Call task management method and device, electronic equipment and storage medium

Abstract

The invention discloses a call task management method and device, electronic equipment and a storage medium, and relates to the field of financial science and technology, and the method comprises the steps: obtaining the actual bearing capacity of each service node according to a call task borne by each service node and the maximum bearing capacity; if it is determined that at least one abnormal service node exists, obtaining a corresponding balance weight according to a call bearer proportion of the at least one abnormal service node in all service nodes; and allocating a new call task to each normal service node according to the actual bearing capacity and the balance weight of each normal service node. According to the technical scheme provided by the embodiment of the invention, the normal service nodes bear the call task of the abnormal service node, the response timeliness of the call task is ensured, the task accumulation phenomenon is avoided, the load balance of each normal service node is realized, and the processing efficiency of the call task is improved.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a call task management method, device, electronic device and storage medium. Background Art

[0002] With the continuous development of science and technology, intelligent outbound call systems have become an important part of the business systems of financial institutions, providing users with various services by automatically dialing customer calls.

[0003] In the prior art, the intelligent outbound call systems of various financial institutions are usually divided by region, and outbound call equipment is configured in different geographical locations. The outbound call equipment at the current location completes the local call; and the call task is sent down in a flood-like manner, that is, a large number of call tasks are sent to the current outbound call equipment at the same time. If it cannot be processed at the same time, it will be resent after waiting for a period of time, and this cycle is repeated until all call tasks are completed.

[0004] However, this call task management method not only has low call task processing efficiency and often causes task accumulation, but also cannot cope with abnormal situations such as sudden equipment failures, and has poor disaster recovery processing capabilities. Summary of the invention

[0005] The present invention provides a call task management method, device, electronic device and storage medium to solve the problems of low task processing efficiency and poor disaster recovery processing capability of an intelligent outbound call system.

[0006] According to one aspect of the present invention, a call task management method is provided, which is applied to a management node and includes:

[0007] According to the call tasks carried by each service node and the maximum carrying capacity, the actual carrying capacity of each service node is obtained;

[0008] If it is determined that there is at least one abnormal service node, obtaining a corresponding balancing weight according to the call bearer ratio of the at least one abnormal service node in all service nodes;

[0009] According to the actual carrying capacity of each normal service node and the balancing weight, a new call task is allocated to each normal service node.

[0010] The actual carrying capacity of each business node is obtained according to the call tasks carried by each business node and the maximum carrying capacity, including: obtaining the matching predicted end time according to the communication type of the call task carried by the current business node; wherein the communication type includes waiting for calls, non-interactive calls and interactive calls; the actual carrying capacity of the current business node is obtained according to the maximum carrying capacity of the current business node and the predicted end time of the carried call task.

[0011] The method of obtaining the matching predicted end time according to the communication type of the call task carried by the current service node includes at least one of the following: if it is determined that the current call task is a call waiting to be connected, obtaining the matching predicted end time according to the waiting time of the current call task; wherein the waiting time for the call waiting to be connected is negatively correlated with the predicted end time; if it is determined that the current call task is a call without interaction, obtaining the matching predicted end time according to the connection time of the current call task; wherein the connection time and the predicted end time of the call without interaction conform to the first normal distribution curve; if it is determined that the current call task is an interactive call, obtaining the matching predicted end time according to the connection time and the interaction frequency of the current call task; wherein the interaction frequency of the interactive call and the predicted end time conform to the second normal distribution curve, and the connection time of the interactive call and the predicted end time conform to the third normal distribution curve.

[0012] The actual carrying capacity of each business node is obtained according to the call tasks carried by each business node and the maximum carrying capacity, including: obtaining the matching resource occupancy according to the communication type of the call task carried by the current business node; obtaining the actual carrying capacity of the current business node according to the maximum carrying capacity of the current business node and the resource occupancy of the carried call task.

[0013] The actual carrying capacity of each business node is obtained according to the call tasks carried by each business node and the maximum carrying capacity, including: obtaining the matching predicted end time according to the business type of the call task carried by the current business node; obtaining the actual carrying capacity of the current business node according to the maximum carrying capacity of the current business node and the predicted end time of the carried call task.

[0014] The task management method also includes: if a health status message reported by the first business node is not obtained within a first preset time, sending a health detection message to the first business node; if the first business node is not obtained within a second preset time, configuring the first business node as an abnormal business node in response to a health feedback message sent by the health detection message; wherein the second preset time is greater than the first preset time.

[0015] According to another aspect of the present invention, there is provided a call task management device, comprising:

[0016] The carrying capacity acquisition module is used to obtain the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity;

[0017] A balancing weight acquisition module, configured to acquire a corresponding balancing weight according to a call carrying ratio of the at least one abnormal service node in all service nodes if it is determined that there is at least one abnormal service node;

[0018] The task allocation execution module is used to allocate new call tasks to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

[0019] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0020] at least one processor; and

[0021] a memory communicatively connected to the at least one processor; wherein,

[0022] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the call task management method described in any embodiment of the present invention.

[0023] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the call task management method described in any embodiment of the present invention when executed.

[0024] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the call task management method according to any embodiment of the present invention is implemented.

[0025] According to the technical solution of the embodiment of the present invention, the management node obtains the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity, and when determining that there is an abnormal service node, obtains the corresponding balancing weight according to the call carrying proportion of the abnormal service node in all service nodes; finally, new call tasks are allocated according to the actual carrying capacity and balancing weight of the normal service node. In this way, the normal service node carries the call tasks of the abnormal service node, ensuring the timely response of the call tasks, avoiding the phenomenon of task accumulation, improving the disaster recovery processing capability of the service system, and realizing the load balancing of each normal service node, thereby improving the processing efficiency of the call tasks.

[0026] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a schematic diagram of a scenario to which the call task management method provided by an embodiment of the present invention is applicable;

[0029] Figure 2 is a schematic diagram of the structure of a service node provided by an embodiment of the present invention;

[0030] Figure 3 is a flow chart of a call task management method provided according to Embodiment 1 of the present invention;

[0031] Figure 4 is a flowchart of another call task management method provided according to Embodiment 2 of the present invention;

[0032] Figure 5 is a flowchart of another call task management method provided according to Embodiment 3 of the present invention;

[0033] Figure 6 is a structural diagram of a call task management device provided according to Embodiment 4 of the present invention;

[0034] Figure 7 It is a structural schematic diagram of an electronic device for implementing the call task management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0037] A call task management method provided by an embodiment of the present invention can be applied to Figure 1 In the management node 100 of the intelligent outbound call system shown, the management node 100 sends call tasks to the service node 200, and then the service node 200 specifically executes each call task; wherein, the management node 100 can be composed of one or more servers, and each service node 200 can also be composed of one or more servers. The management node 100 and each service node 200 can be deployed in the same geographical location or in different geographical locations.

[0038] After acquiring the call task, the service node 200 initiates the call task through the communication operator 300 according to the communication operator 300 to which the call number belongs, and after the call is connected, it broadcasts the relevant preset content to the user according to the service information of the current call task, or broadcasts the relevant content based on the user's feedback information. Figure 2 As shown, each service node 200 may be specifically composed of an ingress server 201 , an outbound call server 202 and a gateway server 203 .

[0039] The entry server 201 is the communication medium between the current business node 200 and the management node 100. It is used to obtain the outbound call tasks issued by the management node 100, as well as the health detection messages issued by the management node 100, and to feedback or actively report the health status messages to the management node 100; the outbound call server 202 is used to manage and execute specific call tasks, including playing matching business information to the user during the call, and obtaining reply information triggered by the user through voice or keystrokes, and querying the feedback results through the database based on the above reply information, and broadcasting the feedback results to the user; the gateway server 203 is the communication medium between the business node and the communication operator 300. It is used to obtain the communication operator 300 to which the current call number belongs through the number portability database according to the call number, and then add the identification information of the communication operator 300 in front of the current call number, and send the call task to the corresponding communication operator to call the user through the communication operator 300.

[0040] Embodiment 1

[0041] Figure 3 This is a flowchart of a call task management method provided in the first embodiment of the present invention. This embodiment is applicable to the situation where the management node sends call tasks to each service node according to the carrying capacity of each service node. The method can be executed by the call task management device in any embodiment of the present invention. The call task management device can be implemented in the form of hardware and / or software. The call task management device can be configured in an electronic device such as a server. Figure 3 As shown, the method includes:

[0042] S101. Acquire actual carrying capacity of each service node according to call tasks carried by each service node and maximum carrying capacity.

[0043] The call tasks carried by the business node include tasks in progress, such as call tasks that have been called and connected, and call tasks that have been called but not connected; because the number and type of devices configured between different business nodes may be different, the maximum number of concurrent calls at the same time on different business nodes may be different.

[0044] The maximum carrying capacity can be expressed by the maximum number of concurrent calls at the same time; the more the maximum number of concurrent calls that can be made at the same time, the stronger the task carrying capacity; the fewer the maximum number of concurrent calls that can be made at the same time, the weaker the task carrying capacity; the actual carrying capacity indicates the number of call tasks that the service node can still carry; for the current service node, the difference between the maximum number of calls and the tasks in call (including the tasks that have been called and connected as mentioned above, as well as the tasks that have been called but not connected) can be used as the actual carrying capacity of the current service node.

[0045] At the same time, the actual carrying capacity can also be expressed by how long it takes to complete the tasks in the current call. The shorter the processing time of the tasks in the current call, the faster the task processing resources can be released to take on more new call tasks, and the stronger the carrying capacity of the business node. The longer the processing time of the tasks in the current call, the more difficult it is to release task processing resources in a short time to take on more new call tasks, and the weaker the task carrying capacity of the business node.

[0046] Therefore, the actual carrying capacity can be obtained based on the predicted completion time of the task in the current call, and the predicted completion time is negatively correlated with the actual carrying capacity; that is, if the predicted completion time of the task in the current call is shorter, that is, the system resources used to process the current task can be released faster to undertake new call tasks, then the score mapped by the task will be higher. Adding the mapping scores of the tasks in each call in the current business node can represent the actual carrying capacity of the current business node; the larger the addition result, the stronger the actual carrying capacity; the smaller the addition result, the weaker the actual carrying capacity.

[0047] The average call time of all historical call tasks can be obtained based on historical call records, and then the above average call time can be set as the predicted task time of the tasks in each call; wherein, the predicted task time refers to the time from the outgoing call of the task to the end (i.e., hang up) of the task; the predicted end time refers to the time from the current sampling moment to the end moment; the predicted task time minus the time when the task in the current call has been started is the predicted end time. If the predicted end time is a negative value, the value can be regarded as 0, that is, the current predicted end time is extremely short.

[0048] For example, the maximum number of calls of service node A is equal to that of service node B, and service node A has more in-call tasks than service node B. However, since the processing time of each in-call task in service node A is shorter, it may release system resources more quickly to take on new call tasks. Then, based on the difference in the number of in-call tasks and the mapping score, when the actual carrying capacity of the service nodes is calculated together, the calculation result may be that the actual carrying capacity of service node A is stronger than that of service node B.

[0049] In addition, the call tasks carried by the service node may also include tasks in call waiting; wherein, tasks in call waiting refer to tasks that are in the call waiting queue and have not issued a call request; the actual carrying capacity can also be represented by the ratio of call waiting tasks to the maximum number of calls, and this ratio is also negatively correlated with the actual carrying capacity; the larger the ratio is, the longer it takes for the service node to complete all tasks, that is, the weaker the actual carrying capacity; the smaller the ratio is, the shorter the time it takes for the service node to complete all tasks, that is, the stronger the actual carrying capacity; accordingly, the actual carrying capacity of each service node can be calculated based on one or more of the difference in the number of tasks in call and the mapping score, as well as the ratio of the number of call waiting tasks.

[0050] S102: If it is determined that there is at least one abnormal service node, obtain a corresponding balancing weight according to a call bearer ratio of the at least one abnormal service node in all service nodes.

[0051] The business node can periodically report its health status and send a health status message to the management node to inform the management node that it can process call tasks normally, that is, it is a normal business node; the management node can also periodically send down health detection messages to guide the business node to detect its own health status, and guide the business node to send a health feedback message after the detection is completed. When the management node receives the health feedback message, it can determine that the business node is a normal business node; and when the management node does not receive the health status message or health feedback message sent by the business node, it can be determined that the current business node has a call task processing failure, that is, it is determined to be an abnormal business node.

[0052] When the management node determines that there are one or more abnormal service nodes in the intelligent outbound call system, the management node adds the sum of the maximum call numbers of all abnormal service nodes to the proportion of the total maximum call numbers of all service nodes and the value 1, and the sum is the balancing weight; wherein, the balancing weight indicates that the normal service node needs to share the call tasks originally carried by the abnormal service node to increase its own workload, and the specific load ratio after the increase is the above-mentioned balancing weight.

[0053] Optionally, in an embodiment of the present invention, the task management method further includes: if the health status message reported by the first business node is not obtained within a first preset time, sending a health detection message to the first business node; if the first business node is not obtained within a second preset time, configuring the first business node as an abnormal business node in response to the health feedback message sent by the health detection message; wherein the second preset time is greater than the first preset time.

[0054] Specifically, as described in the above technical solution, if the business node uses the first preset time as the sending period of health status messages to the management node, then if the management node does not obtain the health status message reported by the first business node within the first preset time, it means that there is an abnormality in the health detection mechanism or communication mechanism of the first business node. At this time, the management node sends a health detection message to the first business node to guide the first business node to detect its own health status.

[0055] Since it takes a long time for the first business node to perform its own status detection, the second preset time is usually set to a larger value, that is, a value greater than the first preset time. After waiting for the second preset time, if the first business node can send a health feedback message in response to the health detection message, it indicates that the self-detection mechanism of the first business node may be abnormal, but the communication mechanism and health status are normal. At this time, the first business node is still regarded as a normal business node.

[0056] If after waiting for the second preset time, the health feedback message sent by the first business node is still not obtained, the first business node is regarded as an abnormal business node. Therefore, when an abnormality occurs in the health status reporting mechanism of the business node, a health detection message is sent to guide the business node to perform a self-check operation of the health status again, which not only ensures the timeliness of the detection of abnormal business nodes, but also avoids the occurrence of false detection of abnormal business nodes.

[0057] S103: Allocate a new call task to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

[0058] The actual carrying capacity of each normal business node is multiplied by the balancing weight respectively, and the product result is the proportion of the number of new call tasks that each normal business node should actually carry. Finally, the number of new call tasks carried by each normal business node is determined according to the total number of new call tasks; accordingly, according to the new call task set, according to the number of new call tasks that each normal business node should carry, new call tasks are allocated to each normal business node; then at the next sampling time, the call tasks carried by each business node continue to be obtained, and based on the above technical solution, the allocation of new call tasks in the new call task set continues to be completed until there are no call tasks in the new call task set.

[0059] Optionally, in an embodiment of the present invention, the actual carrying capacity of each business node is obtained based on the call tasks carried by each business node and the maximum carrying capacity, including: obtaining the business type of the call task carried by the current business node; obtaining the matching predicted end time based on the business type of the call task carried by the current business node; obtaining the actual carrying capacity of the current business node based on the maximum carrying capacity of the current business node and the predicted end time of the carried call task.

[0060] Specifically, call tasks of different business types provide different business content to users, that is, the length of the voice paragraphs broadcast to users is different, and the broadcasting time is naturally different; at the same time, some call tasks may only send prompt messages to users and do not require users to make feedback, so the task time of this type of call tasks is shorter; while some call tasks not only require users to reply, but also need to reply based on the user's reply result query, obviously this type of call task has a longer task time; therefore, according to the business type of each call task carried by the current business node, the matching predicted end time can be obtained respectively; then, as described in the above technical solution, the actual carrying capacity of the current business node is obtained based on the sum of the predicted end time, thereby improving the accuracy of obtaining the predicted end time of the task in each call, ensuring the accuracy of calculating the actual carrying capacity, thereby ensuring the load balancing of the business node.

[0061] According to the technical solution of the embodiment of the present invention, the management node obtains the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity, and when determining that there is an abnormal service node, obtains the corresponding balancing weight according to the call carrying proportion of the abnormal service node in all service nodes; finally, new call tasks are allocated according to the actual carrying capacity and balancing weight of the normal service node. In this way, the normal service node carries the call tasks of the abnormal service node, ensuring the timely response of the call tasks, avoiding the phenomenon of task accumulation, improving the disaster recovery processing capability of the service system, and realizing the load balancing of each normal service node, thereby improving the processing efficiency of the call tasks.

[0062] Embodiment 2

[0063] Figure 4 This is a flowchart of a call task management method provided by Embodiment 2 of the present invention. The relationship between this embodiment and the above embodiment is that the actual carrying capacity of the service node is obtained according to the communication type of the call task carried by the service node. Figure 4 As shown, the method includes:

[0064] S201. Obtain a matched predicted end time according to a communication type of a call task carried by a current service node; wherein the communication type includes a call waiting to be connected, a call without interaction, and a call with interaction.

[0065] The calls currently made by the business node can be specifically divided into calls waiting to be connected, calls without interaction, and calls with interaction; among them, calls waiting to be connected refer to calls that have been made and have not yet been answered by the user; for these calls, since they are in an unconnected state, it is not certain whether the user will answer the call successfully, nor is it certain the specific call time after the user is connected. At this time, the average call time of all historical call tasks can be used as the predicted task time for this type of call task; then, based on the started time of the current call task and the above-mentioned predicted task time, the predicted end time of the current call task is obtained.

[0066] A non-interaction call refers to a call that has been made and the user has answered the call, but the user has not provided any feedback on the call content through voice or keystrokes. In this case, the average call time of all historical call tasks answered by the user can be used as the predicted task duration of this type of call task. The predicted end time of the current call task can be obtained based on the started time of the current call task and the above predicted task duration.

[0067] An interactive call refers to a call that has been made and the user has provided feedback on the call content through voice or keystrokes. In this case, the average call time of all historical call tasks with user interactive behaviors can be used as the predicted task duration of this type of call task. The predicted end time of this type of call task can be obtained based on the started time of the current call task and the above predicted task time.

[0068] S202: Acquire the actual carrying capacity of the current service node according to the maximum carrying capacity of the current service node and the predicted end time of the carried call task.

[0069] The predicted task times for waiting for calls to be connected, calls without interactions, and calls with interactions increase in sequence. Thus, according to the different communication types of call tasks, different values ​​of predicted task times are obtained, and then based on the start time of the current call task, the predicted end time is calculated to obtain the predicted end time. This improves the accuracy of the calculation results of the predicted end time, ensures the reasonable allocation of subsequent call tasks, and improves the load balancing of each normal business node.

[0070] Optionally, in an embodiment of the present invention, the actual carrying capacity of each business node is obtained based on the call tasks carried by each business node and the maximum carrying capacity, including: obtaining the matching resource occupancy based on the communication type of the call task carried by the current business node; obtaining the actual carrying capacity of the current business node based on the maximum carrying capacity of the current business node and the resource occupancy of the carried call task.

[0071] Specifically, for call tasks of different communication types, the node resources that the business node needs to use when processing are different, that is, the resource occupancy of the call task is different; taking the above-mentioned technical solution as an example, waiting for a call to be connected, since it is in the waiting state, no business content is currently broadcast, and only fewer resources are needed to maintain the call. Therefore, the resource occupancy of this type of call task is the lowest.

[0072] In the case of non-interactive calls, only the pre-configured business content needs to be broadcasted, and no operations such as information query and query result feedback are performed. Therefore, the node resource usage is relatively low. In the case of interactive calls, not only the pre-configured business content needs to be broadcasted, but also the relevant content needs to be queried based on the user's reply results, and feedback information is broadcasted based on the query results. Therefore, the node resource usage is relatively high.

[0073] The lower the resource occupancy of each call task currently undertaken by the business node, the more new call tasks its remaining node resources can handle, that is, the stronger its actual carrying capacity; accordingly, the resource occupancy of each call task corresponds to a matching mapping score, and the lower the resource occupancy, the higher the mapping score; adding the mapping scores of each call task of the current business node can represent the actual carrying capacity of the current business node; the larger the addition result, the stronger the actual carrying capacity; the smaller the addition result, the weaker the actual carrying capacity. In addition, the actual carrying capacity can also be calculated based on the difference in the number of tasks in the current business node, the mapping score of the predicted end time, and the mapping score of the resource occupancy.

[0074] Thus, the actual carrying capacity of the current service node is evaluated according to the resource occupancy corresponding to the communication type of the call task, which not only fully utilizes the node resources of each service node, but also avoids excessive system load of the service node, ensuring the stable operation of the system of the service node. In particular, the above-mentioned node resources can refer to hardware resources such as central processing unit, memory and hard disk, or software resources such as operating system, application program and driver; optionally, in the embodiment of the present invention, the type of node resources is not specifically limited.

[0075] S203: If it is determined that there is at least one abnormal service node, obtain a corresponding balancing weight according to the call bearer ratio of the at least one abnormal service node in all service nodes.

[0076] S204: Allocate a new call task to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

[0077] According to the technical solution of the embodiment of the present invention, the management node obtains the matching predicted end time according to the communication type of the call task carried by the current business node, and then obtains the actual carrying capacity of the current business node according to the predicted end time, thereby improving the accuracy of the calculation result of the predicted end time, ensuring the reasonable allocation of subsequent call tasks, and improving the load balancing of each normal business node.

[0078] Embodiment 3

[0079] Figure 5 This is a flowchart of a call task management method provided by Embodiment 3 of the present invention. The relationship between this embodiment and the above embodiments is that the waiting time or connection time of call tasks of different communication types has different mapping relationships with the predicted end time. Figure 5 As shown, the method includes:

[0080] S301. If it is determined that the current call task is a call waiting to be connected, obtain a matching predicted end time according to the waiting time of the current call task; wherein the waiting time for the call waiting to be connected is negatively correlated with the predicted end time.

[0081] During the waiting time when a call is just made, the user often needs some time to react to decide whether to answer the call. The user may choose to hang up directly or answer the call. Therefore, in the initial stage of the call, that is, when the waiting time is short, the predicted call time of this call task is actually an average value with a relatively moderate value (for example, duration A); wherein duration A can be obtained based on historical call records.

[0082] As the call waiting time continues to increase, the possibility that the user is not next to the phone gradually increases, resulting in a further increase in the possibility that the call will not be answered. Therefore, when the waiting time is longer, the predicted call time of this call task is actually a smaller value (for example, duration B). The longer the waiting time, the smaller the value and the closer it is to the maximum call waiting time; wherein, the maximum call waiting time is the waiting time set by the communication operator for automatic hanging up when the call is not answered; therefore, the above waiting time is actually negatively correlated with the predicted end time; and the change curve of the above negative correlation can be obtained based on historical call tasks.

[0083] S302: If it is determined that the current call task is a non-interactive call, obtain a matching predicted end time according to the connection time of the current call task; wherein the connection time of the non-interactive call and the predicted end time conform to a first normal distribution curve.

[0084] After the call is connected, if the user is not interested in the content of the call, he or she will often hang up quickly. Therefore, if the call connection time of the current call task is short, there is also a high possibility that the call will be hung up quickly, that is, the predicted end time of this call task is a small value; and as the connection time gradually increases, it shows that the user is patiently listening to the specific content of this call task, and it is obvious that the user is more interested, so the predicted end time is a large value.

[0085] As the connection time is extended again, the business content of this call task is gradually broadcast, and the possibility of terminating this call task continues to increase. At this time, the predicted termination time is actually a smaller value; from the above technical solution, it can be seen that for non-interactive calls, the predicted termination time first increases with the increase of the connection time, and then decreases with the increase of the connection time. On the whole, it conforms to the normal distribution curve relationship, and the specific normal distribution curve (i.e., the first normal distribution curve) can be obtained according to the historical call tasks.

[0086] S303. If it is determined that the current call task is an interactive call, obtain a matching predicted end time according to the connection time and interaction frequency of the current call task; wherein the interaction frequency of the interactive call and the predicted end time conform to the second normal distribution curve, and the connection time of the interactive call and the predicted end time conform to the third normal distribution curve.

[0087] For interactive calls, at the beginning, as the number of interactive frequencies increases, the user's interest becomes higher and higher, so the predicted end time is a gradually increasing value; and as the number of interactive frequencies increases again, the user has gradually learned more complete information, so the corresponding predicted end time will gradually decrease, which also conforms to the normal distribution curve relationship, and the specific normal distribution curve (i.e., the second normal distribution curve) can be obtained based on historical call tasks; similarly, similar to non-interactive calls, the connection time and predicted end time of interactive calls also have a numerical change relationship that first rises and then falls, which also conforms to the normal distribution curve relationship, and the specific normal distribution curve (i.e., the third normal distribution curve) can also be obtained based on historical call tasks. In particular, according to the connection time and interaction frequency of the current call task, the predicted end time can be obtained based on two normal distribution curves respectively, and the average of the above two predicted end times, or the weighted calculated value, is used as the final predicted end time.

[0088] S304: If it is determined that there is at least one abnormal service node, obtain a corresponding balancing weight according to the call bearer ratio of the at least one abnormal service node in all service nodes.

[0089] S305: Allocate a new call task to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

[0090] The technical solution of the embodiment of the present invention obtains the matching predicted end time through different numerical mapping relationships according to the waiting time for waiting to connect the call, the connection time of the call without interaction, and the interaction frequency of the call with interaction and the predicted end time, thereby further improving the accuracy of the calculation result of the predicted end time, ensuring the reasonable allocation of subsequent call tasks, and improving the load balancing of each normal business node.

[0091] Embodiment 4

[0092] Figure 6 : is a structural block diagram of a call task management device provided by Embodiment 4 of the present invention, the device specifically comprises:

[0093] The carrying capacity acquisition module 401 is used to acquire the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity;

[0094] The balancing weight acquisition module 402 is used to acquire the corresponding balancing weight according to the call carrying ratio of the at least one abnormal service node in all service nodes if it is determined that there is at least one abnormal service node;

[0095] The task allocation execution module 403 is used to allocate a new call task to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

[0096] According to the technical solution of the embodiment of the present invention, the management node obtains the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity, and when determining that there is an abnormal service node, obtains the corresponding balancing weight according to the call carrying proportion of the abnormal service node in all service nodes; finally, new call tasks are allocated according to the actual carrying capacity and balancing weight of the normal service node. In this way, the normal service node carries the call tasks of the abnormal service node, ensuring the timely response of the call tasks, avoiding the phenomenon of task accumulation, improving the disaster recovery processing capability of the service system, and realizing the load balancing of each normal service node, thereby improving the processing efficiency of the call tasks.

[0097] Optionally, the carrying capacity acquisition module 401 is specifically used to obtain a matching predicted end time according to the communication type of the call task carried by the current business node; wherein the communication type includes waiting for a call to be connected, a call without interaction, and a call with interaction; and obtain the actual carrying capacity of the current business node according to the maximum carrying capacity of the current business node and the predicted end time of the carried call task.

[0098] Optionally, the carrying capacity acquisition module 401 is also specifically used to, if it is determined that the current call task is a call waiting to be connected, obtain a matching predicted end time according to the waiting time of the current call task; wherein, the waiting time for the call waiting to be connected is negatively correlated with the predicted end time; if it is determined that the current call task is a call without interaction, obtain a matching predicted end time according to the connection time of the current call task; wherein, the connection time and the predicted end time of the call without interaction conform to a first normal distribution curve; if it is determined that the current call task is an interactive call, obtain a matching predicted end time according to the connection time and interaction frequency of the current call task; wherein, the interaction frequency of the interactive call and the predicted end time conform to a second normal distribution curve, and the connection time of the interactive call and the predicted end time conform to a third normal distribution curve.

[0099] Optionally, the carrying capacity acquisition module 401 is also used to obtain the matching resource occupancy according to the communication type of the call task carried by the current business node; and to obtain the actual carrying capacity of the current business node according to the maximum carrying capacity of the current business node and the resource occupancy of the carried call task.

[0100] Optionally, the carrying capacity acquisition module 401 is also used to obtain the matching predicted end time according to the service type of the call task carried by the current service node; and obtain the actual carrying capacity of the current service node according to the maximum carrying capacity of the current service node and the predicted end time of the carried call task.

[0101] Optionally, the call task management device is also used to send a health detection message to the first service node if the health status message reported by the first service node is not obtained within a first preset time; if the first service node is not obtained within a second preset time, configure the first service node as an abnormal service node in response to the health feedback message sent by the health detection message; wherein the second preset time is greater than the first preset time.

[0102] The above device can execute the call task management method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the call task management method provided by any embodiment of the present invention.

[0103] Embodiment 5

[0104] Figure 7 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, electronic devices, blade electronic devices, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0105] like Figure 7 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0106] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0107] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the call task management method.

[0108] In some embodiments, the call task management method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program can be loaded and / or installed on a heterogeneous hardware accelerator via a ROM and / or a communication unit. When the computer program is loaded into RAM and executed by a processor, one or more steps of the call task management method described above can be performed. Alternatively, in other embodiments, the processor can be configured to perform the call task management method by any other appropriate means (e.g., by means of firmware).

[0109] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or electronic device.

[0111] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide interaction with a user, the systems and techniques described herein may be implemented on a heterogeneous hardware accelerator having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) through which a user can provide input to the heterogeneous hardware accelerator. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data electronic device), or a computing system that includes middleware components (e.g., an application electronic device), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] The computing system may include a client and an electronic device. The client and the electronic device are generally remote from each other and usually interact through a communication network. The relationship between the client and the electronic device is generated by computer programs running on corresponding computers and having a client-electronic device relationship with each other. The electronic device may be a cloud electronic device, also known as a cloud computing electronic device or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0115] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0116] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A call task management method, characterized in that: Applicable to management nodes, including: According to the call tasks carried by each service node and the maximum carrying capacity, the actual carrying capacity of each service node is obtained; If it is determined that there is at least one abnormal service node, obtaining a corresponding balancing weight according to the call bearer ratio of the at least one abnormal service node in all service nodes; According to the actual carrying capacity of each normal service node and the balancing weight, a new call task is allocated to each normal service node.

2. The method according to claim 1, characterized in that The obtaining of the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity includes: According to the communication type of the call task carried by the current service node, the matching predicted end time is obtained; wherein the communication type includes waiting for a call to be connected, a call without interaction, and a call with interaction; The actual carrying capacity of the current service node is obtained according to the maximum carrying capacity of the current service node and the predicted end time of the carried call task.

3. The method according to claim 2, characterized in that The obtaining of the matched predicted end time according to the communication type of the call task carried by the current service node includes at least one of the following: If it is determined that the current call task is a call waiting to be connected, the matching predicted end time is obtained according to the waiting time of the current call task; wherein the waiting time of the call waiting to be connected is negatively correlated with the predicted end time; If it is determined that the current call task is a non-interactive call, the matched predicted end time is obtained according to the connection time of the current call task; wherein the connection time of the non-interactive call and the predicted end time conform to the first normal distribution curve; If it is determined that the current call task is an interactive call, obtain the matching predicted end time according to the connection time and interaction frequency of the current call task; wherein, the interaction frequency and the predicted end time of the interactive call conform to the second normal distribution curve, and the connection time of the interactive call and the predicted end time conform to the third normal distribution curve.

4. The method according to claim 1, characterized in that: The obtaining of the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity includes: According to the communication type of the call task carried by the current service node, the matching resource occupancy is obtained; The actual carrying capacity of the current service node is obtained according to the maximum carrying capacity of the current service node and the resource occupation of the carried call task.

5. The method according to claim 1, characterized in that The obtaining of the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity includes: According to the service type of the call task carried by the current service node, obtain the matching predicted end time; The actual carrying capacity of the current service node is obtained according to the maximum carrying capacity of the current service node and the predicted end time of the carried call task.

6. The method according to claim 1, characterized in that The task management method further includes: If the health status message reported by the first service node is not obtained within the first preset time, sending a health detection message to the first service node; If the first service node is not acquired within a second preset time, the first service node is configured as an abnormal service node in response to a health feedback message sent by the health detection message; wherein the second preset time is greater than the first preset time.

7. A call task management device, characterized in that: include: The carrying capacity acquisition module is used to obtain the actual carrying capacity of each service node according to the call tasks carried by each service node and the maximum carrying capacity; A balancing weight acquisition module, configured to acquire a corresponding balancing weight according to a call carrying ratio of the at least one abnormal service node in all service nodes if it is determined that there is at least one abnormal service node; The task allocation execution module is used to allocate new call tasks to each normal service node according to the actual carrying capacity of each normal service node and the balancing weight.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the call task management method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the call task management method according to any one of claims 1 to 6 when executed.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the call task management method according to any one of claims 1 to 6 is implemented.

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