Enterprise service management system and method oriented to multiple service scenes

Abstract

The invention relates to the field of enterprise service management, and discloses an enterprise service management system and method oriented to multiple service scenarios, and the method comprises the steps: carrying out the real-time capturing of an operation event generated by each service module in a distributed storage node, disassembling the event into an operation type, a data influence range and related resources; calculating the dynamic weight of the potential impact of the event on the service chain in combination with the delay, resource competition and synchronization sensitivity of the event on the whole service chain; for the high-risk events and the corresponding nodes, simulating local state changes generated in the distributed storage environment after the events are triggered; key nodes and operation events are integrated, and the event execution sequence and resource allocation are dynamically adjusted in combination with node processing capacity, storage bandwidth and synchronous delay constraint; and performing real-time state correction and operation priority adjustment on the key nodes through the generated coordination strategy. The method has the advantage of keeping the real-time consistency of the service state.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of enterprise service management, in particular to an enterprise service management system and method for multi-business scenarios. BACKGROUND

[0002] Under the background of the continuous expansion of current enterprise multi-business integration applications, the internal service management platform usually carries multiple types of business modules such as sales process, customer support, inventory scheduling and financial settlement. The existing system generally relies on manual input triggering, fixed format form calling and static permission strategy in cross-department cooperation, which is difficult to adapt to the frequently changing service links in multi-business scenarios. There is a typical small problem in the cooperation process of enterprise customer service and warehouse scheduling: when customer service personnel are processing customer exception work orders, they need to obtain the current inventory status and its change record in real time. However, due to the distributed storage architecture commonly used by each business module, the refresh cycle of local data copies of different business nodes is not consistent, which causes the inventory information displayed on the customer service end to lag 3-10 minutes, making the customer service unable to confirm whether to immediately execute restocking, order cancellation or warehouse transfer operation based on the latest status, further causing repeated communication, work order retention and even incorrect instruction issuance. The existing technology cannot provide consistent, traceable and real-time service status feedback for different roles in the service link in a low system burden manner in multi-business scenarios. Therefore, it is necessary to design an enterprise service management system and method for multi-business scenarios that can keep the service status real-time consistent. SUMMARY

[0003] In view of the deficiencies of the prior art, the present application provides an enterprise service management system and method for multi-business scenarios, which has the advantage of keeping the service status real-time consistent and solves the problems in the background technology.

[0004] To achieve the above purpose of keeping the service status real-time consistent, the present application provides the following technical scheme: an enterprise service management method for multi-business scenarios, comprising the following steps: Real-time capture of operation events generated by each business module in the distributed storage node, disassemble the events into operation type, data impact range and involved resources, form a node snapshot sequence, and depict the state and potential operation impact of each node at a specific time; According to the node snapshot sequence, combined with the delay of the event on the overall service chain, resource competition and synchronization sensitivity, calculate the dynamic weight of the potential impact of the event on the service chain, and include the parallel operation and shared resource occupation between nodes into the comprehensive evaluation, form an event risk table; For high-risk events and corresponding nodes, simulate the local state changes generated after event triggering in a distributed storage environment, identify the key nodes that cause short-term inconsistency in service chain by comparing the simulation results with real-time snapshots, and record resource consumption and operation dependency characteristics; Integrate key nodes and operation events, combine node processing capacity, storage bandwidth and synchronization delay constraints, dynamically adjust event execution order and resource allocation, and generate coordination strategies for real-time execution in a distributed storage environment; Through the generated coordination strategy, perform real-time state correction and operation priority adjustment on the key nodes, collect node response and event completion information, and form a state feedback loop.

[0005] Preferably, the process of forming a node snapshot sequence is: Align operation events from different business modules according to a unified timestamp within the distributed storage node, and group event sources based on event identifiers, call chain context and resource access tags; Perform semantic-level analysis on each event to extract corresponding operation types, data read-write range, index affected segment and lock occupation mode, and generate a local state label reflecting the operation impact degree combined with the current node load; Combine the local state label with the cache state, persistent queue state and replica synchronization state of the node to form a snapshot segment that can describe the short-term behavior characteristics of the node; Splice multiple snapshot segments according to the event triggering order and correct the time sequence to form a node snapshot sequence for dynamic evaluation.

[0006] Preferably, the process of calculating the dynamic weight of the potential impact of events on the service chain is: According to the event triggering time, resource call depth and data cross-node propagation range recorded in the node snapshot sequence, identify the propagation path of the event in the service chain; Statistical the response delay, resource waiting time, lock competition count and replica synchronization offset value of each node on the propagation path to form a multi-parameter set reflecting synchronization sensitivity; According to the change trend of the multi-parameter set, build a dynamic weight generation model to increase the weight of events on high-delay nodes, high-conflict nodes and synchronization abnormal nodes, thereby forming a dynamic weight representing the degree of influence of events on the overall stability of the service chain.

[0007] Preferably, the process of forming an event risk table is: Based on the analysis of the dynamic weight of the event, detect the parallelism of events that occur at the same time or have time overlap, and quantify the resource mutual exclusion degree and operation chain conflict intensity between events; Collecting real-time occupancy and queue arrangement of shared resources, and identifying events sensitive to resource occupancy; Fusing the parallelism detection result, resource occupancy and dynamic impact weight to build a comprehensive risk score, and classifying and ranking the events according to the score to form an event risk table.

[0008] Preferably, the process of simulating the local state change after the trigger of the event in the distributed storage environment is: Taking the high-risk events in the event risk table as input, extracting the access chain between nodes, data transmission path and replica synchronization position of these events; Building a local state simulator to simulate lock contention, cache flushing, interrupt retry and replication delay, and generating a node-level state change curve; Mapping the state change curve to the node replica state, data consistency interval and operation chain dependency depth to form a structured simulation result.

[0009] Preferably, the process of recording resource consumption and operation dependency characteristics is: Comparing the node state change curve output by the local state simulator with the real-time state recorded in the node snapshot sequence at the time point level to identify nodes with state offset exceeding the threshold; For the state offset nodes, analyze the resource consumption indicators and the depth of influence range of event dependency chain; According to the node offset type, offset amplitude and dependency link topology, determine whether to constitute a key node of short-term inconsistency of service chain; Recording the key nodes and corresponding resource consumption characteristics and dependency characteristics as key node feature description.

[0010] Preferably, the process of generating a coordination strategy executed in real time in the distributed storage environment is: Based on the key node feature description, extracting the event chain, resource usage history, peak conflict interval and cross-node call relationship involved in the key node; Building a resource constraint model according to the node processing capacity, storage bandwidth and replica synchronization delay; Matching the resource constraint model with the event chain corresponding to the key node, rearranging the order of operations in the event chain that are prone to conflict, and assigning a reserved bandwidth area or a priority access window to the bandwidth-sensitive node; Generating a coordination strategy that can be executed in real time in the distributed storage system, including an event execution order table, a resource allocation instruction and a synchronization offset correction scheme.

[0011] Preferably, the process of performing real-time state correction and operation priority adjustment on the key node is: Based on the event execution order table in the coordination strategy, the operation queues of key nodes are reordered in real time, and high-conflict events are given higher scheduling priority. According to the resource allocation instructions, the bandwidth, cache and IO resources of the critical nodes are reallocated, the synchronization rhythm of the replicas is adjusted through the synchronization offset correction scheme, and compensation synchronization is performed on replicas with excessive offset. During the correction process, node response time, resource utilization, and link consistency indicators are continuously collected to generate status feedback information.

[0012] Preferably, the process of forming a state feedback loop is as follows: Based on the status feedback information, the response time, resource utilization, link consistency indicators and event completion status of key nodes during the correction process are aggregated by time slice to construct a set of feedback segments to characterize the short-term fluctuation characteristics of nodes. Perform cross-fragment correlation analysis on the feedback fragment set to identify local unstable segments caused by resource reallocation lag, priority adjustment offset or abnormal replica synchronization rhythm, and generate an offset profile reflecting the intensity of transient disturbances. The offset profile is matched with the expected execution window of the coordination strategy, and the adjustment factors that can be injected back into the offset section are extracted and written back to the node scheduling and resource management path to form a state feedback loop.

[0013] An enterprise service management system for multiple business scenarios includes: Event acquisition module: Captures operation events generated by various business modules in the distributed storage nodes in real time and generates a sequence of node snapshots; Risk assessment module: Combining node snapshot sequences, assesses the event's impact on service chain latency, resource contention, and synchronization sensitivity, and generates an event risk table; Node simulation module: For high-risk events, it simulates local state changes after the event is triggered and identifies key nodes that cause short-term inconsistencies; Strategy generation module: Integrates key nodes and operation events, and combines node processing capabilities, storage bandwidth and synchronization latency constraints to generate a coordination strategy that is executed in real time; Status feedback module: Based on the coordination strategy, it performs status correction and operation priority adjustment on key nodes, and collects node response and event completion status to form a status feedback loop.

[0014] Compared with existing technologies, the present invention provides an enterprise service management system and method for multiple business scenarios, which has the following beneficial effects: This invention achieves dynamic quantification of potential event impacts and accurate identification of high-risk events by real-time capture, fine-grained breakdown, and node snapshot sequence construction of operational events from various business modules within distributed storage nodes. By simulating local state changes after event triggering, key nodes that may cause short-term inconsistencies in the service chain can be identified in advance, and their resource consumption and operational dependencies can be recorded. Integrating key nodes and operational events, and considering node processing capabilities, storage bandwidth, and synchronization latency constraints, a coordination strategy that can be executed in real-time in a distributed storage environment is generated. This dynamically adjusts the event execution order and resource allocation, prioritizing highly sensitive operations and reducing the impact of lock contention, cache refresh latency, and replica synchronization anomalies on the system. By performing real-time state correction and operation priority adjustment on key nodes, and collecting node responses, resource usage, and event completion status, a state feedback loop is formed, enabling continuous monitoring and adaptive optimization of node behavior. This allows for timely detection of anomalies and adjustment of strategies, preventing the spread of potential conflicts and maintaining the overall stability of the service chain. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method of the present invention; Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Please refer to Figure 1 As shown in the figure, an enterprise service management method for multiple business scenarios in an embodiment of the present invention includes the following steps: S1: Real-time capture of operation events generated by various business modules in the distributed storage nodes, breaking down the events into operation types, data impact scope and involved resources, forming a node snapshot sequence to depict the state of each node at a specific moment and potential operational impact.

[0018] The process of forming a node snapshot sequence in S1 is as follows: Within the distributed storage nodes, operation events from different business modules are aligned using a unified timestamp, and the event sources are grouped based on event identifiers, call chain contexts, and resource access tags. The time base of each node is calibrated using network time calibration or a local correction mechanism based on logical time to keep the time error between nodes within a configurable range. After an event arrives, it is processed by time window. Within each time window, events with similar timestamps are considered as the same time slice and sorted according to the triggering order. At the same time, standardized metadata items are extracted from each event, such as event identifiers, business identifiers, call chain information, instance identifiers, local timestamps, and global time stamps. Based on this metadata, events are grouped using a rule engine or similarity discrimination mechanism. The grouping criteria include, but are not limited to, the same call chain identifier, the same target data partition, shared lock objects, or similar resource access targets. For each event, semantic-level parsing is performed to extract the corresponding operation type, data read / write range, index-affected segment, and lock holding mode. A local state label reflecting the impact of the operation is generated based on the node's current load. An extensible rule base is used to match and extract parameters from the event text or structured description, identifying whether the event belongs to a read, write, metadata modification, or transaction boundary operation. Further parsing reveals the target data table or partition, key-value range or shard identifier, estimated data read / write volume, and potential index or range scans. The type of lock the event may occupy and the estimated lock holding duration are assessed. The parsed elements are then integrated with the node's real-time monitoring metrics, such as CPU utilization, memory usage, current I / O queue depth, number of active threads, and local transaction queue length. Based on this integrated information, a local state label is generated for the event. This label represents the impact intensity of the event on the node in the form of a level or score, and simultaneously records the parsing confidence, rule version, and generation time. Local state labels are combined with the node's cache state, persistent queue state, and replica synchronization state to form snapshot fragments that describe the short-term behavioral characteristics of the node. Each local state label is combined with a set of real-time collected node performance metrics, including cache hit rate and cache usage, length and latency of write buffer or persistent queue, number of pending transactions, statistics of disk operation latency, and synchronization lag and unacknowledged operation counts with other replicas. This information is encapsulated in the form of structured records as snapshot fragments, and each fragment is appended with a time window start and end identifier, source node identifier, and integrity check code. To support efficient storage and retrieval, snapshot fragments are stored locally using binary serialization and written to a short-term buffer, while being asynchronously reported to a centralized time-series storage or a local queryable repository. An index entry is maintained for each fragment to enable fast retrieval by time, node, or event grouping. Multiple snapshot segments are spliced ​​and time-series corrected according to the event triggering order to form a node snapshot sequence for dynamic evaluation. The segments are spliced ​​by sequence number using the event triggering time as the primary sequence key. At the same time, the timestamps of each segment are offset and corrected using previously recorded time correction information to eliminate pseudo-out-of-order errors caused by clock differences between nodes. To handle late or out-of-order snapshot segments, a short-term buffering waiting mechanism is adopted to wait for late segments to arrive within the buffer period. Segments that exceed the buffer period are still retained but marked as out-of-order. After splicing, the granularity of the sequence is adjusted. The original fine granularity is maintained in the event-intensive area for accurate analysis, while time aggregation is used in the idle area to reduce storage and computational overhead. The resulting node snapshot sequence contains a continuous timeline, integrity verification information for each segment, a list of source nodes, and sequence version information, and is persisted to a queryable time-series repository, providing an external read interface for real-time access by the risk assessment, simulation, and coordination modules.

[0019] S2: Based on the node snapshot sequence, combined with the event's latency, resource contention, and synchronization sensitivity to the overall service chain, calculate the dynamic weight of the event's potential impact on the service chain, and incorporate the parallel operations between nodes and the shared resource usage into the comprehensive assessment to form an event risk table.

[0020] The process of calculating the dynamic weights of an event's potential impact on the service chain in S2 is as follows: Based on the event trigger time, resource call depth, and data propagation range across nodes recorded in the node snapshot sequence, the propagation path of the event in the service chain is identified. The trigger time, associated business module, and corresponding data access range of each event are extracted from the node snapshot sequence. Combined with the dependencies between events, possible transmission links between events are constructed. When determining the links, the shared resources involved in the event, such as data shards, caches, or transaction lock objects, as well as the indirect impact of the event on other nodes, are considered. The event propagation path diagram is generated using link mapping rules to clarify the nodes, operation sequences, and estimated arrival times that the event may affect in the service chain in sequence. Each node in the path is assigned a node identifier, trigger time, and impact range. The system collects and analyzes the response latency, resource waiting time, lock contention count, and replica synchronization offset of each node along the propagation path to form a multi-parameter set reflecting synchronization sensitivity. At each node, the system collects and analyzes the response latency of event processing, including the time required from the arrival of the event to the completion of processing. It also records the time the event waits in the queue or buffer, as well as the number of blocking events caused by lock or resource contention during the operation. The system continuously monitors the data synchronization status between replicas involved in the event, including synchronization latency, the number of unconfirmed operations, and data version offset. The above indicators are organized by node to form a multi-parameter set, which includes latency indicators, resource contention indicators, and synchronization status indicators. Each indicator in the set records the timestamp, source node, and measurement accuracy. Based on the changing trends of the multi-parameter set, a dynamic weight generation model is constructed to increase the weight of events on high-latency nodes, high-conflict nodes, and synchronization anomaly nodes, thereby forming dynamic weights that characterize the degree of impact of events on the overall stability of the service chain. The multi-parameter set is processed using time series analysis methods to identify peaks in node response time and resource utilization, abnormal increases in lock conflict frequency, or sudden increases in replica synchronization lag. Based on these trends, dynamic weights are assigned to each event, so that the weights of events with a large impact on the service chain are automatically increased. At the same time, the cumulative effect of event propagation across nodes and the cumulative risk of events at critical nodes are considered. The dynamic weight generation model comprehensively evaluates the latency, conflict, and synchronization anomaly factors between nodes and adjusts the weights according to the position of the event on the propagation path to form a numerical value or level that can truly reflect the impact of events on the overall stability of the service chain.

[0021] The process of creating the event risk table in S2 is as follows: Based on the analysis of event dynamic weights, parallelism detection is performed on events that occur simultaneously or have overlapping time periods to quantify the degree of resource mutual exclusion and the intensity of operation chain conflicts between events. According to the node snapshot sequence and event dynamic weights, events are grouped by time windows to identify the set of events that may be executed in parallel within the same time period. For each event set, the shared resources, data partitions and lock objects involved in each event are analyzed to determine the operation mutual exclusion between events and the possible execution conflicts. At the same time, the upstream and downstream dependencies of events in the service chain are considered to assess the impact of parallel execution of events on the chain order, data consistency and node load. These analysis results are quantified into comparable conflict intensity indicators. The system collects real-time resource occupancy and queue arrangement data, and identifies resource-sensitive events. During event execution, it monitors the CPU, memory, storage, and network resource usage of each node, as well as the length and arrangement order of each operation queue, transaction buffer, or pending task queue. Events executed on high-occupancy or bottleneck resources are identified and marked as resource-sensitive events to reflect their potential impact on the overall service chain. During the collection process, it also records resource occupancy trends, peak periods, and queue congestion, generating a traceable resource occupancy record for each event to accurately reflect the resource impact characteristics of the event in the risk table. The parallelism detection results, resource utilization rate, and dynamic impact weights are integrated to construct a comprehensive risk score. Events are then risk-classified and ranked based on the score, forming an event risk table. Through a scoring model, the dynamic weight, parallel conflict intensity, and resource sensitivity indicators of each event are comprehensively calculated to generate a unified risk score. A higher score indicates a greater potential impact on the service chain. Events are classified into risk levels (e.g., high risk, medium risk, low risk) based on their scores, and then ranked within the same level according to time sequence or scope of impact. Event identifiers, risk scores, risk levels, involved nodes, and related resource information are integrated to form a structured event risk table for real-time access and use by the scheduling, coordination, and intervention modules. The scoring calculation basis and parameter versions are also retained in the records to ensure the traceability and verifiability of the risk table.

[0022] S3: For high-risk events and corresponding nodes, simulate the local state changes that occur in the distributed storage environment after the event is triggered. By comparing the simulation results with real-time snapshots, identify the key nodes that cause short-term inconsistencies in the service chain and record resource consumption and operation dependency characteristics.

[0023] The process of local state changes that occur in a distributed storage environment after a simulated event is triggered in S3 is as follows: Using high-risk events from the event risk table as input, we extract the access chains, data transmission paths, and replica synchronization locations of these events between nodes. We then filter out events identified as high-risk from the event risk table, obtain the set of nodes involved in each event and the corresponding operation sequence, and construct the access chains between nodes based on the event trigger time and data access range recorded in the node snapshot sequence. This clarifies the order of the event's impact on each node and the possible data transmission paths, identifies the storage location and synchronization relationship of the data replicas involved in the event in the distributed storage environment, and provides complete path and structure information for simulating local state changes after the event is triggered, ensuring that the simulation can cover key nodes and key data operations. A local state simulator is constructed to simulate lock contention, cache refresh, interruption retries, and replication latency, generating node-level state change curves. In the local state simulator, each high-risk event is applied to each node step by step according to the access chain. The processing queue, cache state, and lock resources of each node are dynamically simulated. During the simulation, the lock contention and waiting latency caused by the event, the data access latency caused by cache refresh, the fluctuation of node load due to operation interruption and retries, and the impact of replica synchronization latency on data consistency are considered. The processing status, response latency, and resource consumption of each node before and after the event are recorded in a time series manner to generate node-level state change curves, reflecting the immediate impact of the event on the performance and consistency of the local system. The state change curves are mapped to node replica states, data consistency intervals, and operation chain dependency depths to form structured simulation results. Each node-level state change curve is analyzed and converted into specific state indicators for node replicas, such as whether the data is readable, whether there are unsynchronized changes or consistency delays. The state changes are combined with the dependency depth of events in the operation chain to identify the affected operation sequences and potential conflict intervals, forming data consistency interval information. The node states, event dependencies, data consistency intervals, and resource usage are integrated to generate structured simulation result files or data objects.

[0024] The process of recording resource consumption and operation dependency characteristics in S3 is as follows: The node state change curves output by the local state simulator are compared with the real-time states recorded in the node snapshot sequence at a time point level to identify nodes whose state deviation exceeds a threshold. The state change curves of each node generated by the simulator during the event triggering process are compared with the actual states of the corresponding nodes recorded in the node snapshot sequence at time points. By analyzing the differences in response latency, processing queue length, cache update status, and replica synchronization status, nodes that deviate from the normal behavior range at specific time points are identified. Slight fluctuations and significant anomalies are distinguished according to preset deviation thresholds to ensure that the analysis only targets nodes that may affect the consistency of the service chain, thereby locating potential risk points. For state offset nodes, analyze resource consumption metrics and the depth and scope of impact of event dependency chains; for identified state offset nodes, collect information on their resource usage during simulation, including CPU, memory, storage bandwidth, network transmission, and lock usage, and analyze the dependencies between nodes. Evaluate the cascading impact of the offset node on other nodes and operation chains. By analyzing the position, depth of impact, and propagation range of the offset node in the event dependency chain, it is possible to determine the service chain interruption, operation blockage, or data consistency deviation that the node anomaly may cause. Based on the node offset type, offset magnitude, and dependent link topology, determine whether a node constitutes a critical node for short-term inconsistency in the service chain. Conduct a comprehensive analysis of the type of state offset node (e.g., delay, failure, or synchronization anomaly), offset magnitude (the value or time difference deviating from the standard state), and the topology of the event dependency chain. By evaluating the node's operational weight in the service chain, the number of dependent nodes, and the sensitivity of downstream nodes, determine whether the node offset will lead to data inconsistency, operational blockage, or process out-of-order issues in the service chain within a short period. Only nodes that meet the conditions for short-term inconsistency impact are marked as critical nodes. Key nodes and their corresponding resource consumption and dependency characteristics are recorded as key node feature descriptions. The node identifier, resource usage details (including CPU usage, memory usage, storage and network resource consumption), lock and queue usage, as well as its upstream and downstream relationships and influence depth in the event dependency chain of each identified key node are organized into structured feature records. The feature descriptions include timestamps, node state change ranges, key event identifiers and dependency strengths, forming a complete, traceable key node data set for coordination strategy calculation and state correction.

[0025] S4: Integrates key nodes and operational events, and dynamically adjusts the execution order of events and resource allocation based on node processing capabilities, storage bandwidth, and synchronization latency constraints, generating a coordination strategy for real-time execution in a distributed storage environment.

[0026] The process of generating a coordination strategy in S4 that is executed in real time in a distributed storage environment is as follows: Based on the key node feature description, the event chain, resource usage history, peak conflict intervals, and cross-node call relationships involved in the key nodes are extracted; the operation events related to each key node and their triggering order are read from the key node feature description, and the resource consumption of these events at different time periods, including CPU, memory, storage bandwidth, and lock usage, are analyzed. High conflict time windows and cross-node call relationships that occur in historical execution are identified, and the dependency paths and possible conflict chains of each key event between nodes are clarified, thus forming a complete event chain and resource usage background. A resource constraint model is constructed based on node processing capacity, storage bandwidth, and replica synchronization latency. The processing capacity of each key node, including computing throughput and concurrent processing capacity, storage bandwidth limit, and maximum latency range of replica synchronization, is integrated into a quantifiable resource constraint model. In the model, the availability and constraints of each type of resource are labeled, including peak period limits, inter-node bandwidth allocation, cache capacity, and lock contention threshold. The model is used to accurately describe the range of resources that a node can support under multi-event concurrent operations. The resource constraint model is matched with the event chain corresponding to the key nodes. Operations in the event chain that are prone to conflict are rearranged. Bandwidth-sensitive nodes are assigned reserved bandwidth areas or priority access windows. Based on the execution order, resource requirements and dependencies of each event in the event chain, high-risk events that may cause node competition or operation blocking are identified. For these events, the execution order is adjusted, buffer time is inserted or priority resource windows are allocated to ensure that key operations can be completed smoothly according to priority. For bandwidth-sensitive nodes, a certain proportion of bandwidth is allocated or access priority is set to avoid data transmission bottlenecks affecting the stability of the overall service chain. Generate a coordination strategy that can be executed in real time within the distributed storage system, including an event execution sequence table, resource allocation instructions, and synchronization offset correction schemes. Integrate the event chain after sequence optimization and resource matching with constraints to form a specific executable coordination strategy. The triggering order of each event is specified in the form of an event execution sequence table, and the bandwidth, cache, and lock allocation of each node in the form of resource allocation instructions are used to indicate the allocation status of each node in different time periods. It also includes a correction scheme for replica synchronization offsets to ensure that data replicas between nodes remain consistent. The generated coordination strategy can be read and executed immediately, realizing the dynamic stability and efficient operation of the service chain in multiple business scenarios.

[0027] The process of performing real-time state correction and operation priority adjustment for critical nodes in S4 is as follows: Based on the event execution order table in the coordination strategy, the operation queues of key nodes are reordered in real time, and high-conflict events are given higher scheduling priority. The event execution order table generated by the coordination strategy is read, the current operation queues of key nodes are analyzed, and the conflict risk, resource consumption requirements and dependencies of each operation event are evaluated. For high-conflict and high-impact events, their priority is dynamically increased, and the execution order of events in the queue is adjusted in real time so that key operations are processed in the shortest possible time, while ensuring that the sequential dependencies between operations are not broken, thereby reducing service chain delays or state inconsistencies caused by event conflicts. According to the resource allocation instructions, the bandwidth, cache, and I / O resources of key nodes are reallocated. The synchronization rhythm of replicas is adjusted through the synchronization offset correction scheme, and compensatory synchronization is performed on replicas with excessive offset. According to the resource allocation instructions in the coordination strategy, various resources of key nodes are dynamically adjusted, including allocating additional bandwidth, cache space, and input / output channels for high-priority operations. The synchronization status of replica data between nodes is monitored. For replicas that deviate from the normal synchronization rhythm, rate limiting, priority synchronization, or compensatory synchronization operations are performed to ensure that the data of each node is restored to consistency in a short time, reduce the impact of replica latency on business operations, and ensure the data consistency of the entire distributed service chain. During the correction process, node response time, resource utilization, and link consistency indicators are continuously collected to form status feedback information. During event reordering and resource adjustment, the operation response time, bandwidth and cache utilization, input / output channel utilization, and synchronization consistency of links between nodes are monitored in real time for each key node. The collected indicators are integrated and analyzed to form complete status feedback information, which is used to evaluate the immediate effect of the adjustment strategy, provide real-time basis for the optimization of the coordination strategy, and support the prediction and dynamic intervention of future high-conflict events, thereby achieving the continuous and stable operation of the service chain in a distributed environment.

[0028] S5: Through the generated coordination strategy, perform real-time status correction and operation priority adjustment on key nodes, collect node response and event completion status, and form a status feedback loop.

[0029] The process of forming a state feedback loop in S5 is as follows: Based on status feedback information, the response time, resource utilization, link consistency indicators, and event completion status of key nodes during the correction process are aggregated by time slice to construct a set of feedback segments to characterize the short-term fluctuation characteristics of nodes. Various indicator data generated by key nodes during real-time status correction and operation priority adjustment are collected and aggregated according to a unified time slice. The response latency, bandwidth utilization, cache utilization, input / output channel load, and replica synchronization status within each time slice are integrated to form a feedback segment that reflects the overall behavioral characteristics of the node within that time period, ensuring that short-term fluctuations and transient events can be fully captured. Cross-segment correlation analysis is performed on the feedback segment set to identify local unstable segments caused by resource reallocation lag, priority adjustment offset, or abnormal replica synchronization rhythm, and an offset profile reflecting the intensity of transient disturbances is generated. Feedback segments from multiple time slices are compared according to the event triggering order and resource usage correlation to analyze the changing trends and abnormal peaks of key indicators within continuous time periods. Nodes with increased response latency, bandwidth allocation conflicts, or replica synchronization inconsistencies are marked to form local unstable segments, and their disturbance intensity is quantified to generate an offset profile, indicating the risk level of service chain inconsistency that nodes may cause in a short period of time. The offset profile is matched with the expected execution window of the coordination strategy. Adjustable factors that can be injected back into the deviation segment are extracted and written back to the node scheduling and resource management path to form a state feedback loop. The generated offset profile is compared with the expected execution time window and resource allocation plan of each event in the original coordination strategy to identify the events and nodes that deviate from the execution. The corresponding resource adjustment amount, priority correction magnitude and synchronization compensation suggestions are written back to the scheduling and resource management path as adjustment factors. In the next round of event scheduling and resource allocation, the deviation can be automatically corrected, thereby forming a state feedback loop and realizing continuous monitoring and dynamic optimization of the behavior of key nodes in the distributed storage environment.

[0030] Example 2: As Figure 2 As shown, an enterprise service management system for multiple business scenarios includes: Event acquisition module: Captures operation events generated by various business modules in the distributed storage nodes in real time and generates a sequence of node snapshots; Risk assessment module: Combining node snapshot sequences, assesses the event's impact on service chain latency, resource contention, and synchronization sensitivity, and generates an event risk table; Node simulation module: For high-risk events, it simulates local state changes after the event is triggered and identifies key nodes that cause short-term inconsistencies; Strategy generation module: Integrates key nodes and operation events, and combines node processing capabilities, storage bandwidth and synchronization latency constraints to generate a coordination strategy that is executed in real time; Status feedback module: Based on the coordination strategy, it performs status correction and operation priority adjustment on key nodes, and collects node response and event completion status to form a status feedback loop.

[0031] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for enterprise service management oriented towards multiple business scenarios, characterized in that, Includes the following steps: Real-time capture of operational events generated by various business modules in distributed storage nodes; decomposition of events into operation type, data impact scope and involved resources; formation of node snapshot sequence; characterization of the state of each node at a specific moment and potential operational impact. Based on the node snapshot sequence, and combined with the event's latency, resource contention, and synchronization sensitivity to the overall service chain, the dynamic weight of the event's potential impact on the service chain is calculated. The parallel operation and shared resource usage between nodes are included in the comprehensive assessment to form an event risk table. For high-risk events and corresponding nodes, simulate the local state changes that occur in the distributed storage environment after the event is triggered. By comparing the simulation results with real-time snapshots, identify the key nodes that cause short-term inconsistencies in the service chain and record resource consumption and operation dependency characteristics. By integrating key nodes and operational events, and combining node processing capabilities, storage bandwidth, and synchronization latency constraints, the execution order of events and resource allocation are dynamically adjusted to generate a coordination strategy for real-time execution in a distributed storage environment. The generated coordination strategy performs real-time status correction and operation priority adjustment on key nodes, collects node responses and event completion status, and forms a status feedback loop.

2. The enterprise service management method for multiple business scenarios according to claim 1, characterized in that, The process of forming a node snapshot sequence is as follows: Within the distributed storage node, operation events from different business modules are aligned using a unified timestamp, and the event sources are grouped based on event identifiers, call chain contexts, and resource access tags. For each event, perform semantic-level parsing to extract the corresponding operation type, data read / write range, index affected area, and lock holding mode, and combine it with the node's current load to generate local state labels that reflect the degree of operation impact; The local state label is combined with the node's cache state, persistent queue state, and replica synchronization state to form a snapshot fragment that can describe the short-term behavioral characteristics of the node. Multiple snapshot segments are spliced ​​and time-series corrected according to the event triggering order to form a node snapshot sequence for dynamic evaluation.

3. The enterprise service management method for multiple business scenarios according to claim 2, characterized in that, The process of calculating the dynamic weights of an event's potential impact on the service chain is as follows: Based on the event trigger time, resource call depth, and data propagation range across nodes recorded in the node snapshot sequence, identify the propagation path of the event in the service chain; The response delay, resource waiting time, lock contention count, and replica synchronization offset of each node along the propagation path are statistically analyzed to form a multi-parameter set reflecting synchronization sensitivity. Based on the changing trends of the multi-parameter set, a dynamic weight generation model is constructed to increase the weight of events on high-latency nodes, high-conflict nodes, and synchronization-abnormal nodes, thereby forming dynamic weights that characterize the degree of impact of events on the overall stability of the service chain.

4. The enterprise service management method for multiple business scenarios according to claim 3, characterized in that, The process of creating an event risk table is as follows: Based on the analysis of dynamic weights of events, parallelism detection is performed on events that occur simultaneously or have overlapping time periods to quantify the degree of resource mutual exclusion and the intensity of operation chain conflicts between events. Collect real-time occupancy rate and queue arrangement of shared resources, and identify events that are sensitive to resource occupancy. The parallelism detection results, resource utilization rate, and dynamic impact weight are integrated to construct a comprehensive risk score. Based on the score, the events are classified and ranked according to risk, and the ranked events and risk levels are combined to form an event risk table.

5. The enterprise service management method for multiple business scenarios according to claim 4, characterized in that, The process of local state changes that occur in a distributed storage environment after a simulated event is triggered is as follows: Using high-risk events in the event risk table as input, extract the access chain, data transmission path and replica synchronization location of these events between nodes; A local state simulator is built to simulate lock contention, cache refresh, interrupt retry and replication delay, and generate node-level state change curves. The state change curves are mapped to the node replica states, data consistency intervals, and operation chain dependency depths to form structured simulation results.

6. The enterprise service management method for multiple business scenarios according to claim 5, characterized in that, The process of recording resource consumption and operation dependency characteristics is as follows: The node state change curves output by the local state simulator are compared with the real-time states recorded in the node snapshot sequence at the time point level to identify nodes whose state deviation exceeds the threshold. For state offset nodes, analyze resource consumption metrics and the depth and scope of influence of event dependency chains; Based on the node offset type, offset magnitude, and dependent link topology, determine whether it constitutes a critical node for short-term inconsistency in the service chain; Key nodes and their corresponding resource consumption and dependency characteristics are recorded as key node feature descriptions.

7. The enterprise service management method for multiple business scenarios according to claim 6, characterized in that, The process of generating a coordination strategy for real-time execution in a distributed storage environment is as follows: Based on the feature description of key nodes, extract the event chain, resource usage history, peak conflict interval and cross-node call relationship involved in the key nodes; A resource constraint model is constructed based on node processing capacity, storage bandwidth, and replica synchronization latency. Match the resource constraint model with the event chain corresponding to the key node, rearrange the order of operations that are prone to conflict in the event chain, and assign reserved bandwidth area or priority access window to bandwidth sensitive node. Generate coordination strategies that can be executed in real time within the distributed storage system, including event execution order tables, resource allocation instructions, and synchronization offset correction schemes.

8. The enterprise service management method for multiple business scenarios according to claim 7, characterized in that, The process of performing real-time status correction and operation priority adjustment for critical nodes is as follows: Based on the event execution order table in the coordination strategy, the operation queues of key nodes are reordered in real time, and high-conflict events are given higher scheduling priority. According to the resource allocation instructions, the bandwidth, cache and IO resources of the critical nodes are reallocated, the synchronization rhythm of the replicas is adjusted through the synchronization offset correction scheme, and compensation synchronization is performed on replicas with excessive offset. During the correction process, node response time, resource utilization, and link consistency indicators are continuously collected to generate status feedback information.

9. The enterprise service management method for multiple business scenarios according to claim 8, characterized in that, The process of forming a state feedback loop is as follows: Based on the status feedback information, the response time, resource utilization, link consistency indicators and event completion status of key nodes during the correction process are aggregated by time slice to construct a set of feedback segments to characterize the short-term fluctuation characteristics of nodes. Perform cross-fragment correlation analysis on the feedback fragment set to identify local unstable segments caused by resource reallocation lag, priority adjustment offset or abnormal replica synchronization rhythm, and generate an offset profile reflecting the intensity of transient disturbances. The offset profile is matched with the expected execution window of the coordination strategy, and the adjustment factors that can be injected back into the offset section are extracted and written back to the node scheduling and resource management path to form a state feedback loop.

10. An enterprise service management system for multiple business scenarios, applied to the method described in any one of claims 1-9, characterized in that, include: Event acquisition module: Captures operation events generated by various business modules in the distributed storage nodes in real time and generates a sequence of node snapshots; Risk assessment module: Combining node snapshot sequences, assesses the event's impact on service chain latency, resource contention, and synchronization sensitivity, and generates an event risk table; Node simulation module: For high-risk events, it simulates local state changes after the event is triggered and identifies key nodes that cause short-term inconsistencies; Strategy generation module: Integrates key nodes and operation events, and combines node processing capabilities, storage bandwidth and synchronization latency constraints to generate a coordination strategy that is executed in real time; Status feedback module: Based on the coordination strategy, it performs status correction and operation priority adjustment on key nodes, and collects node response and event completion status to form a status feedback loop.

Images