Exception handling method and device, electronic equipment, storage medium and program product
By monitoring and exception identification processing of the list scheduling link in the intelligent outgoing call system, the problem of timely handling of the intelligent outgoing call system in abnormal situations is solved, and the response speed and processing efficiency of abnormal events are improved.
Patent Information
- Application Number
- CN202411066994.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-05
- Publication Date
- 2024-12-17
AI Technical Summary
When an exception occurs in an intelligent outbound call system, the existing technology is difficult to deal with in a timely manner, resulting in the system being paralyzed or not dealt with in a timely manner.
By monitoring the listening nodes in the list scheduling link, receiving the listening data, generating indicator data, and abnormal identification and processing are carried out based on the exception judgment conditions, the timely processing of abnormal events is achieved.
It improves the timeliness of handling exception events in the call center, ensures that the system can respond quickly and handle appropriately in abnormal situations, and avoids system paralysis.
Smart Images
Figure CN119155382A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of intelligent outbound call technology, and specifically to an exception handling method, device, electronic device, storage medium and program product. Background Art
[0002] In the related art, in order to realize the intelligent outbound call function, the intelligent outbound call system usually needs to go through the links of pushing the list, dispatching the list and answering. Any abnormality in any link may cause the intelligent outbound call system to be paralyzed. In the related art, when an abnormality occurs in the intelligent outbound call system, the log information of each link is usually analyzed to determine the abnormal location, and then the abnormal location is handled. This abnormal handling method usually leads to the problem of untimely abnormal handling. Summary of the invention
[0003] The present application provides an exception handling method, device, electronic device, storage medium and program product, which are conducive to improving the timeliness of handling abnormal events in a call center.
[0004] In a first aspect, an embodiment of the present application provides an exception handling method, the method comprising:
[0005] Monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes monitoring point information and monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0006] Obtaining indicator generation strategy information corresponding to the monitoring indicator;
[0007] Generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0008] Based on the abnormality judgment condition, the indicator data is subjected to abnormality identification to obtain an identification result;
[0009] In a case where the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on an abnormal processing plan corresponding to the abnormal event type information.
[0010] In a second aspect, an embodiment of the present application provides an exception handling device, the device comprising:
[0011] A monitoring module, used to monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes the monitoring point information and the monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0012] An acquisition module, used to acquire indicator generation strategy information corresponding to the monitoring indicator;
[0013] A generating module, configured to generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0014] An identification module, used to identify abnormalities of the indicator data based on the abnormality judgment condition to obtain an identification result;
[0015] The processing module is used to process the abnormal event indicated by the abnormal event type information based on the abnormal processing plan corresponding to the abnormal event type information when the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information.
[0016] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method steps of the first aspect described above are implemented.
[0017] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method steps of the first aspect described above are implemented.
[0018] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the steps of the method described in the first aspect.
[0019] In the embodiment of the present application, the monitoring node in the list scheduling link is monitored to obtain monitoring data, and then the monitoring data is used to generate index data, and the index data is abnormally identified based on the abnormal judgment condition to obtain the identification result, and when the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on the abnormal processing plan corresponding to the abnormal event type information. In this process, since the node status in the list scheduling link can be monitored online, and the abnormal event is processed in time when the abnormal event is monitored, it is beneficial to improve the timeliness of abnormal event processing in the call center. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.
[0021] Figure 1 This is one of the flowcharts of the exception handling method provided in the embodiment of the present application;
[0022] Figure 2 It is a flowchart of the scheduling process of the list scheduling link in the embodiment of the present application;
[0023] Figure 3 It is a flowchart of the joint action of each module in the exception handling system in the embodiment of the present application;
[0024] Figure 4 It is one of the schematic diagrams of the data processing process performed by the data processing module in the embodiment of the present application;
[0025] Figure 5 This is the second schematic diagram of the data processing process performed by the data processing module in the embodiment of the present application;
[0026] Figure 6 It is a flowchart of the event processing process performed by the event processing module in the embodiment of the present application;
[0027] Figure 7 This is the second flowchart of the exception handling method provided in the embodiment of the present application;
[0028] Figure 8 It is one of the structural schematic diagrams of the exception handling device provided in the embodiment of the present application.
[0029] Fig. 9 This is the second structural diagram of the exception handling device provided in the embodiment of the present application. DETAILED DESCRIPTION
[0030] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0031] See also Figure 1 , Figure 1 A flowchart of an exception handling method provided in an embodiment of the present application is provided, wherein the exception handling method comprises the following steps:
[0032] Step 101: monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes monitoring point information and monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0033] Step 102: Obtain indicator generation strategy information corresponding to the monitoring indicator;
[0034] Step 103: Generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0035] Step 104: performing abnormality identification on the indicator data based on the abnormality judgment condition to obtain an identification result;
[0036] Step 105: When the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on an abnormal processing plan corresponding to the abnormal event type information.
[0037] For easier understanding, see Figure 2 ,The following is an introduction to the list scheduling link, which can specifically include the following links:
[0038] Upstream telemarketing, anti-fraud, anti-telephone fraud and customer service teams push their selected user lists and dialing strategies to the intelligent outbound call list receiving system;
[0039] After receiving the list, the intelligent outbound call list receiving system first queries the user portrait, user tag and scheduling information, and then pushes it to the list scheduling system;
[0040] The list dispatching system allocates outbound call channels to each list according to its characteristics, and dials the numbers on the list through the outbound call channels;
[0041] After establishing a call connection with the user, each round of human-computer dialogue can be handled by the intelligent outbound call answering system.
[0042] Lifecycle coverage of list scheduling links Figure 2 The push list, dispatch list and response system in Figure 2It is not difficult to see that a list to be dialed is pushed by the upstream business system. In the embodiment of the present application, monitoring nodes can be deployed in each link of the list scheduling link, so that the entire process from receiving the list to completing the dialing of the list can be monitored for exceptions. In the process of tracking and monitoring each list, the current on-site data will be collected first, then analyzed and reported, and finally the exception judgment and exception handling will be completed, thereby realizing the full life cycle of the monitored list.
[0043] See also Figure 3 In some embodiments of the present application, the above-mentioned exception handling method can be implemented based on an exception handling system, wherein the exception handling system includes a link monitoring module, a data processing module and an event processing module. The link monitoring module is mainly responsible for monitoring each node that the list passes through during the entire life cycle, and completing the collection and storage of data; the data processing module is responsible for real-time analysis of the data obtained by the link monitoring module. If the analysis determines that there is an abnormal event at the monitoring node, an event to be processed is generated and sent to the event processing module; the event processing module is responsible for processing various abnormal events pushed in, and processes the abnormal events based on the abnormal handling plan corresponding to the abnormal event type information, and archives them after the case is closed for subsequent algorithm mining.
[0044] The above-mentioned pile point information can be pre-stored in the corresponding monitoring node. In this way, when monitoring the monitoring node, the corresponding monitoring data content can be collected according to the pile point information, and the monitoring data content and the corresponding pile point information can be sent to the data processing module as monitoring data.
[0045] The above-mentioned monitoring indicators may include various indicators that can characterize whether there is an abnormality in the corresponding monitoring node, for example, various types of indicators such as the saturation rate indicator of the list storage warehouse corresponding to the list scheduling link, the agent resource idle rate indicator, the number delivery abnormality rate indicator and the link abnormality number indicator.
[0046] The above data processing module may be pre-configured with indicator generation strategy information corresponding to each monitoring indicator, for example, as shown in the following Table 1:
[0047] Table 1:
[0048]
[0049] The above abnormal judgment condition may be a predefined judgment condition. For example, when the saturation rate is greater than 110%, a warehouse oversaturation abnormal event is triggered. At this time, the abnormal event type information may include: warehouse oversaturation abnormal event.
[0050] Please see further Figure 3 The process of the link monitoring module, data processing module and event processing module in the embodiment of the present application working together is as follows:
[0051] 1) The process of link monitoring module monitoring the monitoring node mainly includes the following steps:
[0052] a. Generate a monitoring task to collect data;
[0053] b. Consume the monitoring task generated in step a and collect all available data of the current monitoring node, such as basic data of the list, abnormal information data, link information, and pile point information;
[0054] c. Pack the data according to the established structure and then store it persistently in the database;
[0055] d. Send a data processing request to the data processing module, wherein the data processing request includes the above-mentioned monitoring data.
[0056] 2) After receiving the data processing request sent by the link monitoring module, the data processing module can perform the following steps:
[0057] a. Read the monitoring data in the data processing request, wherein the monitoring data includes the monitoring point information and the monitoring data content, wherein the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0058] b. Obtaining indicator generation strategy information corresponding to the monitoring indicator;
[0059] c. generating indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0060] d. Based on the abnormal judgment condition, the indicator data is identified as abnormal to obtain an identification result; when the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, an exception handling request is sent to an event handling module, wherein the exception handling request includes the identification result.
[0061] 3) After receiving the exception handling request, the event handling module first searches whether there is an exception handling plan corresponding to the exception event indicated by the exception event type information. If so, the exception event is handled according to the exception handling plan. If not, the solution recommender can be used to predict and recommend a solution, and the exception handling is performed based on the predicted and recommended solution. After the exception handling is completed, the exception event and the corresponding exception handling solution can be sent to the relevant responsible person and archived.
[0062] Among them, the link monitoring module is the starting point, which carries the functions of monitoring the list scheduling link, collecting and storing data, and sending data to the data processing module.
[0063] The monitoring list scheduling link is implemented by pile points, and the so-called pile points are defined on the list scheduling link to be monitored, and the pile points are also the above-mentioned monitoring nodes. In the embodiment of the present application, the pile points are divided into two categories: custom and system preset. The custom pile points refer to the positions that the user specifies to be monitored on the list scheduling link, and the system preset pile points are some indicators that are set in advance on the list scheduling link to be monitored (later changeable). The annotation method used in the embodiment of the present application defines the pile point identifier.
[0064] As shown in Table 2, the annotation contains the following attribute definitions:
[0065] Table 2:
[0066]
[0067] After identifying the stub annotation, the link monitoring module will intercept the business code through AOP. Because it is necessary to intercept and process before and after the business code, the AOP surround notification type is needed to intercept the business code. The surround notification here can be simply understood as wrapping the target business code and putting logic code before and after its execution.
[0068] After listening to the custom stubs and system preset stubs, the stub annotations are first parsed to identify the business type and business code behavior, and the corresponding business rule definition is obtained through the business type. Then, the corresponding initialization business identity recognition context data is obtained according to the rules. Here, the relevant data of the previously executed associated business code is obtained and put into the context manager. Next, some identity recognition data and related custom functions of the current business code are obtained according to the fields of the business code behavior of the stub annotations.
[0069] After processing the custom function and context data, the corresponding data will be packaged and put into the storage warehouse dedicated to the current business code execution, where the monitoring data can include the data in the storage warehouse. After waiting for all relevant data to be collected, the link monitoring module data storage and sending phase will be entered. First, the data will be stored in the database, and then the data will be packaged and sent to the data processing module through the message queue for analysis and processing.
[0070] like Figure 4 As shown, the above data processing module is mainly responsible for receiving the data collected by the link monitoring module, then calculating various indicators through these data, and finally identifying whether there is an abnormal event based on the various indicators.
[0071] The above calculation indicators mean that after receiving the data collected by the link monitoring module, the data processing module will calculate its value according to some established indicators, such as data warehouse fullness rate, agent resource idleness rate, number delivery abnormality rate and link abnormality number. The calculation formulas of all the above indicators are preset and configured in advance. If you need to change it, just modify the configuration directly. If you want to add a new indicator, just add the configuration directly.
[0072] Analyze indicators from three aspects: links, big data, and services. Links refer to whether a certain point in the entire list scheduling link is healthy, including health indicators of the list scheduling link such as warehouses, queues, and number delivery pools; big data refers to whether the entire list scheduling link is healthy as a whole, such as whether the list from 9 to 10 in the morning can be dialed as planned; services refer to whether some calls to third-party services on the list scheduling link are healthy, such as the response time for requesting basic resources, etc.
[0073] Anomaly detection needs to be fast and accurate. To avoid false alarms, this application uses an intelligent anomaly detection algorithm based on the idea of similar distribution of historically adjacent points (similar time series features) for a detection. If the current point to be detected is relatively abnormal compared to other historical reference points (there is a point anomaly or a pattern anomaly), the detection process will identify the abnormal point, and generate an abnormal event for the abnormal phenomenon of the indicator to be tested, and then send it to the event processing module through the message queue.
[0074] In order to reduce the time of calculating, analyzing and generating events, the processing flow of the data processing module is as follows Figure 5 As shown, the specific steps include:
[0075] After receiving the monitoring data sent by the link monitoring module, the data processing module sends it to the Flink real-time task processing program through the message queue;
[0076] The Flink real-time task processing program first obtains the indicator generation strategy information corresponding to the monitoring indicator, then generates the indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content, and finally sends all the indicator data to the data processing module;
[0077] After receiving the indicator data, the data processing module performs diagnosis, selects the abnormal data to generate abnormal events, and then sends the abnormal events to the event processing module through the message queue.
[0078] The above event processing module will monitor all managed events. Once a managed event arrives, it will obtain the exception handling plan corresponding to the abnormal event. If the exception handling plan is entrusted to the system, the event processing module will immediately execute the exception handling plan, then notify the responsible person and archive the event. If the exception handling plan is not entrusted to the system, the event processing module can directly notify the responsible person and wait for the responsible person to handle it by himself, and finally archive the event.
[0079] The execution process of the event processing module is as above Figure 6 As shown, the specific steps include:
[0080] The event processing module receives the abnormal events sent by the data processing module, and then registers the abnormal events to the event management center, which is managed by the event management center;
[0081] The event management center coordinates and manages various abnormal events. When an abnormal event arrives, it first retrieves the event metadata from the event management center; then it processes and diagnoses the event according to the delegation relationship, processing plan, and responsible person information defined in the event metadata; finally, it sends the diagnosis results to the corresponding instance for processing.
[0082] If the abnormal event is directly entrusted to the system for processing, the system will wait until the entire abnormal event is processed before notifying the responsible person and filing a summary. If the event is not directly entrusted to the system for processing, the system will wait until the responsible person completes the processing before filing a summary.
[0083] In this implementation, the monitoring data is obtained by monitoring the monitoring nodes in the list scheduling link, and then the index data is generated by using the monitoring data, and the index data is abnormally identified based on the abnormal judgment condition to obtain the identification result, and when the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on the abnormal processing plan corresponding to the abnormal event type information. In this process, since the node status in the list scheduling link can be monitored online, and the abnormal event is processed in time when the abnormal event is monitored, it is beneficial to improve the timeliness of abnormal event processing in the call center.
[0084] Optionally, the stake point information includes at least two different monitoring indicators, wherein different monitoring indicators among the at least two different monitoring indicators correspond to different abnormal event types, the abnormal judgment condition includes at least two abnormal judgment sub-conditions corresponding one-to-one to the at least two different monitoring indicators, and the abnormal event type information includes at least two event type sub-information corresponding one-to-one to the at least two different monitoring indicators, and among the at least two event type sub-information, different event type sub-information corresponds to different abnormal handling plans.
[0085] Specifically, more than two monitoring indicators can be set for the same monitoring node. For example, the following monitoring indicators can be set for the data warehousing monitoring node: receiving rate indicator, basic data integrity indicator, warehousing rate indicator, warehouse saturation rate indicator, warehouse queue utilization rate indicator, etc. Correspondingly, a corresponding abnormal event type can be set for each monitoring indicator. For example, the abnormal event type set for the receiving rate indicator is: receiving rate abnormal event; the abnormal event type set for the basic data integrity indicator is: basic data integrity abnormal event, etc.
[0086] Since the calculation formula of each indicator is different, the abnormality judgment threshold for the indicator value of each indicator may also be different, that is, different monitoring indicators correspond to different abnormality judgment sub-conditions. Correspondingly, different abnormalities may correspond to different abnormality handling plans.
[0087] In this implementation, by generating more than two monitoring indicators for the same monitoring node, and setting different abnormal event types corresponding to different monitoring indicators, and setting different abnormal judgment sub-conditions and abnormal handling plans, it is beneficial to promptly handle various abnormalities in the call system.
[0088] Optionally, the at least two different monitoring indicators include at least two of the following: a link indicator, a dashboard indicator and a service indicator, wherein the link indicator is used to characterize whether there is an abnormality in the corresponding monitoring node in the list scheduling link, the dashboard indicator is used to characterize whether the list scheduled by the list scheduling link is abnormal, and the service indicator is used to characterize whether the third-party service associated with the list scheduling link is abnormal.
[0089] The above market index may specifically refer to whether the entire list currently being scheduled can be normally scheduled within a preset time. The above third-party service is a third-party service that the list scheduling link relies on in the process of scheduling the list.
[0090] In this implementation, by making the at least two different monitoring indicators include at least two of the following: a link indicator, a market indicator, and a service indicator, it is helpful to handle various anomalies in the call system in a timely manner.
[0091] Optionally, the monitoring node includes a first warehouse node of a warehouse for storing the list, and the at least two different monitoring indicators include a list receiving rate indicator, a list basic data completeness indicator, a warehouse entry rate indicator, a warehouse saturation rate indicator, and a warehouse queue utilization rate indicator;
[0092] The abnormal event type information includes: a first abnormality for characterizing an abnormal list receiving rate, a second abnormality for characterizing an abnormal list basic data completeness, a third abnormality for characterizing an abnormal warehouse entry rate, a fourth abnormality for characterizing a warehouse oversaturation, a fifth abnormality for characterizing an undersaturated warehouse, and a sixth abnormality for characterizing an abnormal warehouse queue usage rate;
[0093] The indicator data includes: the indicator value corresponding to the list receiving rate indicator, the indicator value corresponding to the list basic data completeness indicator, the indicator value corresponding to the warehousing rate indicator, the indicator value corresponding to the warehouse saturation rate indicator, and the indicator value corresponding to the warehouse queue utilization rate indicator.
[0094] Among them, the first warehouse node can be a warehouse node used to store the list in each link in the list scheduling link.
[0095] In some embodiments of the present application, the abnormality identification of the indicator data based on the abnormality judgment condition to obtain the identification result includes:
[0096] When the indicator value corresponding to the list receiving rate indicator is less than the first threshold, determining that the first warehouse node has the first abnormality;
[0097] When the index value corresponding to the list basic data completeness index is less than the second threshold, determining that the first warehouse node has the second abnormality;
[0098] When the indicator value corresponding to the warehousing rate indicator is greater than the third threshold, it is determined that the first warehouse node has the third abnormality;
[0099] When the index value corresponding to the warehouse saturation rate index is greater than a fourth threshold, determining that the first warehouse node has the fourth abnormality;
[0100] When the index value corresponding to the warehouse saturation rate index is less than a fifth threshold, determining that the first warehouse node has the fifth abnormality, wherein the fifth threshold is less than the fourth threshold;
[0101] When the indicator value corresponding to the warehouse queue usage indicator is less than the sixth threshold, it is determined that the sixth abnormality exists in the first warehouse node.
[0102] In some embodiments of the present application, the exception handling plan corresponding to the first exception is: outputting a first alarm message via email and / or telephone;
[0103] The exception handling plan corresponding to the second exception is: starting the compensation mechanism to repair the missing data;
[0104] The exception handling plan corresponding to the third exception is: outputting the second alarm information via email and / or telephone, and opening the backup resources corresponding to the first warehouse node to store the list. After opening the backup resources, if the third exception occurs again, the list that needs to be stored in the first warehouse node within the first time period is stopped from being put into storage, and the list that has not been put into storage within the first time period is stored in the second warehouse node, wherein the second warehouse node is the warehouse node corresponding to the next time period of the first time period, and the first warehouse node is the warehouse node corresponding to the first time period;
[0105] The exception handling plan corresponding to the fourth exception is: outputting the third alarm information by email and / or phone, and stopping the storage of the list that needs to be stored in the first warehouse node within the first time period, and storing the list that has not been stored in the first time period in the second warehouse node;
[0106] The exception handling plan corresponding to the fifth exception is: outputting the fourth alarm information by email and / or telephone, and storing the list to be stored in the second warehouse node in the first warehouse node;
[0107] The exception handling plan corresponding to the sixth exception is: outputting the fifth alarm information via email and / or telephone.
[0108] The states corresponding to the monitoring indicators in the above embodiment and the exception handling plan can be represented by the following Table 3:
[0109] Table 3:
[0110]
[0111]
[0112] In this implementation, by setting various monitoring indicators and corresponding exception handling plans, it is possible to implement exception monitoring and exception handling of each link in the list scheduling link based on the above-mentioned monitoring indicators and corresponding exception handling plans, thereby facilitating the timeliness of exception handling of the list scheduling link.
[0113] It should be noted that in the embodiments of the present application, the monitoring location, data collection and processing, and emergency plans are all customizable. They are not limited to monitoring at a certain location, nor are there any regulations on collecting specific data, nor are there any emergency plans that are applicable to all scenarios.
[0114] The monitoring position is implemented by pile points one by one in the monitoring list scheduling link as described in the above embodiment. The so-called pile point is the position to be monitored defined on the list scheduling link, wherein the pile points are divided into two categories: custom and system preset. The pile points are woven into the desired monitoring position by explicit annotations. When the link monitoring module encounters a pile point during the execution of the monitoring code, it will read the corresponding pile point configuration table according to the pile point to collect data, and then identify the abnormal indicators, and read the abnormal handling plan according to the indicators to respond to the abnormality.
[0115] In the embodiment of the present application, the example of receiving a list in list scheduling and then sending it to the warehouse is used to illustrate that the act of receiving the list and sending it to the warehouse is Figure 2 The behavior of the process of pushing the list to the list scheduling system is to receive the list from the upstream business system, and then put it into different scheduling warehouses after sorting it according to the dialing time and priority. For example, the list dialed between 9 and 10 o'clock is sorted according to priority and put into the warehouse between 9 and 10 o'clock, and so on until all the lists are put into the warehouse. Before weaving in the monitoring pile point, it is necessary to analyze the data to be monitored and collected and the abnormal situations to be handled in this scenario, which correspond to the monitoring indicators and abnormal events respectively. For example, after analysis, the above Table 3 can be obtained.
[0116] According to Table 3 above, firstly, pile points will be woven into the three places where the code is executed, namely, the entrance for receiving the upstream list, the entrance for entering the list into the warehouse, and the list warehouse, in the form of annotations. In the pile points, the indicator data to be monitored and collected and the data in the previous table will be specified. The first six fields are business fields on the system and will not be described here. The last one, l istening, is the one that really works.
[0117] Table 4:
[0118]
[0119] Please refer to Table 4. The embodiment of the present application takes the warehouse saturation rate as an example to further explain the process of collecting and processing data: the link monitoring module identifies the pile point and collects the required data according to the configuration (specifically, the number of in-stock lists and the number of people to be entered into the warehouse of each warehouse will be collected). After the corresponding data is collected, it will be sent to the data processing module. After receiving the pile point information and the collected data, the data processing module will read the corresponding calculation formula according to the monitoring indicator of the pile point configuration to calculate its indicator (warehouse saturation rate = (number of in-stock lists + number of people to be entered into the warehouse - number of people leaving the warehouse) / number of calls made by the total number of seats in one hour).
[0120] After obtaining the monitoring indicator, the l istening field in the pile point configuration is used to determine whether an abnormal event needs to be generated. Taking the saturation rate as an example, the l istening field configuration in the previous text is {saturation rate>110% triggers a warehouse oversaturation abnormal event}. This configuration is divided into two sections based on the trigger. The first half is the monitoring indicator and the conditions for judging whether it is abnormal, and the second half defines the type of abnormal event. That is, the saturation rate configuration means that when the warehouse saturation rate is greater than 110%, the warehouse oversaturation abnormal event is triggered and sent to the event processing module.
[0121] After the event processing module receives the abnormal event sent by the data processing module (for example, the abnormal event is a warehouse oversaturation abnormal event), it reads the corresponding abnormal handling plan according to the event type, and then handles it according to the abnormal handling plan. Taking the warehouse oversaturation abnormal event as an example, the corresponding abnormal handling plan will be read from the event management center, and an email alert will be sent to the system operation and maintenance personnel first, and then the number of oversaturated lists will be calculated, and a specified number of low-priority lists will be selected in the warehouse, and finally the selected lists will be moved to the warehouse to be dialed in the next hour.
[0122] See also Figure 7 , Figure 7 A flowchart of an exception handling method provided in an embodiment of the present application, the specific implementation process of which may include:
[0123] Monitor all system preset and custom stake points on the entire list scheduling link, collect data, context management, front and back concatenation, package all on-site data and send them to the data processing module through the message queue;
[0124] After receiving the link monitoring data, the indicators that can be calculated by reading the data are sent to the Flink real-time task processing program through the message queue, which completes the calculation of the indicators. The calculation results are then returned to the data processing module for diagnosis. If the diagnosis is abnormal, an abnormal event is generated and sent to the event processing module through the message queue;
[0125] After receiving an abnormal event, it is registered to the abnormal event management center, which then takes over the subsequent processing flow. First, the abnormal event metadata that has been configured in the event management center is used to identify the abnormal event; then the abnormal event is handed over to different instances to execute the abnormal event and notify the responsible person according to the event metadata; finally, after the processing is completed, the event is archived for case analysis.
[0126] See also Figure 8 , Figure 8 The schematic diagram of the structure of an exception handling device 800 provided in an embodiment of the present application is as follows. The exception handling device 800 includes:
[0127] The monitoring module 801 is used to monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes the monitoring point information and the monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0128] An acquisition module 802 is used to acquire indicator generation strategy information corresponding to the monitoring indicator;
[0129] A generating module 803, configured to generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0130] An identification module 804 is used to identify abnormalities of the indicator data based on the abnormality judgment condition to obtain an identification result;
[0131] The processing module 805 is used to process the abnormal event indicated by the abnormal event type information based on the abnormal processing plan corresponding to the abnormal event type information when the identification result indicates that the monitoring node has the abnormal event indicated by the abnormal event type information.
[0132] Optionally, the stake point information includes at least two different monitoring indicators, wherein different monitoring indicators among the at least two different monitoring indicators correspond to different abnormal event types, the abnormal judgment condition includes at least two abnormal judgment sub-conditions corresponding one-to-one to the at least two different monitoring indicators, and the abnormal event type information includes at least two event type sub-information corresponding one-to-one to the at least two different monitoring indicators, and among the at least two event type sub-information, different event type sub-information corresponds to different abnormal handling plans.
[0133] Optionally, the at least two different monitoring indicators include at least two of the following: a link indicator, a dashboard indicator and a service indicator, wherein the link indicator is used to characterize whether there is an abnormality in the corresponding monitoring node in the list scheduling link, the dashboard indicator is used to characterize whether the list scheduled by the list scheduling link is abnormal, and the service indicator is used to characterize whether the third-party service associated with the list scheduling link is abnormal.
[0134] Optionally, the monitoring node includes a first warehouse node of a warehouse for storing the list, and the at least two different monitoring indicators include a list receiving rate indicator, a list basic data completeness indicator, a warehouse entry rate indicator, a warehouse saturation rate indicator, and a warehouse queue utilization rate indicator;
[0135] The abnormal event type information includes: a first abnormality for characterizing an abnormal list receiving rate, a second abnormality for characterizing an abnormal list basic data completeness, a third abnormality for characterizing an abnormal warehouse entry rate, a fourth abnormality for characterizing a warehouse oversaturation, a fifth abnormality for characterizing an undersaturated warehouse, and a sixth abnormality for characterizing an abnormal warehouse queue usage rate;
[0136] The indicator data includes: the indicator value corresponding to the list receiving rate indicator, the indicator value corresponding to the list basic data completeness indicator, the indicator value corresponding to the warehousing rate indicator, the indicator value corresponding to the warehouse saturation rate indicator, and the indicator value corresponding to the warehouse queue utilization rate indicator.
[0137] Optionally, the identification module 804 is specifically configured to determine that the first abnormality exists in the first warehouse node when the indicator value corresponding to the list receiving rate indicator is less than a first threshold;
[0138] The identification module 804 is specifically configured to determine that the first warehouse node has the second anomaly when the indicator value corresponding to the list basic data completeness indicator is less than a second threshold value;
[0139] The identification module 804 is specifically configured to determine that the first warehouse node has the third abnormality when the indicator value corresponding to the warehousing rate indicator is greater than a third threshold value;
[0140] The identification module 804 is specifically configured to determine that the first warehouse node has the fourth abnormality when the indicator value corresponding to the warehouse saturation rate indicator is greater than a fourth threshold value;
[0141] The identification module 804 is specifically configured to determine that the first warehouse node has the fifth abnormality when the indicator value corresponding to the warehouse saturation rate indicator is less than a fifth threshold, wherein the fifth threshold is less than the fourth threshold;
[0142] The identification module 804 is specifically configured to determine that the sixth abnormality exists in the first warehouse node when the indicator value corresponding to the warehouse queue usage rate indicator is less than a sixth threshold.
[0143] Optionally, the exception handling plan corresponding to the first exception is: outputting a first alarm message via email and / or telephone;
[0144] The exception handling plan corresponding to the second exception is: starting the compensation mechanism to repair the missing data;
[0145] The exception handling plan corresponding to the third exception is: outputting the second alarm information via email and / or telephone, and opening the backup resources corresponding to the first warehouse node to store the list. After opening the backup resources, if the third exception occurs again, the list that needs to be stored in the first warehouse node within the first time period is stopped from being put into storage, and the list that has not been put into storage within the first time period is stored in the second warehouse node, wherein the second warehouse node is the warehouse node corresponding to the next time period of the first time period, and the first warehouse node is the warehouse node corresponding to the first time period;
[0146] The exception handling plan corresponding to the fourth exception is: outputting the third alarm information by email and / or phone, and stopping the storage of the list that needs to be stored in the first warehouse node within the first time period, and storing the list that has not been stored in the first time period in the second warehouse node;
[0147] The exception handling plan corresponding to the fifth exception is: outputting the fourth alarm information by email and / or telephone, and storing the list to be stored in the second warehouse node in the first warehouse node;
[0148] The exception handling plan corresponding to the sixth exception is: outputting the fifth alarm information via email and / or telephone.
[0149] See also Fig. 9 , Fig. 9 is a structural diagram of an exception handling device 900 provided in another embodiment of the present application, such as Fig. 9 As shown, the exception handling device 900 includes: a processor 901, a memory 902, and a computer program stored in the memory 902 and executable on the processor. The various components in the database generating device are coupled together via a bus interface 903. When the computer program is executed by the processor 901, the following steps are implemented:
[0150] Monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes monitoring point information and monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node;
[0151] Obtaining indicator generation strategy information corresponding to the monitoring indicator;
[0152] Generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content;
[0153] Based on the abnormality judgment condition, the indicator data is subjected to abnormality identification to obtain an identification result;
[0154] In a case where the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on an abnormal processing plan corresponding to the abnormal event type information.
[0155] Optionally, the stake point information includes at least two different monitoring indicators, wherein different monitoring indicators among the at least two different monitoring indicators correspond to different abnormal event types, the abnormal judgment condition includes at least two abnormal judgment sub-conditions corresponding one-to-one to the at least two different monitoring indicators, and the abnormal event type information includes at least two event type sub-information corresponding one-to-one to the at least two different monitoring indicators, and among the at least two event type sub-information, different event type sub-information corresponds to different abnormal handling plans.
[0156] Optionally, the at least two different monitoring indicators include at least two of the following: a link indicator, a dashboard indicator and a service indicator, wherein the link indicator is used to characterize whether there is an abnormality in the corresponding monitoring node in the list scheduling link, the dashboard indicator is used to characterize whether the list scheduled by the list scheduling link is abnormal, and the service indicator is used to characterize whether the third-party service associated with the list scheduling link is abnormal.
[0157] Optionally, the monitoring node includes a first warehouse node of a warehouse for storing the list, and the at least two different monitoring indicators include a list receiving rate indicator, a list basic data completeness indicator, a warehouse entry rate indicator, a warehouse saturation rate indicator, and a warehouse queue utilization rate indicator;
[0158] The abnormal event type information includes: a first abnormality for characterizing an abnormal list receiving rate, a second abnormality for characterizing an abnormal list basic data completeness, a third abnormality for characterizing an abnormal warehouse entry rate, a fourth abnormality for characterizing a warehouse oversaturation, a fifth abnormality for characterizing an undersaturated warehouse, and a sixth abnormality for characterizing an abnormal warehouse queue usage rate;
[0159] The indicator data includes: the indicator value corresponding to the list receiving rate indicator, the indicator value corresponding to the list basic data completeness indicator, the indicator value corresponding to the warehousing rate indicator, the indicator value corresponding to the warehouse saturation rate indicator, and the indicator value corresponding to the warehouse queue utilization rate indicator.
[0160] Optionally, the performing abnormality identification on the indicator data based on the abnormality judgment condition to obtain an identification result includes:
[0161] When the indicator value corresponding to the list receiving rate indicator is less than the first threshold, determining that the first warehouse node has the first abnormality;
[0162] When the index value corresponding to the list basic data completeness index is less than the second threshold, determining that the first warehouse node has the second abnormality;
[0163] When the indicator value corresponding to the warehousing rate indicator is greater than the third threshold, it is determined that the first warehouse node has the third abnormality;
[0164] When the index value corresponding to the warehouse saturation rate index is greater than a fourth threshold, determining that the first warehouse node has the fourth abnormality;
[0165] When the index value corresponding to the warehouse saturation rate index is less than a fifth threshold, determining that the first warehouse node has the fifth abnormality, wherein the fifth threshold is less than the fourth threshold;
[0166] When the indicator value corresponding to the warehouse queue usage indicator is less than the sixth threshold, it is determined that the sixth abnormality exists in the first warehouse node.
[0167] Optionally, the exception handling plan corresponding to the first exception is: outputting a first alarm message via email and / or telephone;
[0168] The exception handling plan corresponding to the second exception is: starting the compensation mechanism to repair the missing data;
[0169] The exception handling plan corresponding to the third exception is: outputting the second alarm information via email and / or telephone, and opening the backup resources corresponding to the first warehouse node to store the list. After opening the backup resources, if the third exception occurs again, the list that needs to be stored in the first warehouse node within the first time period is stopped from being put into storage, and the list that has not been put into storage within the first time period is stored in the second warehouse node, wherein the second warehouse node is the warehouse node corresponding to the next time period of the first time period, and the first warehouse node is the warehouse node corresponding to the first time period;
[0170] The exception handling plan corresponding to the fourth exception is: outputting the third alarm information by email and / or phone, and stopping the storage of the list that needs to be stored in the first warehouse node within the first time period, and storing the list that has not been stored in the first time period in the second warehouse node;
[0171] The exception handling plan corresponding to the fifth exception is: outputting the fourth alarm information by email and / or telephone, and storing the list to be stored in the second warehouse node in the first warehouse node;
[0172] The exception handling plan corresponding to the sixth exception is: outputting the fifth alarm information via email and / or telephone.
[0173] An embodiment of the present application also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the various processes of the above-mentioned method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be described here.
[0174] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, each process of the above method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
[0175] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.
[0176] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for an electronic device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.
[0177] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.
Claims
1. An exception handling method, characterized in that: The method comprises: Monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes monitoring point information and monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node; Obtaining indicator generation strategy information corresponding to the monitoring indicator; Generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content; Based on the abnormality judgment condition, the indicator data is subjected to abnormality identification to obtain an identification result; In a case where the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information, the abnormal event indicated by the abnormal event type information is processed based on an abnormal processing plan corresponding to the abnormal event type information.
2. The method according to claim 1, characterized in that The stake point information includes at least two different monitoring indicators, wherein different monitoring indicators among the at least two different monitoring indicators correspond to different abnormal event types, the abnormal judgment condition includes at least two abnormal judgment sub-conditions corresponding one-to-one to the at least two different monitoring indicators, and the abnormal event type information includes at least two event type sub-information corresponding one-to-one to the at least two different monitoring indicators, and among the at least two event type sub-information, different event type sub-information corresponds to different abnormal handling plans.
3. The method according to claim 2, characterized in that The at least two different monitoring indicators include at least two of the following: a link indicator, a dashboard indicator, and a service indicator, wherein the link indicator is used to characterize whether the corresponding monitoring node has an abnormality in the list scheduling link, the dashboard indicator is used to characterize whether the list scheduled by the list scheduling link is abnormal, and the service indicator is used to characterize whether the third-party service associated with the list scheduling link is abnormal.
4. The method according to claim 3, characterized in that The monitoring node includes a first warehouse node of a warehouse for storing the list, and the at least two different monitoring indicators include a list receiving rate indicator, a list basic data completeness indicator, a warehouse entry rate indicator, a warehouse saturation rate indicator, and a warehouse queue utilization rate indicator; The abnormal event type information includes: a first abnormality for characterizing an abnormal list receiving rate, a second abnormality for characterizing an abnormal list basic data completeness, a third abnormality for characterizing an abnormal warehouse entry rate, a fourth abnormality for characterizing a warehouse oversaturation, a fifth abnormality for characterizing an undersaturated warehouse, and a sixth abnormality for characterizing an abnormal warehouse queue usage rate; The indicator data includes: the indicator value corresponding to the list receiving rate indicator, the indicator value corresponding to the list basic data completeness indicator, the indicator value corresponding to the warehousing rate indicator, the indicator value corresponding to the warehouse saturation rate indicator, and the indicator value corresponding to the warehouse queue utilization rate indicator.
5. The method according to claim 4, characterized in that The performing abnormality identification on the indicator data based on the abnormality judgment condition to obtain an identification result includes: When the indicator value corresponding to the list receiving rate indicator is less than the first threshold, determining that the first warehouse node has the first abnormality; When the index value corresponding to the list basic data completeness index is less than the second threshold, determining that the first warehouse node has the second abnormality; When the indicator value corresponding to the warehousing rate indicator is greater than the third threshold, it is determined that the first warehouse node has the third abnormality; When the index value corresponding to the warehouse saturation rate index is greater than a fourth threshold, determining that the first warehouse node has the fourth abnormality; When the index value corresponding to the warehouse saturation rate index is less than a fifth threshold, determining that the first warehouse node has the fifth abnormality, wherein the fifth threshold is less than the fourth threshold; When the indicator value corresponding to the warehouse queue usage indicator is less than the sixth threshold, it is determined that the sixth abnormality exists in the first warehouse node.
6. The method according to claim 5, characterized in that The exception handling plan corresponding to the first exception is: outputting the first alarm information via email and / or telephone; The exception handling plan corresponding to the second exception is: starting the compensation mechanism to repair the missing data; The exception handling plan corresponding to the third exception is: outputting the second alarm information via email and / or telephone, and opening the backup resources corresponding to the first warehouse node to store the list. After opening the backup resources, if the third exception occurs again, the list that needs to be stored in the first warehouse node within the first time period is stopped from being put into storage, and the list that has not been put into storage within the first time period is stored in the second warehouse node, wherein the second warehouse node is the warehouse node corresponding to the next time period of the first time period, and the first warehouse node is the warehouse node corresponding to the first time period; The exception handling plan corresponding to the fourth exception is: outputting the third alarm information by email and / or phone, and stopping the storage of the list that needs to be stored in the first warehouse node within the first time period, and storing the list that has not been stored in the first time period in the second warehouse node; The exception handling plan corresponding to the fifth exception is: outputting the fourth alarm information by email and / or telephone, and storing the list to be stored in the second warehouse node in the first warehouse node; The exception handling plan corresponding to the sixth exception is: outputting the fifth alarm information via email and / or telephone.
7. An exception handling device, characterized in that: include: A monitoring module, used to monitor the monitoring nodes in the list scheduling link and receive monitoring data, wherein the monitoring data includes the monitoring point information and the monitoring data content, and the monitoring point information includes the monitoring index, abnormal judgment condition and abnormal event type information of the monitoring node; An acquisition module, used to acquire indicator generation strategy information corresponding to the monitoring indicator; A generating module, configured to generate indicator data corresponding to the monitoring indicator based on the indicator generation strategy information and the monitoring data content; An identification module, used to identify abnormalities of the indicator data based on the abnormality judgment condition to obtain an identification result; The processing module is used to process the abnormal event indicated by the abnormal event type information based on the abnormal processing plan corresponding to the abnormal event type information when the identification result indicates that the monitoring node has an abnormal event indicated by the abnormal event type information.
8. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program implements the method steps according to any one of claims 1 to 6 when executed by the processor.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps according to any one of claims 1 to 6 are implemented.
10. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the steps of the method according to any one of claims 1 to 6.