources are invoked to execute event responses. The response process is monitored in real time through a response duration monitoring algorithm. When the response duration reaches a preset proportion of the response time requirement, the resource expansion mechanism is triggered until the event response is completed.

2. The operation and maintenance event response method for differentiated needs according to claim 1, characterized in that, The basic event information includes the event type, trigger source identifier, and event frequency within a first preset duration; the business association information includes the business system level corresponding to the event, the scope of business impact, and the business interruption loss coefficient.

3. The operation and maintenance event response method for differentiated needs according to claim 1, characterized in that, The following formula is used to calculate and determine the event priority parameters: P = α × S + β × R + γ × F; Wherein, P: event priority parameter, with a value range of 0-1, the larger the value, the higher the priority; α: business system level weight coefficient; S: business system level coefficient; β: business impact scope weight coefficient; R: business impact scope coefficient; γ: event frequency weight coefficient; F: event frequency normalization value within the first preset duration, which is the actual frequency / maximum allowed frequency.

4. The operation and maintenance event response method for differentiated needs according to claim 1, characterized in that, The formula used in analyzing the risk of historical events through the risk value calculation algorithm is as follows: ； Among them, Risk hist Risk value for a single historical event, ranging from 0 to 2, with higher values ​​indicating higher risk; L act Actual losses resulting from failure to respond promptly to historical events; L coef : Business interruption loss coefficient corresponding to historical events; D: Event duration impact coefficient, which is the actual duration / preset tolerance duration.

5. The operation and maintenance event response method for differentiated needs according to claim 4, characterized in that, Calculating Risks of Historical Events hist The average value, based on historical events' Risk hist The risk level of the target event is calculated by averaging the values.

6. An operation and maintenance event response system for differentiated needs, characterized in that, The method for implementing the operation and maintenance event response method for differentiated requirements as described in any one of claims 1-5 includes: The event collection module responds to target operation and maintenance events in operation and maintenance scenarios and is used to collect basic event information and business-related information. The demand profile building module, based on the collected information, determines the event priority parameters by calculating the event priority, and determines the event response timeliness requirements by combining the business interruption loss coefficient, and builds a differentiated demand profile composed of priority parameters, response timeliness requirements and event types. The risk grading module, based on differentiated demand profiles, calls historical operation and maintenance data matching algorithms to extract similar historical events, analyzes the risk of historical events by calculating risk values, and determines the risk level of the target operation and maintenance event by combining the difference rate between the target event and historical events. The resource scheduling module, based on differentiated demand profiles and risk levels, adopts a hierarchical resource pool scheduling algorithm to divide hardware resources into three levels: core, general, and shared resource pools. It allocates corresponding resource pool resources to events with different priorities and risk levels and dynamically adjusts the resource allocation ratio. The response execution module calls the scheduled resources to execute the event response. It monitors the response process in real time through a response duration monitoring algorithm. When the response duration reaches the preset ratio of the response time requirement, it triggers the resource expansion mechanism until the event response is completed.

7. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the operation and maintenance event response method for differentiated needs as described in claims 1-6 by executing the executable instructions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the operation and maintenance event response method for differentiated needs as described in claims 1-6.

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