A service early warning method, device, equipment and medium

Abstract

The application discloses a service early warning method, device, equipment and medium. The method comprises the following steps: in a current period, obtaining first service data of a first database in a previous period of the current period, the first database storing data associated with multiple service processes; for each service node, updating the alarm threshold of each service node for the previous period according to the first service data to obtain the alarm threshold of each service node for the current period; in response to data change of a second database, synchronizing the changed second service data in the second database to the first database; determining the monitoring value of each early warning dimension according to the changed second service data; when the monitoring value of the early warning dimension exceeds the alarm threshold of the early warning dimension in the current period, triggering service early warning. The embodiment of the application can improve the service management and disposal efficiency.

Description

Technical Field

[0001] This invention relates to the field of Internet technology, and in particular to a business early warning method, apparatus, device, and medium. Background Technology

[0002] In modern production operation management, once a business is initiated at the counter, it is broken down into tasks according to a preset process and assigned to the corresponding personnel for processing. To ensure operational efficiency and avoid task backlog, it is necessary to monitor and dynamically adjust business flow, task allocation, and personnel status in real time, thereby achieving refined management.

[0003] Currently, common practices in the industry include: first, collecting monitoring data and inputting it into control and prediction models for early warning; second, recording task completion status, performing basic progress statistics, and using threshold judgments for alarms. However, these methods have certain limitations in practical applications. Prediction models mainly rely on predicted values ​​for judgment, making it difficult to perceive the actual backlog of certain tasks or the exceeding of processing time limits in real time, which may lead to false alarms; while threshold judgment methods lack clear criteria for setting alarm thresholds, making it difficult to meet the actual needs of refined management of production operations. Summary of the Invention

[0004] This invention provides a business early warning method, apparatus, equipment, and medium that can improve business management and processing efficiency.

[0005] According to one aspect of the present invention, an embodiment of the present invention provides a business early warning method, the method comprising: In the current cycle, the first business data of the first database in the previous cycle of the current cycle is obtained. The first database stores data related to multiple business processes, and the business process is configured with at least one business node. For each of the aforementioned business nodes, based on the first business data, the alarm threshold for each of the aforementioned business nodes for the previous period is updated to obtain the alarm threshold for each of the aforementioned business nodes for the current period, wherein the alarm threshold has at least one warning dimension. In response to data changes in the second database, the changed second business data in the second database is synchronized to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business; The monitoring values ​​for each of the aforementioned early warning dimensions are determined based on the changed second business data; A business alert is triggered when the monitored value of the aforementioned alert dimension exceeds the alarm threshold of the aforementioned alert dimension for the current period.

[0006] According to another aspect of the present invention, embodiments of the present invention also provide a business early warning device, the device comprising: The batch data acquisition module is used to acquire the first business data of the first database in the previous period of the current period in the current period. The first database stores data related to multiple business processes, and the business process is configured with at least one business node. An alarm threshold setting module is used to update the alarm threshold of each of the aforementioned business nodes for the previous period based on the first business data, so as to obtain the alarm threshold of each of the aforementioned business nodes for the current period, wherein the alarm threshold has at least one warning dimension. The real-time data acquisition module is used to respond to data changes in the second database and synchronize the changed second business data in the second database to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business; The monitoring indicator determination module is used to determine the monitoring values ​​of each of the early warning dimensions based on the changed second business data; The comparison and early warning module is used to trigger a business early warning when the monitored value of the early warning dimension exceeds the alarm threshold of the early warning dimension in the current period.

[0007] According to another aspect of the present invention, embodiments of the present invention also provide a service early warning device, the service early warning device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to execute the business warning method of any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the service early warning method of any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the business early warning method described in any embodiment of the present invention.

[0010] The technical solution of this invention updates the alarm threshold at the beginning of the current cycle based on the first business data generated in the previous cycle, making the alarm threshold setting more reasonable and in line with recent business conditions. It also obtains the second business data of the second database in real time as business requests in the current cycle are received, that is, the current business situation is obtained, and the monitoring value is calculated. When the monitoring value is greater than the alarm threshold, an early warning is triggered. The early warning can be triggered in a timely manner according to the real-time business situation, which meets the actual needs of refined business control, improves business management and processing efficiency, and can dynamically adjust business allocation in a timely manner in response to business early warnings.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a business early warning method provided according to an embodiment of the present invention; Figure 2 This is a flowchart of a business early warning method provided according to an embodiment of the present invention; Figure 3 This is a structural diagram of a business early warning device provided according to an embodiment of the present invention; Figure 4 This is a structural schematic diagram of a business early warning device provided in an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] In the technical solutions of the embodiments of the present invention, the acquisition, storage and application of business-related data, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0017] Figure 1 This is a flowchart illustrating a business early warning method provided by an embodiment of the present invention. This embodiment is applicable to situations requiring monitoring and early warning of daily business processing progress. The method can be executed by a business early warning device, which can be implemented in hardware and / or software. This business early warning device can be configured in a server.

[0018] See Figure 1 The business early warning methods shown include: S101. In the current cycle, obtain the first business data of the first database in the previous cycle of the current cycle. The first database stores data related to multiple business processes, and the business process is configured with at least one business node.

[0019] Here, "current period" can refer to the current business cycle. "Previous period" can refer to the business cycle preceding the current one. "First database" can refer to a database used for monitoring business processes, storing data related to multiple business processes. "Business process" can refer to a series of ordered operation procedures set to achieve a specific work objective. "Business node" can refer to a node in a business process. "First business data" can refer to all batches of business data generated in the previous period.

[0020] Business data is analyzed and processed periodically, for example, on a daily basis, with the business data analyzed and processed once per day.

[0021] At the start of the current cycle, all business data generated in the previous cycle is first retrieved. For example, the data collection process is automatically woken up at a preset trigger time in the early morning of each day, the current date is obtained, and all business data from the previous day is retrieved from the first database using the date of the day before the current date as the filter condition.

[0022] Business data can be data associated with business processes. For example, business data includes, but is not limited to: business identifier, operator, business node, and creation time.

[0023] A business process contains at least one business node. For example, a business process may include four business nodes: information entry, information review, risk assessment, and decision-making.

[0024] In an optional embodiment, the step of obtaining the first service data of the first database in the previous period of the current period in the current period includes: obtaining the source service data of the second database in the previous period of the current period in the current period; calibrating the first service data of the first database in the previous period of the current period based on the source service data; and obtaining the calibrated first service data.

[0025] The second database can be a database that stores data related to multiple business processes for business processing. The source business data can be business data directly written in response to business requests.

[0026] The business process is initiated by the business processing system, which directly writes the associated data into the second database using Structured Query Language. The first database acts as a replica of the second database, responding to changes in the data in the first database by writing business data accordingly.

[0027] At the start of the current cycle, all source business data from the previous cycle is retrieved from the second database. This source business data is the original data written in response to business requests. The first business data in the first database is then compared and corrected based on this source business data. This first business data in the first database is obtained in real-time through synchronization with the second database in response to data changes in the previous cycle. Essentially, this comparison and correction of the first business data in the first database based on the source business data involves calibrating the real-time synchronized business data from the first database in the previous cycle using a batch of original data from the second database from the previous cycle.

[0028] It is evident that by calibrating the first business data based on the source business data of the second database in the previous period, the accuracy and integrity of the first business data are ensured. Updating the alarm threshold based on the calibrated first business data makes the alarm threshold setting more objective and reasonable.

[0029] S102. For each of the aforementioned business nodes, based on the first business data, the alarm threshold of each of the aforementioned business nodes for the previous period is updated to obtain the alarm threshold of each of the aforementioned business nodes for the current period, wherein the alarm threshold has at least one warning dimension.

[0030] Here, the alarm threshold can refer to the critical value used to trigger an alarm. The alert dimension can be the dimension for setting alarm alerts.

[0031] Different business nodes handle different business operations. For example, the number of business operations handled by the order placement node may be half that of the order fulfillment node. If alarm thresholds are set for each business process, and multiple business nodes use the same alarm threshold, some business nodes may frequently issue alarms, while others may never issue alarms, thus rendering business alerts meaningless.

[0032] For each business node, each business node can adjust its alarm threshold for the current period based on its actual performance in the previous period, so that the alarm standard is adapted to the business volume and pace of each business node.

[0033] In addition, when the business process changes, such as adding a new verification step, you only need to add the alarm threshold for the newly added business node, and the alarm threshold settings for other nodes will not be affected.

[0034] Alarm thresholds can cover multiple measurement dimensions simultaneously, such as monitoring both total processing volume and processing time or number of errors. At least one appropriate alarm threshold for each business node can be set based on its actual business operations to reduce false alarms and missed alarms.

[0035] S103. In response to data changes in the second database, synchronize the changed second business data in the second database to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business.

[0036] The second business data can be business data generated in real time during the current period as business activities continue.

[0037] The first database is a monitoring database specifically used to store data related to multiple business processes. The second database is a runtime database used to process business requests and record business data in real time.

[0038] In the business system, the second database needs to respond to business requests at high speed and perform frequent read and write operations. If it also handles complex data query and analysis tasks, it will slow down the business response speed. Therefore, the second business data generated in the second database is copied to the first database in real time, allowing all query and analysis work to be performed on the first database. The first database acts as a backup copy, does not participate in business processing, and is only responsible for providing data support for monitoring business volume and issuing alerts. When data is added, modified, or deleted in the second database, the first database completes the corresponding synchronous replication of the changes in a very short time, ensuring that the data in the two databases remains consistent.

[0039] In one specific embodiment, a change data capture component is deployed on the second database. This component continuously monitors the insert, update, and delete operation logs of all business data tables. When a data change event is captured, the component immediately extracts the primary key identifier of the changed record and the complete data row after the change. The extracted change data is then encapsulated into a synchronization message and sent to the synchronization receiving service of the first database via an internal network channel. After parsing the message content, the receiving service of the first database performs the corresponding write operation according to the change type: inserting a record for a new change, updating an existing record for a modification, and removing the corresponding record for a deletion.

[0040] For example, when data changes occur in the second database, the changed second business data is collected through the Data Replication Service (DRS). The real-time collected second business data is pushed to the business monitoring system used for business monitoring through the message queue of the data lake's distributed message queue system (Apache Kafka, Kafka). The business monitoring system deploys multiple Kafka microservices and uses a distributed approach to receive real-time data, avoiding data congestion and single point of failure on a single node.

[0041] Optionally, the data acquisition method can be switched according to the system configuration. When DRS acquisition encounters an anomaly or is not applicable to the current business scenario, the data can be acquired by switching to the Change Data Loader (CDL) to ensure the continuity of real-time data acquisition.

[0042] In an optional embodiment, the step of synchronizing the changed second business data in the second database to the first database in response to a data change in the second database includes: in response to a data change in the second database, obtaining the changed second business data in the second database; determining whether the preceding business data on which the second business data depends has been synchronized to the first database, and obtaining a determination result; when the determination result is yes, synchronizing the second business data to the first database; when the determination result is no, adding the second business data to a delay queue, waiting for a target time, and then determining again whether the preceding business data on which the second business data depends has been synchronized to the first database, until the number of determinations reaches a threshold or the determination result is yes.

[0043] In this context, "preceding business data" refers to business data generated in the previous stage of the business process that precedes or depends on the current business data. For example, when modifying the business data of order A to the current second business data, the second business data used to create order A can be defined as preceding business data. The delay queue can be a waiting queue used to temporarily store second business data that cannot be processed at the moment. The count threshold is the maximum allowed number of times the same data item can be repeatedly attempted for synchronization. The target time is a fixed waiting period set before each retry.

[0044] In the business process, there are sequential dependencies between various business nodes. For example, a payment record is only generated after an order is placed, and a delivery note is only generated after payment is completed. If the payment record is synchronized to the first database before the corresponding order record has been synchronized, the payment record cannot be successfully synchronized to the first database due to the lack of the corresponding order record. By introducing a preceding business data dependency judgment mechanism, when synchronization to the first database cannot be completed smoothly, the second business data is first placed in a delay queue, waiting for the preceding business data to be synchronized before synchronization proceeds.

[0045] In one specific implementation, when the Kafka microservice receives second business data that cannot be processed, it puts it into a delayed queue and re-executes consumption after a delay of several milliseconds. If consumption still fails after 3 consecutive attempts, where consumption failure may mean that the second business data cannot be parsed and processed normally, then the second business data is discarded to avoid data accumulation affecting system performance.

[0046] It is evident that by introducing a preceding data dependency judgment and a delayed retry mechanism, the timing correctness of writing the second business data into the first database is effectively guaranteed, and data accumulation is avoided from affecting system performance.

[0047] In one specific embodiment, the acquisition of the first and second business data is achieved through a batch-streaming hybrid data acquisition architecture. Batch-streaming hybrid refers to the simultaneous support of batch data processing and streaming data processing within the same system. For the business early warning system to which this method is applied, the first business data is batch data, and the second business data is streaming data.

[0048] S104. Determine the monitoring value of each of the aforementioned early warning dimensions based on the changed second business data.

[0049] Among them, the monitored value can be a real-time value used to determine the current business situation.

[0050] The second business data, synchronized in real time, is processed. Specifically, this involves data cleaning, field extraction, and calculation of monitoring values.

[0051] In practical applications, time intervals can be set, and the monitoring values ​​of each warning dimension can be determined based on the changes in the second business data within a certain time interval.

[0052] For example, if the monitoring value is determined every 2 minutes, the monitoring value of each warning dimension can be determined based on the changes in the second business data within 2 minutes.

[0053] S105. When the monitored value of the warning dimension exceeds the alarm threshold of the warning dimension in the current period, a business warning is triggered.

[0054] The process involves comparing the monitored value with the corresponding alarm threshold for each alert dimension. An alert is triggered when the monitored value exceeds the corresponding alarm threshold.

[0055] Optionally, three alarm threshold levels can be set to obtain alarm intervals. The relationship between the monitored value and each alarm interval is determined. When the monitored value belongs to the alarm interval of a certain alarm level, an alert for the corresponding alarm level of that alarm interval is triggered.

[0056] Optionally, the monitored value can be compared with an alarm threshold. When the monitored value exceeds the alarm threshold, a business alert is triggered immediately. The business alert information includes the alert dimension represented by the monitored value, the degree of abnormality, the alarm level, and scheduling suggestions. Optionally, an alarm cancellation threshold can be set. When the monitored value falls back to the alarm cancellation threshold, the alert is cancelled, avoiding the problem of alerts persisting without manual cancellation.

[0057] The technical solution of this invention updates the alarm threshold at the beginning of the current cycle based on the first business data generated in the previous cycle, making the alarm threshold setting more reasonable and in line with recent business conditions. It also obtains the second business data of the second database in real time as business requests in the current cycle are received, that is, the current business situation is obtained, and the monitoring value is calculated. When the monitoring value is greater than the alarm threshold, an early warning is triggered. The early warning can be triggered in a timely manner according to the real-time business situation, which meets the actual needs of refined business control, improves business management and processing efficiency, and can dynamically adjust business allocation in a timely manner in response to business early warnings.

[0058] Figure 2 This is a flowchart illustrating a business early warning method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment updates the alarm thresholds for each business node for the previous period according to the first business data to obtain alarm thresholds for each business node for the current period. The alarm thresholds have at least one warning dimension, specifically: for each business node, based on the first business data, generating business characteristic parameters for each business node, including: peak business volume, unit processing time, average processing volume per person, and backlog baseline; updating the alarm thresholds for each warning dimension for the previous period according to the business characteristic parameters to obtain alarm thresholds for each warning dimension for each business node for the current period; the warning dimensions include: node pending processing volume warning dimension, estimated processing time warning dimension, and manpower shortage warning dimension.

[0059] It should be noted that for parts not described in detail in the embodiments of the present invention, please refer to the descriptions in other embodiments.

[0060] See Figure 2 The business early warning methods shown include: S201. In the current cycle, obtain the first business data of the first database in the previous cycle of the current cycle. The first database stores data related to multiple business processes, and the business process is configured with at least one business node.

[0061] S202. For each of the aforementioned business nodes, based on the first business data, generate business characteristic parameters for each business node, wherein the business characteristic parameters include: peak business volume, unit processing time, average processing volume per person, and backlog baseline.

[0062] Among them, the business feature parameters can be feature parameters extracted from the first business data that describe the operating status of the business nodes.

[0063] Peak traffic volume can be the highest number of backlogged transactions at a business node. Unit processing time can be the average time a business node takes to complete a single transaction. Based on the business identifier field of the first business data, the interval between the preceding and subsequent business nodes can be determined, and this interval is used as the processing time of the preceding business node. Unit processing time is the average processing time for completing a single transaction within a business node.

[0064] Average processing capacity per employee can be defined as the average number of tasks completed by a single worker within a business node over a fixed time period. Backlog baseline can be defined as the reasonable level of unfinished tasks maintained by a business node under normal conditions. "Normal conditions" refers to a state where the requirement to complete tasks on time is met, and operators do not need to work overtime.

[0065] The first business data is statistically analyzed to extract business characteristic parameters that can characterize the actual operating capabilities of business nodes. These business characteristic parameters are used as the basis for subsequent alarm threshold updates.

[0066] In an optional embodiment, the second database includes: a database of a banking business system; the business process includes: a banking business process; and the business node corresponds to multiple staff members of the same job type.

[0067] The banking system's database can be an online database used by the bank to process various business transactions. A banking business process can be a series of ordered operational steps defined by the bank for specific financial services. Multiple staff members of the same job type refer to multiple specific operators who assume the same responsibilities and operational authority within the business process.

[0068] One business node can correspond to multiple operators. The number of operators handling one business node can be determined by the operator field in the first business data, thus obtaining the average processing volume per person.

[0069] It is evident that by providing specific banking scenarios, business nodes correspond to multiple staff members of the same job type, which allows for adjustments to both business allocation and personnel allocation, adapting to the current business operation status from multiple perspectives.

[0070] S203. Based on the business characteristic parameters, update the alarm thresholds for each warning dimension of the previous period to obtain the alarm thresholds for each warning dimension of the current period for each business node; the warning dimensions include: node pending processing volume warning dimension, estimated processing time warning dimension, and manpower shortage warning dimension.

[0071] Among them, the node pending processing volume warning dimension is used to monitor the number of currently backlogged and unfinished tasks at business nodes. The estimated processing time warning dimension is used to monitor the estimated time required for business nodes to complete the current backlog of tasks. The manpower shortage warning dimension is used to monitor whether the currently available manpower at business nodes meets the workload requirements.

[0072] Different warning dimensions reflect different aspects of business processing risks at different business nodes. Excessive pending processing volume at a business node indicates backlog risk; excessively long estimated processing time indicates timeliness risk; and significant manpower shortage indicates resource insufficiency risk. These three dimensions are interconnected but their patterns of change differ. For example, a sudden surge in second-level business data might rapidly increase the pending processing volume, triggering a warning in the pending processing volume dimension. However, because the business operation might be relatively simple and easy to process, it might not trigger a warning in the estimated processing time dimension. Therefore, it is necessary to calculate and update values ​​independently for each warning dimension to accurately reflect its actual situation.

[0073] In an optional embodiment, updating the alarm thresholds for each warning dimension of the previous period based on the business characteristic parameters to obtain the alarm thresholds for each warning dimension of the current period for each of the business nodes includes: updating the alarm thresholds for the node pending processing volume warning dimension of the previous period based on the updated peak business volume and the backlog benchmark to obtain the alarm thresholds for the node pending processing volume warning dimension of the current period for each of the business nodes; updating the alarm thresholds for the expected processing time warning dimension of the previous period based on the updated peak business volume and the unit processing time to obtain the alarm thresholds for the expected processing time warning dimension of the current period for each of the business nodes; and updating the alarm thresholds for the manpower shortage warning dimension of the previous period based on the updated average processing volume per person and the backlog benchmark to obtain the alarm thresholds for the manpower shortage warning dimension of the current period for each of the business nodes.

[0074] Specifically, the update of alarm thresholds for the node pending processing volume warning dimension can be based on peak business volume and backlog baseline. Peak business volume reflects the highest value of backlogged business volume, while backlog baseline reflects the business backlog under normal conditions.

[0075] In some embodiments, the difference between the peak traffic volume and the backlog baseline can be calculated, and this difference can be multiplied by a degree coefficient to obtain an incremental value. The incremental value is then added to the backlog baseline to update the alarm threshold for the node pending processing volume warning dimension.

[0076] The alarm threshold for the expected processing time warning dimension can be updated based on peak business volume and unit processing time.

[0077] In some embodiments, the peak traffic volume can be calculated by dividing it by the unit processing time, that is, by multiplying the highest number of traffic by the unit processing time, to obtain the predicted time that may be needed to process this number of traffic. The alarm threshold for the expected processing time warning dimension is obtained by multiplying the predicted time obtained from the traffic volume peak by a fixed coefficient.

[0078] The alarm threshold for the manpower shortage warning dimension is updated based on the average processing volume per person and the backlog baseline. In some embodiments, the alarm threshold for the manpower shortage warning dimension is determined by dividing the backlog baseline by the average processing volume per person. For example, if the backlog baseline is 8 business transactions and the average processing volume per person is 2 business transactions, then the alarm threshold for the manpower shortage warning dimension can be -4. When determining the monitoring value for the manpower shortage warning dimension based on the second business data, the number of operators processing data within a certain period can be determined based on the identifier field representing the operator and the field describing the operation time, thus obtaining the actual number of personnel. To unify the comparison logic, the monitoring value for the manpower shortage warning dimension can be the inverse of the actual number of personnel.

[0079] It is evident that updating the expected processing time warning threshold by peak business volume and unit processing time can reflect the risk of processing timeout; updating the node pending processing volume warning threshold by peak business volume and backlog baseline can accurately reflect the node business congestion risk; and updating the manpower shortage warning threshold by backlog baseline and per capita processing volume can quantify whether manpower is sufficient.

[0080] S204. In response to data changes in the second database, synchronize the changed second business data in the second database to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business.

[0081] S205. Determine the monitoring value of each of the aforementioned early warning dimensions based on the changed second business data.

[0082] S206. When the monitored value of the warning dimension exceeds the alarm threshold of the warning dimension in the current period, a business warning is triggered.

[0083] In an optional embodiment, triggering a business alert when the monitored value of the alert dimension exceeds the alarm threshold of the alert dimension includes: sending a business alert to the task processing user associated with the business node when the monitored value of the alert dimension of the node's pending processing volume exceeds the alarm threshold; sending a business alert and a warning prompt to transfer subsequent task allocation to the task management user associated with the business node when the monitored value of the alert dimension of the expected processing time exceeds the alarm threshold; and sending a business alert and a warning prompt to add task processing users to the central management user associated with the business node when the monitored value of the alert dimension of the manpower shortage exceeds the alarm threshold.

[0084] Among them, task processing users are those who directly handle business processes. Task management users are those with task allocation authority. Central management users are those who can coordinate human resources.

[0085] When responding to business requests and breaking down business processes into various business nodes, if there are parallel business nodes, the business allocation can be adjusted. For example, after the business process is broken down, there is a business node a that needs to be processed by department a. When business node a has issued an alert based on the estimated processing time, the latest business of business node a can be transferred to business node A of the superior department.

[0086] When the monitored value of the pending processing volume at a node exceeds the alarm threshold, it indicates that the node has experienced a backlog of business, and frontline operators (i.e., task processing users) must be notified immediately to expedite processing. When the monitored value of the estimated processing time exceeds the alarm threshold, it indicates that the time required for the node to process the current backlog of business has exceeded the acceptable range, and management must be notified to adjust the task allocation strategy to avoid delays in the overall process. When the monitored value of the manpower shortage exceeds the alarm threshold, it indicates that the existing personnel at the node are insufficient to handle the current workload, and central management must be notified to coordinate and allocate additional manpower. These three warning dimensions correspond to different warning levels and response entities, ensuring that alarm information is accurately delivered to the corresponding decision-making level and improving emergency response efficiency.

[0087] Optionally, different views can be displayed based on different types of users: (1) Task processing user view: Displays the monitoring values ​​of business nodes related to daily business operations, and at the same time displays the warning information of the warning dimension of the node pending processing volume corresponding to the task processing user; (2) Task Management User View: Displays the monitoring values ​​of each business node in the business process, as well as the warning information of the expected processing time and the warning quantity of the node to be processed. (3) Central Management User View: Displays the monitoring values ​​of each business node in the business process and the warning information of each warning dimension.

[0088] It is evident that by sending business alerts based on the alert dimensions to the corresponding alert users, targeted measures can be taken to improve the efficiency of business processing and reduce the risk of business backlog.

[0089] The technical solution of this invention updates the alarm threshold by generating business feature parameters and updating the alarm threshold of each warning dimension according to the business feature parameters. This makes the update of the alarm threshold of each warning dimension faster, eliminating the need to separately collect second business data and calculate for each warning dimension. The alarm dimension threshold is synchronously refreshed based on the business feature parameters, which greatly reduces data processing and calculation overhead and improves the real-time performance and efficiency of threshold updates.

[0090] Figure 3 This is a schematic diagram of a business early warning device provided in an embodiment of the present invention. The present invention is applicable to situations requiring monitoring and early warning of daily business processing progress. This device can execute a business early warning method and can be implemented in hardware and / or software.

[0091] See Figure 3 The business early warning device shown includes: The batch data acquisition module 301 is used to acquire first business data from the first database in the previous period of the current period during the current period. The first database stores data associated with multiple business processes, and each business process is configured with at least one business node. The alarm threshold setting module 302 is used to update the alarm threshold of each of the business nodes for the previous period based on the first business data to obtain the alarm threshold of each of the business nodes for the current period, wherein the alarm threshold has at least one warning dimension. The real-time data acquisition module 303 is used to respond to data changes in the second database and synchronize the changed second business data in the second database to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business; The monitoring indicator determination module 304 is used to determine the monitoring values ​​of each of the early warning dimensions based on the changed second business data; The comparison warning module 305 is used to trigger a business warning when the monitored value of the warning dimension exceeds the alarm threshold of the warning dimension in the current period.

[0092] The technical solution of this invention updates the alarm threshold at the beginning of the current cycle based on the first business data generated in the previous cycle, making the alarm threshold setting more reasonable and in line with recent business conditions. It also obtains the second business data of the second database in real time as business requests in the current cycle are received, that is, the current business situation is obtained, and the monitoring value is calculated. When the monitoring value is greater than the alarm threshold, an early warning is triggered. The early warning can be triggered in a timely manner according to the real-time business situation, which meets the actual needs of refined business control, improves business management and processing efficiency, and can dynamically adjust business allocation in a timely manner in response to business early warnings.

[0093] In an optional embodiment, the second database includes: a database of a banking business system; the business process includes: a banking business process; and the business node corresponds to multiple staff members of the same job type.

[0094] In an optional embodiment, for each of the service nodes, based on the first service data, the alarm threshold for the previous period of each service node is updated to obtain the alarm threshold for the current period of each service node. The alarm threshold has at least one warning dimension, including: For each of the aforementioned business nodes, based on the first business data, business characteristic parameters for each business node are generated, including: peak business volume, unit processing time, average processing volume per person, and backlog baseline. Based on the business characteristic parameters, the alarm thresholds for each warning dimension of the previous period are updated to obtain the alarm thresholds for each warning dimension of the current period for each business node; the warning dimensions include: node pending processing volume warning dimension, estimated processing time warning dimension, and manpower shortage warning dimension.

[0095] In an optional embodiment, updating the alarm thresholds for each warning dimension of the previous period based on the business characteristic parameters to obtain the alarm thresholds for each warning dimension of the current period for each business node includes: Based on the updated peak traffic volume and the backlog baseline, the alarm threshold for the node pending volume warning dimension of the previous period is updated to obtain the alarm threshold for each business node for the node pending volume warning dimension of the current period. Based on the updated peak traffic volume and unit processing time, the alarm threshold for the expected processing time warning dimension of the previous period is updated to obtain the alarm threshold for the expected processing time warning dimension of each business node for the current period. Based on the updated average processing volume per person and the backlog baseline, the alarm threshold for the manpower shortage warning dimension of the previous period is updated to obtain the alarm threshold for the manpower shortage warning dimension of each business node for the current period.

[0096] In an optional embodiment, the real-time data acquisition module 303 includes: The real-time data acquisition unit is used to acquire the changed second business data in the second database in response to data changes in the second database; The preceding judgment unit is used to determine whether the preceding business data on which the second business data depends has been synchronized to the first database, and to obtain a judgment result. A synchronization unit is used to synchronize the second business data to the first database when the judgment result is yes; The delay waiting unit is used to add the second business data to the delay queue when the judgment result is negative, wait for the target time, and then judge again whether the preceding business data on which the second business data depends has been synchronized to the first database, until the number of judgments reaches the number threshold or the judgment result is positive.

[0097] In an optional embodiment, triggering a business alert when a monitored value for the alert dimension exceeds the alert threshold for the alert dimension includes: When the monitoring value of the warning dimension of the pending amount of the node exceeds the alarm threshold, a business warning is sent to the task processing user associated with the business node. When the monitoring value of the early warning dimension of the expected processing time exceeds the alarm threshold, a business early warning is sent to the task management user associated with the business node, and an early warning prompt to transfer the subsequent assigned task is sent. When the monitoring value of the warning dimension of the manpower shortage exceeds the alarm threshold, a business warning is sent to the central management user associated with the business node, and a warning prompt to add task processing users is sent.

[0098] In an optional embodiment, the batch data acquisition module 301 includes: The source data acquisition unit is used to acquire source business data from the second database in the previous period of the current period during the current period. A data calibration unit is used to calibrate the first service data in the previous period of the current period in the first database based on the source service data; The batch data acquisition unit is used to acquire the first business data after calibration.

[0099] The business early warning device provided in this embodiment of the invention can execute the business early warning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the business early warning method.

[0100] Figure 4 A schematic diagram of the structure of a service early warning device 400 that can be used to implement an embodiment of the present invention is shown.

[0101] like Figure 4 As shown, the service alert device 400 includes at least one processor 401 and a memory, such as a read-only memory 402 or a random access memory 403, communicatively connected to the at least one processor 401. The memory stores computer programs executable by the at least one processor. The processor 401 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 402 or loaded from the storage unit 408 into the random access memory 403. The random access memory 403 can also store various programs and data required for the operation of the service alert device 400. The processor 401, read-only memory 402, and random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0102] Multiple components in the service alert device 400 are connected to the input / output interface 405, including: an input unit 406, such as a keyboard or mouse; an output unit 407, such as various types of displays or speakers; a storage unit 408, such as a hard disk or optical disk; and a communication unit 409, such as a network interface card (NIC), modem, or wireless transceiver. The communication unit 409 allows the service alert device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] Processor 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 401 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 401 performs the various methods and processes described above, such as business alerting methods.

[0104] In some embodiments, the business alerting method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the business alerting device 400 via read-only memory 402 and / or communication unit 409. When the computer program is loaded into random access memory 403 and executed by processor 401, one or more steps of the business alerting method described above may be performed. Alternatively, in other embodiments, processor 401 may be configured to perform the business alerting method by any other suitable means (e.g., by means of firmware).

[0105] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), complex programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0108] To provide user interaction, the systems and techniques described herein can be implemented on an operational detection device, which includes: a display device (e.g., a cathode ray tube or liquid crystal monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the operational detection device. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0109] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0110] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system. This addresses the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0111] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0112] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A business early warning method, characterized in that, The method includes: In the current cycle, the first business data of the first database in the previous cycle of the current cycle is obtained. The first database stores data related to multiple business processes, and the business process is configured with at least one business node. For each of the aforementioned business nodes, based on the first business data, the alarm threshold for each of the aforementioned business nodes for the previous period is updated to obtain the alarm threshold for each of the aforementioned business nodes for the current period, wherein the alarm threshold has at least one warning dimension. In response to data changes in the second database, the changed second business data in the second database is synchronized to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business; The monitoring values ​​for each of the aforementioned early warning dimensions are determined based on the changed second business data; A business alert is triggered when the monitored value of the aforementioned alert dimension exceeds the alarm threshold of the aforementioned alert dimension for the current period.

2. The method according to claim 1, characterized in that, For each of the aforementioned business nodes, based on the first business data, the alarm threshold for each business node in the previous period is updated to obtain the alarm threshold for each business node in the current period. The alarm threshold has at least one warning dimension, including: For each of the aforementioned business nodes, based on the first business data, business characteristic parameters for each business node are generated, including: peak business volume, unit processing time, average processing volume per person, and backlog baseline. Based on the business characteristic parameters, the alarm thresholds for each warning dimension of the previous period are updated to obtain the alarm thresholds for each warning dimension of the current period for each business node; the warning dimensions include: node pending processing volume warning dimension, estimated processing time warning dimension, and manpower shortage warning dimension.

3. The method according to claim 2, characterized in that, The step of updating the alarm thresholds for each warning dimension of the previous period based on the business characteristic parameters to obtain the alarm thresholds for each warning dimension of the current period for each business node includes: Based on the updated peak traffic volume and the backlog baseline, the alarm threshold for the node pending volume warning dimension of the previous period is updated to obtain the alarm threshold for each business node for the node pending volume warning dimension of the current period. Based on the updated peak traffic volume and unit processing time, the alarm threshold for the expected processing time warning dimension of the previous period is updated to obtain the alarm threshold for the expected processing time warning dimension of each business node for the current period. Based on the updated average processing volume per person and the backlog baseline, the alarm threshold for the manpower shortage warning dimension of the previous period is updated to obtain the alarm threshold for the manpower shortage warning dimension of each business node for the current period.

4. The method according to claim 1, characterized in that, The step of responding to data changes in the second database and synchronizing the changed second business data in the second database to the first database includes: In response to data changes in the second database, retrieve the changed second business data from the second database; Determine whether the preceding business data on which the second business data depends has been synchronized to the first database, and obtain the determination result; When the determination result is yes, the second business data is synchronized to the first database; If the judgment result is negative, the second business data is added to the delay queue. After waiting for the target time, it is judged again whether the preceding business data on which the second business data depends has been synchronized to the first database, until the number of judgments reaches the threshold or the judgment result is positive.

5. The method according to claim 2, characterized in that, The step of triggering a business alert when a monitored value for the alert dimension exceeds the alert threshold for the alert dimension includes: When the monitoring value of the warning dimension of the pending amount of the node exceeds the alarm threshold, a business warning is sent to the task processing user associated with the business node. When the monitoring value of the early warning dimension of the expected processing time exceeds the alarm threshold, a business early warning is sent to the task management user associated with the business node, and an early warning prompt to transfer the subsequent assigned task is sent. When the monitoring value of the warning dimension of the manpower shortage exceeds the alarm threshold, a business warning is sent to the central management user associated with the business node, and a warning prompt to add task processing users is sent.

6. The method according to claim 1, characterized in that, The step of obtaining the first business data from the first database in the previous period of the current period during the current period includes: In the current cycle, retrieve the source business data from the second database in the previous cycle of the current cycle; The first business data in the previous period of the current period in the first database is calibrated based on the source business data. Obtain the first operational data after calibration.

7. The method according to claim 1, characterized in that, The second database includes: a database of the banking business system; the business process includes: a banking business process; and the business node corresponds to multiple staff members of the same job type.

8. A business early warning device, characterized in that, The device includes: The batch data acquisition module is used to acquire the first business data of the first database in the previous period of the current period in the current period. The first database stores data related to multiple business processes, and the business process is configured with at least one business node. An alarm threshold setting module is used to update the alarm threshold of each of the aforementioned business nodes for the previous period based on the first business data, so as to obtain the alarm threshold of each of the aforementioned business nodes for the current period, wherein the alarm threshold has at least one warning dimension. The real-time data acquisition module is used to respond to data changes in the second database and synchronize the changed second business data in the second database to the first database; the first database is a backup database of the second database, the first database is used for monitoring business, and the second database is used for processing business; The monitoring indicator determination module is used to determine the monitoring values ​​of each of the early warning dimensions based on the changed second business data; The comparison and early warning module is used to trigger a business early warning when the monitored value of the early warning dimension exceeds the alarm threshold of the early warning dimension in the current period.

9. A business early warning device, characterized in that, The business early warning equipment includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the business early warning method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the business early warning method according to any one of claims 1-7.

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