A traffic distribution scheduling method and device, electronic equipment and storage medium

Abstract

The application discloses a kind of traffic shunting scheduling method, device, electronic equipment and storage medium, method includes: continuously obtaining ticket log from queuing machine, and ticket log is transmitted to target message queue;Ticket log is filtered and analyzed, and the structured queuing machine interactive log is obtained, and the queuing machine interactive log is imported into target database;Periodically traverse target database, and obtain the connection rate data of artificial traffic;Wherein, connection rate data includes cumulative connection rate and time sequence connection rate;Time sequence connection rate includes the connection rate of multiple preset time periods in succession;When connection rate data meets preset rule, shunting scheduling is carried out by intelligent customer service system.The application is stored as structured data by collecting and processing ticket log, and accurate reference is provided for strategy adjustment, the polling finding of connection rate is carried out, and based on the value change of cumulative connection rate and time sequence connection rate, real-time accurate automatic scheduling is realized, and can be widely applied in computer technology field.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a call traffic diversion and scheduling method, apparatus, electronic device, and storage medium. Background Technology

[0002] Because intelligent customer service can effectively divert human call traffic, significantly reducing enterprise operating costs, it has been widely adopted by existing telecom operators. However, in actual operation, when situations such as group failures lead to increased call volume or insufficient call center staff, it is often necessary to activate emergency human call diversion strategies to ensure the connection rate of human customer service and avoid excessively long waiting times for users entering the human customer service queue. Implementing these emergency human call diversion strategies requires operations personnel to monitor the human call connection rate in real time and manually set it according to established control standards. This method requires operations personnel to continuously monitor call connection status 24 hours a day, constantly turning relevant emergency diversion switches on or off as needed. This not only fails to guarantee the real-time and accurate scheduling but also places a significant workload on operations personnel. Summary of the Invention

[0003] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, this invention proposes a call traffic splitting and scheduling method, apparatus, electronic device, and storage medium, which can efficiently and automatically realize call traffic splitting and scheduling.

[0004] On one hand, embodiments of the present invention provide a call traffic splitting and scheduling method, including:

[0005] Continuously retrieve call detail records (CDRs) from the queuing machine and transmit the CDRs to the target message queue;

[0006] The dialog logs are filtered and parsed to obtain structured queuing machine interaction logs, which are then imported into the target database.

[0007] Periodically traverse the target database to obtain the connection rate data for human-operated calls;

[0008] The connection rate data includes cumulative connection rate and time-series connection rate; the time-series connection rate includes the connection rate for multiple consecutive preset time periods.

[0009] When the connection rate data meets the preset rules, the intelligent customer service system will perform traffic diversion and scheduling.

[0010] Optionally, continuously retrieve call detail record (CDR) logs from the queuing machine and transmit the CDR logs to the target message queue, including:

[0011] The call detail records (CDRs) of the queuing machine are dynamically collected using a log collection program; the log collection program includes the lightweight log collection program filebeat, which is pre-deployed on the queuing machine.

[0012] The call detail record (CDR) logs are transmitted to the Kafka message queue asynchronously.

[0013] Optionally, the dialog box logs are filtered and parsed to obtain structured queuing machine interaction logs, including:

[0014] The call detail record (CDR) logs are transmitted to the log filtering program via the target message queue, and the structured queuing machine interaction logs processed by the log filtering program are obtained.

[0015] The log filtering program extracts the log text of the call detail record (CDR) logs according to a pre-configured regular expression, then matches and segments the log text and maps it to keywords to obtain structured queuing machine interaction logs.

[0016] Optionally, before the step of periodically traversing the target database to obtain the connection rate data for human-operated calls, the method also includes:

[0017] Based on the pre-compiled executable file of the GO GIN framework, a Docker image is generated, which is then used to containerize the target database and set up a connection rate query API.

[0018] The connection rate query API is used to obtain the cumulative connection rate and time-series connection rate from the target database.

[0019] Optionally, the queuing machine interaction log includes records of calls entering the human customer service queue and being connected to a human customer service representative in each call detail record; the target database is periodically traversed to obtain human customer service call connection rate data, including:

[0020] Periodically traverse the target database, and based on the records of entering the human customer service queue and connecting to human customer service in each call detail record, determine the first number of call detail records entering the human customer service queue and the second number of call detail records connecting to human customer service in each time period according to the time sequence.

[0021] Based on the first and second quantities, the connection rate data for manual call handling is determined.

[0022] Optionally, based on the first quantity and the second quantity, the connection rate data for human-operated calls is determined, including:

[0023] The cumulative connection rate is determined by the ratio of the sum of the second number across all time periods to the sum of the first number.

[0024] The timing connection rate is determined based on the ratio of the second quantity to the first quantity in each preset time period.

[0025] Optionally, when the connection rate data meets preset rules, traffic can be routed and scheduled through the intelligent customer service system, including:

[0026] When the cumulative connection rate is higher than the first proportion threshold, the control standard level is determined based on the number of time periods in the time-series connection rate where the connection rate is lower than the second proportion threshold. Then, the target diversion scheduling strategy is determined, and a scheduling instruction is sent to the intelligent customer service system.

[0027] Alternatively, when the cumulative connection rate is lower than the first proportion threshold, the control standard level is determined based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the third proportion threshold, and then the target diversion scheduling strategy is determined and a scheduling instruction is sent to the intelligent customer service system.

[0028] Based on the scheduling instructions, the login text is obtained from the intelligent customer service system, and the login text is placed in the interface header of the intelligent customer service system to execute the target traffic diversion scheduling strategy.

[0029] On the other hand, embodiments of the present invention provide a traffic diversion and scheduling device, comprising:

[0030] The first module is used to continuously retrieve call detail record logs from the queuing machine and transmit the call detail record logs to the target message queue;

[0031] The second module is used to filter and parse the dialog logs to obtain the structured queuing machine interaction logs, and then import the queuing machine interaction logs into the target database.

[0032] The third module is used to periodically traverse the target database to obtain the connection rate data of human call traffic;

[0033] The connection rate data includes cumulative connection rate and time-series connection rate; the time-series connection rate includes the connection rate for multiple consecutive preset time periods.

[0034] The fourth module is used to perform traffic diversion and scheduling for the intelligent customer service system when the connection rate data meets preset rules.

[0035] Optionally, the first module is specifically used for:

[0036] The call detail records (CDRs) of the queuing machine are dynamically collected using a log collection program; the log collection program includes the lightweight log collection program filebeat, which is pre-deployed on the queuing machine.

[0037] The call detail record (CDR) logs are transmitted to the Kafka message queue asynchronously.

[0038] Optionally, the second module is specifically used for:

[0039] The call detail record (CDR) logs are transmitted to the log filtering program via the target message queue, and the structured queuing machine interaction logs processed by the log filtering program are obtained.

[0040] The log filtering program extracts the log text of the call detail record (CDR) logs according to a pre-configured regular expression, then matches and segments the log text and maps it to keywords to obtain structured queuing machine interaction logs.

[0041] Optionally, the device further includes:

[0042] The fifth module is used to generate Docker images from pre-compiled executables based on the GO GIN framework, and then to containerize and deploy the target database, setting up a connectivity query API.

[0043] The connection rate query API is used to obtain the cumulative connection rate and time-series connection rate from the target database.

[0044] Optionally, the queuing machine interaction log includes records of entering the human customer service queue and connecting to a human customer service representative in each call detail record. The third module is specifically used for:

[0045] Periodically traverse the target database, and based on the records of entering the human customer service queue and connecting to human customer service in each call detail record, determine the first number of call detail records entering the human customer service queue and the second number of call detail records connecting to human customer service in each time period according to the time sequence.

[0046] Based on the first and second quantities, the connection rate data for manual call handling is determined.

[0047] Optionally, the third module is further used for:

[0048] The cumulative connection rate is determined by the ratio of the sum of the second number across all time periods to the sum of the first number.

[0049] The timing connection rate is determined based on the ratio of the second quantity to the first quantity in each preset time period.

[0050] Optionally, the fourth module is specifically used for:

[0051] When the cumulative connection rate is higher than the first proportion threshold, the control standard level is determined based on the number of time periods in the time-series connection rate where the connection rate is lower than the second proportion threshold. Then, the target diversion scheduling strategy is determined and a scheduling instruction is sent to the intelligent customer service system.

[0052] Alternatively, when the cumulative connection rate is lower than the first proportion threshold, the control standard level is determined based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the third proportion threshold, and then the target diversion scheduling strategy is determined and a scheduling instruction is sent to the intelligent customer service system.

[0053] Based on the scheduling instructions, the login text is obtained from the intelligent customer service system, and the login text is placed in the interface header of the intelligent customer service system to execute the target traffic diversion scheduling strategy.

[0054] On the other hand, embodiments of the present invention provide an electronic device, including: a processor and a memory; the memory is used to store a program; the processor executes the program to implement the above-described call traffic splitting and scheduling method.

[0055] On the other hand, embodiments of the present invention provide a computer storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the above-described traffic offloading and scheduling method.

[0056] This invention first continuously acquires call detail record (CDR) logs from the queuing system and transmits them to a target message queue. The CDR logs are then filtered and parsed to obtain structured queuing system interaction logs, which are imported into a target database. The target database is periodically traversed to obtain the connection rate data for human operators. This connection rate data includes cumulative connection rate and time-series connection rate. The time-series connection rate includes the connection rate over multiple consecutive preset time periods. When the connection rate data meets preset rules, it is used for traffic routing and scheduling through an intelligent customer service system. This invention collects and processes CDR logs, storing them as structured data to provide accurate references for strategy adjustments. By polling and searching the connection rate, and based on changes in the cumulative and time-series connection rates, real-time and accurate automated scheduling is achieved. This allows for timely provision of automated service channels or timely service reminders to users when connection rates are unstable due to heavy traffic, avoiding long waiting times. This improves user connection experience, ensures that human connection rates are not significantly affected, and enhances the operational efficiency of intelligent voice customer service personnel. Attached Figure Description

[0057] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0058] Figure 1 This is a schematic diagram of an implementation environment for call traffic diversion and scheduling provided in an embodiment of the present invention;

[0059] Figure 2 This is a flowchart illustrating a traffic diversion and scheduling method provided in an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of the process for obtaining connection rate data provided in an embodiment of the present invention;

[0061] Figure 4 A schematic diagram of the traffic splitting and scheduling determination process provided in an embodiment of the present invention;

[0062] Figure 5 A schematic diagram of the architecture of the traffic offloading and scheduling method provided in this embodiment of the invention;

[0063] Figure 6 This is a schematic diagram of an example of a graphical display interface for connecting data over a period of time, provided in an embodiment of the present invention.

[0064] Figure 7 A schematic diagram illustrating another example of a graphical display interface for data connection during a different time period, provided in an embodiment of the present invention;

[0065] Figure 8 A schematic diagram of a call traffic splitting and scheduling device provided in an embodiment of the present invention;

[0066] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention;

[0067] Figure 10 A computer system architecture block diagram suitable for implementing electronic devices according to embodiments of the present invention is provided. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0069] It should be noted that although functional modules are divided in the system diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100," "second / S200," etc., in the specification, claims, and the aforementioned figures are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0070] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0071] It is understood that the traffic offloading and scheduling method provided in this embodiment of the invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet computer, laptop computer, or desktop computer, but it is not limited to these.

[0072] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0073] Server 101 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0074] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0075] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0076] Exemplary based on Figure 1 The implementation environment shown in this embodiment of the invention provides a traffic diversion scheduling method. The following description uses the application of this traffic diversion scheduling method in server 101 as an example. It can be understood that this traffic diversion scheduling method can also be applied to terminal 102.

[0077] Reference Figure 2 , Figure 2 This is a flowchart illustrating a call routing and scheduling method applied to a server, provided in an embodiment of the present invention. The execution entity of this call routing and scheduling method can be any of the aforementioned computer devices. (Refer to...) Figure 2 The method includes the following steps:

[0078] S100: Continuously retrieve call detail record logs from the queuing machine and transmit the call detail record logs to the target message queue;

[0079] It should be noted that in some embodiments, step S100 may include: dynamically collecting call detail records (CDRs) from the queuing machine using a log collection program; wherein the log collection program includes the lightweight log collection program Filebeat, which is pre-deployed on the queuing machine; and transmitting the CDRs to the Kafka message queue asynchronously.

[0080] In some specific embodiments, step S100 can be implemented by the following steps: deploying a lightweight log collection program, filebeat, on the queuing machine to collect call detail records (CDRs) in real time and outputting them to the Kafka message queue asynchronously.

[0081] S200 and dialog logs are filtered and parsed to obtain structured queuing machine interaction logs, which are then imported into the target database.

[0082] It should be noted that, in some embodiments, filtering and parsing the call detail record (CDR) logs to obtain structured queuing machine interaction logs may include: transmitting the CDR logs to a log filtering program via a target message queue, and obtaining the structured queuing machine interaction logs processed by the log filtering program; wherein, the log filtering program extracts the log text of the CDR logs according to a pre-configured regular expression, and then matches and segments the log text and maps it to keywords to obtain structured queuing machine interaction logs.

[0083] In some specific embodiments, step S200 can be implemented through the following steps: the Kafka message queue transmits the received log messages to the log filtering program Logstash, which uses the Grok plugin to configure regular expressions according to the pre-analyzed format, matches and segments the log text and maps it to keywords, thereby filtering and parsing the log data, and finally converting it into queuing machine interaction logs for import into the Greenplum distributed analysis database.

[0084] S300: Periodically traverse the target database to obtain the connection rate data of human call traffic;

[0085] The connection rate data includes the cumulative connection rate and the time-series connection rate; the time-series connection rate includes the connection rate for multiple consecutive preset time periods.

[0086] In some embodiments, before step S300, the method may further include: generating a Docker image based on an executable file pre-compiled using the GO GIN framework, and then deploying the target database in a containerized manner and setting up a connection rate query API; wherein, the connection rate query API is used to obtain the cumulative connection rate and time-series connection rate from the target database.

[0087] It should be noted that the queuing machine interaction log includes records of entering the human customer service queue and connecting to a human customer service representative in each call detail record; in some embodiments, such as Figure 3 As shown, step S300 may include: S301, periodically traversing the target database, and based on the records of entering the human customer service queue and connecting to human customer service in each call detail record, determining the first number of call detail records entering the human customer service queue and the second number of call detail records connecting to human customer service in each time period according to the time sequence; S302, determining the human customer service connection rate data based on the first number and the second number.

[0088] In some embodiments, the connection rate data of manual calls is determined based on the first quantity and the second quantity, including: determining the cumulative connection rate based on the ratio of the sum of the second quantity to the sum of the first quantity for all time periods; and determining the time-series connection rate based on the ratio of the second quantity to the first quantity for each preset time period.

[0089] In some specific embodiments, step S300 can be implemented through the following steps: Based on the GO GIN framework, develop a call connection rate query API to provide real-time query functions for the call connection rate of human operators for each 10-minute period and the cumulative rate. Compile the preceding call connection rate query processing logic into an executable file, generate a Docker image, deploy it in a containerized manner, and provide a query interface to the outside world.

[0090] S400: When the connection rate data meets the preset rules, the intelligent customer service system will be used for traffic diversion and scheduling.

[0091] It should be noted that in some embodiments, such as Figure 4 As shown, step S400 may include: S401, when the cumulative connection rate is higher than the first proportional threshold, determining the control standard level based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the second proportional threshold, thereby determining the target diversion scheduling strategy, and sending a scheduling instruction to the intelligent customer service system; or, when the cumulative connection rate is lower than the first proportional threshold, determining the control standard level based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the third proportional threshold, thereby determining the target diversion scheduling strategy, and sending a scheduling instruction to the intelligent customer service system; S402, based on the scheduling instruction, obtaining the login text from the intelligent customer service system, placing the login text into the interface header of the intelligent customer service system, and executing the target diversion scheduling strategy.

[0092] First, it should be noted that existing intelligent customer service systems can achieve functions such as intelligent customer service business process configuration and testing verification, and real-time demand deployment. Furthermore, they lower the technical requirements and operational threshold for users, allowing customer service center operators to independently configure, test, and verify the integrated intelligent deployment of hot services. Therefore, they are widely used in intelligent customer service systems of various operators. In some specific embodiments, step S400 can be implemented through the following steps: First, perform front-end interaction analysis on the intelligent customer service system to determine that the traffic diversion strategy switch modification interface is updateVarManager, with the input parameter format being {"id":1027,"hierarchy":1,"varParentId":0,"varOrig":3,"varType":1,"varName":"Test Switch","varCode":"test","varInit":"0"}. This interface must have a valid cookie entered after system login in the headers to be successfully called; then, the comparison logic between the connection rate data and preset rules is implemented through the traffic diversion strategy automated scheduling program. The traffic diversion strategy automated scheduling program mainly includes a traffic diversion judgment module, a simulated login module, a strategy modification module, and an email sending module.

[0093] The traffic diversion judgment module calls the API every 5 minutes to query the connection rate based on the activation and deactivation conditions of different traffic diversion emergency strategies formulated by the business operations department, and determines whether to activate or deactivate the relevant strategies.

[0094] The simulated login module is developed based on Microsoft's open-source Playwright automated testing framework. It automates the simulation of user login, obtains login cookies (i.e., login text), and refreshes them periodically to prevent cookies from expiring.

[0095] The strategy modification module puts the cookie into the interface headers, calls the modification interface to modify the traffic distribution strategy switch, and after confirming the modification is successful, sends relevant activation or deactivation success information to the relevant operations personnel through the email sending module.

[0096] In actual operation, when a surge in call volume or a shortage of call handlers occurs due to system failures, it is often necessary to activate emergency call diversion strategies to ensure a high connection rate for human customer service and avoid excessively long waiting times for users in the human customer service queue. Specific measures to implement these diversion business rules include:

[0097] Measure 1: The control standards include: 1. A cumulative connection rate higher than 88%, and a connection rate lower than 75% for two consecutive 10-minute and 15-second intervals; 2. A cumulative connection rate higher than 88%, and a connection rate lower than 85% for two consecutive 10-minute and 15-second intervals. When the connection rate data meets the control standards, the call will be transferred to a human operator, as detailed below:

[0098] The normal process is as follows: When a user first requests to be transferred to a human agent, the intelligent voice customer service announces, "Okay, you will be transferred to a human agent shortly" → "Please select the type of service you need. Press 1 for fault handling, press 2 for other services, and press the asterisk key to return." After the user selects a service, they enter the human agent waiting queue.

[0099] After the diversion measures are activated, the emergency diversion process will begin:

[0100] 1) When a user says "transfer to human operator" for the first time, the intelligent voice customer service will announce, "Hello, this is currently peak time for inquiries and processing, and the waiting time for human operators is relatively long. To save your time, the intelligent voice customer service can provide you with fast service. Please tell me directly that you want to check your phone bill, check your data usage, report a fault, check your consumption details, etc." and then enter the panoramic navigation.

[0101] 2) When the user requests to be transferred to a human agent for the second time, the intelligent voice customer service will announce, "Okay, you will be transferred to a human agent shortly." "Please select the type of service you need. Press 1 for fault handling, press 2 for other services, and press the asterisk key to return."

[0102] Measure 2: The control standards include: 1. A cumulative call connection rate higher than 88%, and a call connection rate lower than 75% for three consecutive 10-minute and 15-second call intervals; 2. A cumulative call connection rate higher than 88%, and a call connection rate lower than 85% for three consecutive 10-minute and 15-second call intervals. When the call connection rate data meets the control standards, user voice rejection and diversion will be implemented, as detailed below:

[0103] The normal procedure is as follows:

[0104] 1) Unrecognized voice prompt: "Sorry, I didn't hear your question clearly. Do you need to check your phone bill, data usage, or report a fault?"

[0105] 2) Unrecognized secondary voice broadcast: "I still don't understand your needs. Do you need to check your bill, subscribe to a data package, or is your broadband unable to connect to the internet? If you need to speak to a human operator, please say 'human service'."

[0106] 3) Unrecognized three voice prompts: "Your problem requires assistance from a human customer service representative. I will now transfer you to a human customer service queue."

[0107] After the diversion measures are activated, the emergency diversion process will begin:

[0108] 1) Unrecognized voice prompt: "Sorry, Xiaoyi did not hear your question clearly. Do you need to check your phone bill, data usage, or report a fault?"

[0109] 2) Unrecognized secondary voice broadcast: "Xiaoyi still doesn't understand your needs. Do you need to check your phone bill, subscribe to a data package, or is your broadband internet connection down?"

[0110] 3) The voice prompt "To better serve you, we are now returning you to the keypad menu. Please follow the voice prompts" was not recognized when entering the dial keypad menu (which includes pressing 0 to speak to a human operator).

[0111] Measure 3: Control standards include: 1. A cumulative call connection rate higher than 88%, and a call connection rate lower than 75% for four consecutive 10-minute and 15-second call cycles; 2. A cumulative call connection rate higher than 88%, and a call connection rate lower than 85% for four consecutive 10-minute and 15-second call cycles. When the call connection rate data meets the control standards, fault routing to human operators will be implemented, as detailed below:

[0112] The normal process is as follows: there is no traffic diversion, and if you are told to transfer to a human operator, press the 1 key to be directly transferred to the human operator queue.

[0113] After the traffic diversion measures are activated, the emergency diversion process begins: Single broadband users who want to be transferred to a live agent should press the "1" key. The intelligent voice customer service will announce: "Currently, live agents are busy. Xiaoyi will immediately report the fault for you. To report a fault in your own broadband connection, press 1; to report a fault in other broadband connections, press 2; to return, press the asterisk key." Pressing "1" will enter the self-service fault reporting process; pressing "2" and the "*" key will enter the live agent service.

[0114] To further explain the embodiments of the present invention, the complete process of the method of the present invention will be described below with reference to some specific embodiments. It should be understood that the following description should not be regarded as a limitation of the present invention.

[0115] Based on the specific traffic splitting rules mentioned above, such as Figure 5 As shown, the automated traffic splitting and scheduling process can be implemented through the automated traffic splitting and scheduling program 10 as follows:

[0116] 1. When a user calls the operator's official number, the log collection process 2 (e.g., Filebeat) automatically starts collecting log information from the position where the last collection ended in the queuing machine 1 and sends it to the log filtering process through the message queue 3 (e.g., Kafka). The log filtering process 4 filters the logs according to the text matching rules and stores them in the target database 5 (e.g., Greenplum).

[0117] 2. Every 5 minutes, the automated scheduler calls the API query interface to check the call connection rate. The call connection rate for a specific time period (time sequence) and the cumulative call connection rate are shown in Table 1 and Table 2:

[0118] Table 1

[0119]

[0120]

[0121] Table 2

[0122]

[0123] According to the rules, if the call connection rate within 15 seconds is below 85% for the two most recent 10-minute timeframes, and the cumulative connection rate is below 88%, the program determines that it should initiate a manual call routing process. Furthermore, in some specific embodiments, the connection rate queried from the API query interface is visualized through chart conversion, providing a graphical display interface for the connection data, such as... Figure 6 and Figure 7 As shown, the visualization interface can accurately capture the trend of call connection rate changes, providing a basis for emergency intervention and diversion for operations and dispatch personnel.

[0124] 4. The program automatically simulates logging into the intelligent customer service system and retrieves the logged-in information from the intelligent customer service system's front-end (6).

[0125] cookies:JSESSIONID=AA6F07783F73E15840E5CFA1B3289437;

[0126] JSESSIONID=AA6F07783F73E15840E5CFA1B3289437.

[0127] 5. The program puts the cookies obtained in the previous step into the header, and fills in the BODY according to the following format, and calls the modification interface (intelligent customer service system interface 7) in POST form to change the switch for transferring to manual service to "on".

[0128] {"id":1027,"hierarchy":1,"varParentId":0,"varOrig":3,"varType":1,"varName":"Manual Diversion Switch","varCode":"zrg","varInit":"1"}

[0129] After a successful call, the API will return:

[0130] {

[0131] "key":"success.operation",

[0132] "message":"Operation successful",

[0133] "status":true,

[0134] "data":{

[0135] "id":1027,

[0136] "createTime":null,

[0137] "modifyTime":null,

[0138] "varParentId":0,

[0139] "varName":"Manual Diversion Switch",

[0140] "varCode":"zrg",

[0141] "varInit":"1",

[0142] "varOrig":3,

[0143] "createUser":null,

[0144] "varRootCode":null,

[0145] "varTreeId":null,

[0146] "varType":1,

[0147] "hierarchy":1,

[0148] "droidUse":null,

[0149] "varCreate":null,

[0150] "varModify":null

[0151] }

[0152] }

[0153] After confirming that the modification was successful, call the email module 8 to send a success message to the operations staff 9.

[0154] In summary, to address the existing problems in the technology, this invention provides an automated scheduling capability for emergency call routing in the field of intelligent customer service. The method of this invention automatically collects queuing machine logs in real time and imports them into the Greenplum distributed analysis database. Furthermore, by developing an automated scheduling program, it automatically queries connection data according to business rules, automatically switches call routing strategies on and off, and automatically notifies operations personnel, achieving fully automated processing. This invention automates the scheduling of emergency call routing strategies for manual calls, solving the problem that current emergency call routing strategies require operations personnel to observe the connection rate of manual calls in real time and manually set or disable relevant emergency call routing switches. This invention includes at least the following beneficial effects:

[0155] Through real-time and accurate automated scheduling, in situations where call volume is high and connection rates are unstable, automated service channels or timely service reminders are provided to users in a timely manner, avoiding long waiting times for users, improving user connection experience, ensuring that the manual connection rate is not significantly affected, and improving the operational efficiency of intelligent voice customer service personnel. The automated scheduling of emergency call diversion strategies plays a very important role in ensuring the manual call connection rate, avoiding long waiting times for users, ensuring user satisfaction, and reducing the workload of operations personnel.

[0156] On the other hand, such as Figure 8 As shown, this embodiment of the invention provides a call traffic diversion and scheduling device 800, including: a first module 810, used to continuously obtain call detail record (CDR) logs from a queuing machine and transmit the CDR logs to a target message queue; a second module 820, used to filter and parse the CDR logs to obtain structured queuing machine interaction logs and import the queuing machine interaction logs into a target database; a third module 830, used to periodically traverse the target database to obtain call connection rate data for human operators; wherein the connection rate data includes cumulative connection rate and time-series connection rate; the time-series connection rate includes the connection rate over multiple consecutive preset time periods; and a fourth module 840, used to divert and schedule calls to an intelligent customer service system when the connection rate data meets preset rules.

[0157] It should be noted that, in some embodiments, the device further includes the following modules:

[0158] The fifth module is used to generate Docker images from pre-compiled executables based on the GO GIN framework, and then to containerize and deploy the target database, setting up a connectivity query API.

[0159] The connection rate query API is used to obtain the cumulative connection rate and time-series connection rate from the target database.

[0160] On the other hand, such as Figure 9 As shown, this embodiment of the invention also provides an electronic device 900, which includes at least one processor 910 and at least one memory 920 for storing at least one program; taking one processor 910 and one memory 920 as an example.

[0161] The processor 910 and memory 920 can be connected via a bus or other means.

[0162] Memory 920, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory 920 may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 920 may optionally include memory remotely located relative to the processor, and this remote memory can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0163] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Specifically, Figure 10 A schematic block diagram of a computer system architecture for implementing an electronic device according to embodiments of the present invention is shown.

[0165] It should be noted that, Figure 10 The computer system 1000 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0166] like Figure 10 As shown, the computer system 1000 includes a central processing unit (CPU) 1001, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 1002 or programs loaded from storage section 1008 into random access memory (RAM). The RAM 1003 also stores various programs and data required for system operation. The CPU 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output interface 1005 (I / O interface) is also connected to the bus 1004.

[0167] The following components are connected to the input / output interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a local area network card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1010 as needed so that computer programs read from it can be installed into the storage section 1008 as needed.

[0168] In particular, according to embodiments of the present invention, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by central processing unit 1001, it performs various functions defined in the system of the present invention.

[0169] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein computer-readable program code is carried. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0170] The content of the method embodiments of the present invention is applicable to the system embodiments. The specific functions implemented in the system embodiments are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0171] Another aspect of this invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the method described above.

[0172] The content of the method embodiments of the present invention is applicable to the computer-readable storage medium embodiments. The specific functions implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above methods.

[0173] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0175] It should be noted that although several modules for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0176] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0177] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0178] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0179] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0180] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution means, apparatus, or device (such as a computer-based device, a processor-including device, or other means that can fetch and execute instructions from, or in conjunction with, an instruction execution means, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution means, apparatus, or device.

[0181] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0182] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution device. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0183] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0184] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0185] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A call traffic offloading and scheduling method, characterized in that, include: Continuously retrieve call detail records (CDRs) from the queuing machine and transmit the CDRs to the target message queue; The call detail record (CDR) logs are filtered and parsed to obtain structured queuing machine interaction logs, which are then imported into the target database. The target database is periodically traversed to obtain the connection rate data of human-operated calls; The connection rate data includes cumulative connection rate and time-series connection rate; the time-series connection rate includes the connection rate over multiple consecutive preset time periods. When the connection rate data meets the preset rules, the intelligent customer service system will perform traffic diversion and scheduling.

2. The call routing and scheduling method according to claim 1, characterized in that, The step of continuously retrieving call detail record (CDR) logs from the queuing machine and transmitting the CDR logs to the target message queue includes: The call detail records (CDRs) of the queuing machine are dynamically collected using a log collection program; wherein, the log collection program includes the lightweight log collection program filebeat, which is pre-deployed on the queuing machine. The call detail record (CDR) logs are transmitted to the Kafka message queue asynchronously.

3. The call routing and scheduling method according to claim 1, characterized in that, The step of filtering and parsing the call detail record (CDR) logs to obtain structured queuing machine interaction logs includes: The call detail record (CDR) logs are transmitted to the log filtering program via the target message queue, and the structured queuing machine interaction logs processed by the log filtering program are obtained. The log filtering program extracts the log text of the call detail record (CDR) log according to a pre-configured regular expression, then matches and segments the log text and maps it to keywords to obtain a structured queuing machine interaction log.

4. The call routing and scheduling method according to claim 1, characterized in that, Before the step of periodically traversing the target database to obtain the connection rate data for human-operated calls, the method further includes: Based on the pre-compiled executable file of the GO GIN framework, a Docker image is generated, and then the target database is containerized and deployed to set up a connection rate query API. The connection rate query API is used to obtain the cumulative connection rate and the time-series connection rate from the target database.

5. The call routing and scheduling method according to claim 1, characterized in that, The queuing machine interaction log includes records of entering the human customer service queue and connecting to human customer service in each call detail record; The periodic traversal of the target database to obtain the connection rate data for human-operated calls includes: The target database is periodically traversed, and based on the records of entering the human customer service queue and connecting to human customer service in each call detail record, the first number of call detail records entering the human customer service queue and the second number of call detail records connecting to human customer service in each time period are determined in chronological order. Based on the first quantity and the second quantity, the connection rate data for human-operated calls is determined.

6. The call routing and scheduling method according to claim 5, characterized in that, The determination of the call connection rate data based on the first quantity and the second quantity includes: The cumulative connection rate is determined based on the ratio of the sum of the second quantity across all time periods to the sum of the first quantity. The timing connection rate is determined based on the ratio of the second quantity to the first quantity in each preset time period.

7. The call routing and scheduling method according to claim 1, characterized in that, When the connection rate data meets preset rules, the intelligent customer service system will perform traffic diversion and scheduling, including: When the cumulative connection rate is higher than the first proportion threshold, the control standard level is determined based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the second proportion threshold, and then the target diversion scheduling strategy is determined, and a scheduling instruction is sent to the intelligent customer service system. Alternatively, when the cumulative connection rate is lower than the first proportion threshold, the control standard level is determined based on the number of preset time periods in the time-series connection rate where the connection rate is lower than the third proportion threshold, and then the target diversion scheduling strategy is determined and a scheduling instruction is sent to the intelligent customer service system. Based on the scheduling instruction, the login text is obtained from the intelligent customer service system, and the login text is placed in the interface header of the intelligent customer service system to execute the target diversion scheduling strategy.

8. A call traffic diversion and scheduling device, characterized in that, include: The first module is used to continuously obtain call detail records (CDRs) from the queuing machine and transmit the CDRs to the target message queue. The second module is used to filter and parse the call detail record logs to obtain structured queuing machine interaction logs, and import the queuing machine interaction logs into the target database. The third module is used to periodically traverse the target database to obtain the connection rate data of human call traffic; The connection rate data includes cumulative connection rate and time-series connection rate; the time-series connection rate includes the connection rate over multiple consecutive preset time periods. The fourth module is used to perform traffic diversion and scheduling for the intelligent customer service system when the connection rate data meets preset rules.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the method as described in any one of claims 1 to 7.

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