CN110929966A - Customer service real-time scheduling method, computer-readable storage medium and electronic device - Google Patents

Abstract

The invention discloses a customer service real-time scheduling method, a nonvolatile computer readable storage medium and electronic equipment, comprising: partitioning the customer service queue according to the pressure state of the customer service; determining a pressure sequencing value of the customer service in the customer service queue according to the pressure value of the customer service, and inserting the customer service into the customer service queue according to the pressure sequencing value; dividing each subarea in the customer service queue into a first subarea and a second subarea, putting all the customer services which are not allocated to the customers under the subarea into the second subarea, and transferring the customer services which are allocated to the customers in the second subarea and hope to be continuously allocated to the customers from the second subarea to the first subarea. Based on the same online customer service queue, the invention adopts two-stage partition strategies of pressure partition sequencing, first sub-partition sequencing, second sub-partition sequencing, concurrent sequencing in the same partition and the like, and an offset sequencing value under concurrent sequencing in the same partition, thereby improving the customer service quality, the service efficiency and the data consistency, and improving the capability of processing concurrent scheduling.

Description

Customer service real-time scheduling method, computer-readable storage medium and electronic device

technical field

The present invention relates to the technical fields of computers and the Internet, and in particular, to a real-time scheduling method for customer service, a computer-readable storage medium and an electronic device.

Background technique

At present, in the customer service under the Internet environment, online customer service is one of the important links. For a better user experience, various scheduling between customer service is often required. The usual method of customer service scheduling is that customers under the same group are usually placed in the same scheduling queue. When a customer arrives, a suitable customer service agent is selected from the scheduling queue and assigned to the customer. This process is customer service scheduling.

Fig. 1 shows a general schematic diagram of real-time scheduling of customer service stress state. As shown in Figure 1, customer service events are generally divided into customer service behaviors such as login, logout, state change, heartbeat, and seat change, where the seat is the seat unit where the customer service receives customers. The real-time pressure scheduling algorithm adjusts operations such as adding, changing the order, and deleting the customer service in the customer service queue (ie, the scheduling queue) according to these customer service events.

In order to bring a better experience to customers and improve the efficiency of customer service itself, scheduling has become more refined and intelligent. Among them, according to a long-term online customer service online data analysis, the behavior of customer service under different stress conditions is different. In general, traditional customer service scheduling will only be done according to the number of customers the customer service receives. Usually, the customer service with fewer customers will be given priority. This scheduling is an evenly distributed scheduling strategy, no matter what stress state the customer service is in. However, this scheduling strategy is not suitable in many scenarios, and there is a waste of customer service resources. For example: a business has 10 customer service staff, and each customer service customer has the ability to receive 10 customers at the same time. When the business is at a low period, a total of 10 customers visit the business. Under the average scheduling strategy, each customer service Can be assigned to a customer, which is a waste of customer service resources. It is also unreasonable for other customer service agents to manually suspend or log out after serving customers, because in many cases, customer service does not know whether the subsequent customer traffic will increase. Once a large number of customers suddenly flood in, it will lead to no Terrible condition of customer service. Therefore, a scheduling strategy is needed to assign customers to some agents, and to ensure that other agents are still online, and other agents can engage in other tasks such as email tracking, business improvement learning, etc., until customers are reassigned.

The above scenarios show that it is very necessary to adopt different real-time scheduling strategies for different customer service pressure states. For example: when the customer service is in an idle state, multiple customers can be allocated continuously, which can make the customer service enter the service state quickly; and when the customer service is in a saturated state, only customers are allocated to the customer service one by one intermittently, so that no matter It is a better choice for the quality of service and the pressure on the customer service; when the customer service is in a state of bursting, the customer will no longer be assigned to the customer service, but other operations such as leaving a message or queuing can be performed, otherwise it will be difficult to guarantee the customer service. service quality.

Adopting different scheduling strategies for different customer service pressure states can better improve customer service quality and make customer service more efficient. However, in the case of online allocation under high concurrency, the performance and data are consistent. Sexually, there are more challenges. And the pressure state of the customer service itself changes dynamically, which also makes the real-time scheduling scenario more complicated.

There are generally two types of existing customer service real-time scheduling mechanisms: set screening method and multi-sequence queue method. (1) Set screening method

The set filtering method does not deal with customer service events, and directly puts customers in an unordered set.

Using the set screening method, the performance of the customer service entering the set is very high. When a customer arrives, the customer service that matches the customer will be screened out from the collection. This will not reduce the efficiency in the case of a small number of customer service personnel, but for a large number of customer service personnel, the efficiency will change. It is low, because it needs to be compared one by one to filter out the customer service that best matches the corresponding customer.

Therefore, there is also a certain improvement in the collection screening method. The improved collection screening method is to put the customer service into multiple collections according to different states, which can bring some performance improvements, but in essence, it is necessary to classify Therefore, the performance of the improved ensemble screening method is still poor.

(2) Multiple sequential queue method

The main idea of the multi-sequence queue method is to classify the stress state of the customer service, and place the customer service in several different sequential queues when the customer service event is triggered. In this way, when a customer arrives, it traverses several sequential queues according to a certain priority to find a customer service suitable for the customer. At the same time, for different pressure state queues, the multi-sequence queue method can adopt different scheduling algorithm strategies. This mechanism is when the customer service event comes: the customer service calculates its sorting value in the sequential queue under the scheduling algorithm, and then inserts it into the appropriate position in the sequential queue. This step is slightly time-consuming, but in distributed caches such as Redis Under the storage structure of the SortedSet, the performance is still good.

The problem with the multi-sequence queue method is that if the concurrency of customer service events is relatively intensive, the customer service needs to switch between different sequential queues, so it is more difficult to ensure data consistency and concurrent performance, and the complexity will also increase. In a continuous iterative production environment, it is difficult to guarantee continuous reliability.

SUMMARY OF THE INVENTION

In view of this, the present invention provides a customer service real-time scheduling method, a computer-readable storage medium and an electronic device, so as to achieve efficient performance of customer service real-time scheduling, reduce the complexity of the scheduling method, and ensure the consistency of status data.

The technical scheme of the present invention is realized as follows:

A real-time scheduling method for customer service, comprising:

According to the pressure state of the customer service, the customer service queue is divided into partitions;

Determine the pressure ranking value of the customer service in the customer service queue according to the pressure value of the customer service, and insert the customer service into the customer service queue according to the pressure ranking value;

Divide each partition in the customer service queue into a first sub-region and a second sub-region, put all the customer service personnel under the partition that have not been assigned customers into the second sub-region, and put the second sub-region in the second sub-region. The agents assigned to the customer and wishing to continue to be assigned to the customer are transferred from the second sub-area to the first sub-area.

Further, the transfer of the customer service agent who is assigned to the customer in the second sub-area and wishes to continue to be assigned to the customer from the second sub-area to the first sub-area includes:

The pressure ranking value of the customer service is adjusted using a time offset factor to transfer the customer service from the second sub-region to the first sub-region.

Further, the pressure ranking value of the customer service in the second sub-area is:

score ₁ =N _a +[( _ci -c _a )/(c _b -c _a )]×(N _b -N _a )

Among them, score ₁ is the pressure ranking value of the customer service in the second sub-area, _{Na is the first boundary value of the partition to which the second sub-area and the first sub-area belong, and N b is} the first sub-area. The second boundary value of the partition to which the second sub-region and the first sub-region belong, c _a is the pressure value corresponding to _Na , c _b is the pressure value corresponding to N _b , and c _i is the pressure value in the second sub-region The pressure value of any customer service of , where N _b >N _a , c _b > _ci >c _a , and c _b , c _a and c _i are all integers.

Further, the pressure ranking value of the customer service in the first sub-area is:

score ₂ =N _a +[offset/(c _b -c _a )]×(N _b -N _a )

Wherein, score ₂ is the pressure ranking value of the customer service in the first sub-area, _{Na is the second sub-area and the first boundary value of the partition to which the first sub-area belongs, and N b is} the first boundary value of the first sub-area. The second boundary value of the partition to which the second sub-region and the first sub-region belong, c _a is the pressure value corresponding to _Na , c _b is the pressure value corresponding to N _b , offset is the time offset factor, so The time offset factor is the ratio of the time stamp when the customer service is assigned to the customer and the maximum value of the long integer, where N _b >N _a , c _b >c _a , and c _b and c _a are integers.

Further, for multiple clients with the same pressure ranking value in the second sub-area, the time offset factor is used to fine-tune the pressure ranking values among the multiple clients, and the multiple clients are sorted according to the fine-tuned pressure ranking values. put in order.

Further, the fine-tuned pressure ranking values of multiple customer service agents with the same pressure ranking value in the second sub-area are:

score ₃ =N _a +[( _ci -c _a +offset)/(c _b -c _a )]×(N _b -N _a )

Wherein, score ₃ is the fine-tuned pressure ranking value, _{Na is the first boundary value of the partition to which the second sub-area and the first sub-area belong, and N b is} the second sub-area and the The second boundary value of the partition to which the first sub-region belongs, c _a is the pressure value corresponding to _Na , c _b is the pressure value corresponding to N _b , and c _i is the pressure of any customer service in the second sub-region value, offset is the time offset factor, and the time offset factor is the ratio of the timestamp when any customer service is inserted into the customer service queue and the maximum value of the long integer, where N _b >N _a , c _b > _ci >c _a , where c _b , c _a and c _i are all integers.

Further, the partitions include idle partitions, active partitions, saturated partitions and burst partitions.

Further, when any customer service event arrives, the customer service pressure is evaluated to obtain the pressure value, and then the pressure ranking value is determined and the customer service is inserted into the customer service queue according to the pressure ranking value.

A non-volatile computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to perform the customer service real-time service described in any of the above Steps in a schedule method.

An electronic device comprising:

at least one processor; and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor executes the method for real-time scheduling of customer service according to any one of the above. A step of.

It can be seen from the above solution that the real-time scheduling method for customer service, the non-volatile computer-readable storage medium and the electronic device of the present invention are based on the same online customer service queue, using pressure partition sorting, and sorting the first sub-region and the second sub-region. Two-level partitioning strategies such as concurrent sorting in the same area have great advantages in improving customer service quality, service efficiency, and the consistency of the state of the dispatcher. At the same time, the present invention also provides the offset sorting value under the concurrency of the same area, thereby improving the ability to handle concurrent scheduling. In addition, there are no redundant assumptions and constraints in the present invention, so it has strong scalability and can adapt to a variety of scheduling scenarios. Applying the technical solution of the present invention to a distributed cache queue can also make its performance better. big boost.

Description of drawings

Figure 1 is a schematic diagram of real-time scheduling of customer service pressure status;

2 is a schematic diagram of steps of a real-time scheduling method for customer service according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the sorting of pressure zones in the real-time scheduling method for customer service according to an embodiment of the present invention;

4 is a schematic diagram of a chief allocation sub-area in an embodiment of the present invention;

5 is a schematic diagram of concurrent sorting in the same region in an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of an electronic device in an embodiment of the present invention.

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

Figure 1 shows the real-time scheduling process of customer service pressure status. As shown in Figure 1, as long as there is a customer service event, the real-time pressure scheduling algorithm will be triggered. The result of the real-time pressure scheduling algorithm determines whether the customer service is added, changed or deleted in the customer service queue. one of the operations. In order to give customers a better experience, it is necessary to sort the customer service according to the pressure from weak (idle side) to strong (explosive side), and the principle of allocation is generally from weak to strong. In order to improve the efficiency of customer service, it is necessary to classify the stress state of customer service. As shown in Figure 1, the stress state of customer service is divided into four states: idle, saturated, saturated, and bursting. For other classification strategies, the customer service in the embodiment of the present invention The same applies to real-time scheduling methods. When a customer enters, a customer service is directly taken out from the head of the queue (idle side) for allocation. If the allocation conditions are not met, the pressure scheduling algorithm is used to update the pressure state of the customer service, so that the customer service after the updated pressure state is placed in the online queue. , and take out a new customer service from the head of the queue again for allocation, and repeat this cycle until the allocation is successful. When the customer service is allocated successfully, it is also necessary to update the pressure status of the customer service and put it into the appropriate position in the online queue.

From the above process, it can be seen that the real-time pressure scheduling mechanism is extremely critical. Determines the quality and efficiency of customer distribution. The real-time customer service scheduling method based on classification and sorting proposed in the following embodiments of the present invention is mainly divided into three key parts: pressure logical partition sorting, chief allocation sub-area sorting, and same-area concurrent sorting. The embodiments of the present application are described in detail below.

As shown in FIG. 2 , the real-time scheduling method for customer service provided by the embodiment of the present invention mainly includes:

Step 1. According to the pressure state of the customer service, divide the customer service queue into partitions;

Step 2. Determine the pressure ranking value of the customer service in the customer service queue according to the pressure value of the customer service, and insert the customer service into the customer service queue according to the pressure ranking value;

Step 3. Divide each partition in the customer service queue into a first sub-area and a second sub-area, put all the customer service personnel under the partition without customer assignment into the second sub-area, and put the Agents who are assigned to a customer and wish to continue to be assigned to a customer are transferred from the second sub-area to the first sub-area.

It should be noted that although the above steps are represented by serial numbers, the above steps are not necessarily divided into a sequence of execution.

In a specific embodiment, transferring the customer service agent in the second sub-area to the customer who wishes to continue to be assigned to the customer from the second sub-area to the first sub-area described in step 3 includes:

The pressure ranking value of the agent is adjusted using the time offset factor to transfer the agent from the second sub-area to the first sub-area.

In a specific embodiment, the pressure ranking value of the customer service in the second sub-area is:

score ₁ =N _a +[( _ci -c _a )/(c _b -c _a )]×(N _b -N _a )

Among them, score ₁ is the pressure ranking value of the customer service in the second sub-area, _{Na is the first boundary value of the partition to which the second sub-area and the first sub-area belong, and N b is} the second sub-area and the first sub-area. The second boundary value of the zone to which it belongs, c _a is the pressure value corresponding to _Na , c _b is the pressure value corresponding to N _b , and c _i is the pressure value of any customer service in the second sub-area, where N _b >N _a , c _b > _ci >c _a , c _b , c _a and c _i are all integers.

In a specific embodiment, the pressure ranking value of the customer service in the first sub-area is:

score ₂ =N _a +[offset/(c _b -c _a )]×(N _b -N _a )

Among them, score ₂ is the pressure ranking value of the customer service in the first sub-area, _{Na is the second sub-area and the first boundary value of the partition to which the first sub-area belongs, and N b is} the second sub-area and the first sub-area. The second boundary value of the partition to which it belongs, c _a is the pressure value corresponding to _Na , c _b is the pressure value corresponding to N _b , offset is the time offset factor, and the time offset factor is the timestamp when the customer service is assigned to the customer Ratio to the maximum value of a long integer, where N _b >N _a , c _b > _{ca , and c b and} c _a are integers.

In a specific embodiment, for multiple clients with the same pressure ranking value in the second sub-area, the time offset factor is used to fine-tune the pressure ranking values among the multiple Sort by customer service. Further, the fine-tuned pressure ranking values of multiple customer service agents with the same pressure ranking value in the second sub-area are:

score ₃ =N _a +[( _ci -c _a +offset)/(c _b -c _a )]×(N _b -N _a )

Among them, score ₃ is the fine-tuned pressure ranking value, _{Na is the first boundary value of the partition to which the second sub-area and the first sub-area belong, and N b is} the second boundary of the partition to which the second sub-area and the first sub-area belong. c a is the pressure value corresponding to _{Na, c b is the pressure value corresponding to N b , c i} is the pressure value of any customer service in the second sub-region, offset is the time offset factor, time offset The factor is the ratio of the timestamp when any customer service is inserted into the customer service queue to the maximum value of the long integer, where N _b >N _a , c _b > _ci >c _a , c _b , c _a and c _i are all is an integer.

In a specific embodiment, the partitions for the customer service queue include idle partitions, active partitions, saturated partitions, and burst partitions.

In the real-time scheduling method for customer service in the embodiment of the present invention, the evaluation and scheduling of customer service pressure are performed in real time. Specifically, the embodiment of the present invention is: when any customer service event arrives, the customer service pressure is evaluated to obtain the customer service pressure. value, and then determine the pressure ranking value of the customer service and insert the customer service into the customer service queue according to the pressure ranking value. Because of the real-time nature of customer service event triggering, the method according to the embodiment of the present invention also performs real-time evaluation and scheduling of customer service pressure.

The real-time scheduling method for customer service in the embodiment of the present invention is mainly embodied in three key parts: pressure logical partition sorting, chief allocation sub-area sorting, and same-area concurrent sorting.

(1) Sorting of pressure logical partitions

For the customer service in the same customer service queue, in order to allow the customer service under different pressure states to adopt different allocation strategies, the customer service queue needs to be divided into pressure value partitions. The technical solution of the present invention proposes a pressure value logical partition strategy. The steps are:

1) State partition

Partition the customer service state, including: idle partition [0, N ₁ ), active partition [N ₁ , N ₂ ), saturated partition [N ₂ , N ₃ ), burst partition [N ₃ ,-), where N ₁ <N ₂ <N ₃ . When any agent event arrives, the pressure order value placed in the agent queue must belong to one of these partitions. As shown in Figure 3.

2) Pressure value mapping. When any customer service event arrives, methods such as rules, AI algorithm classification, etc. are used to evaluate the stress of the customer service. The method for evaluating the stress of the customer service can be implemented by using methods already in the art, which will not be repeated here.

Here is an example of dividing the customer service pressure value by the number of customers the customer service receives. In this example, the rule states that the relationship between the number of customers and the pressure value is: idle [0,c ₁ ), active [c ₁ ,c ₂ ), saturated [c ₂ ,c ₃ ), burst [c ₃ ,-) , where c ₁ , c ₂ , and c ₃ all represent the number of customers, and c ₁ <c ₂ <c ₃ , c ₁ , c ₂ , and c ₃ are integers. Using c _i to represent the number of customers received by a customer service, when a customer service event is triggered, assuming that the number of customers received by the customer service is c ₂ < _ci <c ₃ , then according to the rules, it can be known that the customer service is in a saturated state. Therefore, the calculation formula of the pressure ranking value of the customer service in the saturated state is as follows:

score ₁ =N ₂ +[(ci-c ₂ )/(c ₃ -c ₂ )]×(N ₃ -N ₂ ).

The calculation method of the pressure sorting value in other states is the same as this, except that the interval of the partition adopts the value of the corresponding state interval, which will not be repeated here.

3) Insert the customer service into the customer service queue according to the size of the above pressure sorting value.

The design of the state partition of the customer service queue allows all customers to be in the corresponding state partition, but in fact these customers are still in the same sorting queue, but each customer service in the queue is clustered according to their respective pressure states.

(2) Sorting of the chief sub-area

If in a certain state partition, customer service is often expected to continuously allocate customers in order to achieve a better scheduling effect. Then, in the embodiment of the present invention, the same state partition is divided into a chief sub-area (that is, the first sub-area) and a common sub-area (that is, the second sub-area). When all the customer services in this state partition have not been assigned, all the customer service agents are in the common sub-area. Only after a customer service agent is assigned to a customer and wants to continue to assign customers continuously, the customer service agent will enter the first sub-area first. Area. In this embodiment of the present invention, between two partitions (the first partition and the second partition) split in the same state partition, the customer service in the first partition has a high priority allocation relative to the customer service in the second partition Therefore, in this embodiment of the present invention, the first sub-area may be referred to as the chief sub-area, and the second sub-area may be referred to as the ordinary sub-area, to indicate that the customer service in the chief sub-area has a greater High priority allocation.

For example, for a customer service in an idle partition who expects to allocate x (0<x<c1) customers in a row, the customer service will be placed on In the chief allocation sub-area, as shown in Figure 4.

However, in a real scenario, it takes a certain amount of time to extract a customer service from the customer service queue for allocation. In order to improve the efficiency of concurrent processing, at the same time, there are often cases where multiple customer service personnel enter the chief sub-area at the same time. Therefore, the so-called continuous allocation in the embodiment of the present invention is only a relative continuous allocation, not absolutely, such as customer service A is allocated to X customers continuously, the customer allocation of customer service B is performed, but multiple customer service can be allowed to enter at the same time. Chief subregion to improve concurrency performance.

In the embodiment of the present invention, the sorting rules in the chief sub-area are sorted based on time sequence FIFO (First Input First Output, first in first out). In order to ensure that the customer service in the chief sub-area is always ranked in front of the ordinary sub-area, the sorting is only based on the time factor. In the embodiment of the present invention, the ratio of the current timestamp to the maximum value of the Long type is taken as the time offset factor to determine the order of the customer service in the chief sub-area. The specific calculation formula is as follows:

offset=current_time/Long.MAX_VALUE

Among them, offset is the time offset factor, current_time is the current timestamp, and Long.MAX_VALUE is the maximum value of the Long type. As can be seen from the above formula, the offset is only a decimal between [0, 1), but its sensitivity is very high. Then, the sorting calculation formula of the chief sub-zone in the saturated state in the above-mentioned pressure logical partition sorting example is:

score ₂ =N ₂ +[offset/(c ₃ -c ₂ )]×(N ₃ -N ₂ )

Usually, the value of score ₂ is very close to N2, which can be guaranteed to be in the front of the common sub-area, so it has a high priority allocation. Of course, this is just an example ordering rule, and other ordering rules can be used as appropriate.

(3) Concurrent sorting in the same region

According to the pressure value mapping result in the above-mentioned pressure logical partition sorting, at the same time, the pressure values between at least two customers, for example, between customer service A and customer service B, may be the same. Therefore, it is necessary to solve the problem of the same pressure value. The problem of conflicting pressure sorting values between agents.

It is not difficult to see that this problem of conflicting pressure ranking values mainly occurs in the common sub-area, as shown in Figure 5. If a random method is used to deal with the conflict between customer service A and customer service B, it may lead to the same customer service being continuous in extreme cases. The embarrassing state of assigning a large number of customers, while other customer service has no customers to assign.

In the embodiment of the present invention, a time-sensitive solution is introduced, and a small time offset factor is added to the sorting value in the common partition, and the time offset factor is obtained by using the time offset factor in the above-mentioned sorting of the chief allocation sub-area. way of offset. Therefore, the above-mentioned calculation formula of the pressure ranking value of the customer service in the saturated state evolves into:

score ₃ =N ₂ +[( _ci -c ₂ +offset)/(c ₃ -c ₂ )]×(N ₃ -N ₂ )

The value of offset is very small, but the sensitivity to concurrency under the same conditions is high enough to solve the problem of conflicting sorting values. Therefore, the problem of concurrent scheduling in the same region can be cleverly solved.

Embodiments of the present invention also provide a non-volatile computer-readable storage medium, where the non-volatile computer-readable storage medium stores instructions, which, when executed by a processor, cause the processor to execute as described in the above description The various steps in the real-time scheduling method of customer service.

The embodiment of the present invention also provides an electronic device for executing the tracking scheduling method. As shown in FIG. 6 , the electronic device includes: at least one processor 1 and a memory 2 . The memory 2 and the at least one processor 1 are communicatively connected, for example, the memory 2 and the at least one processor 1 are connected through a bus. The memory 2 stores instructions executable by the at least one processor 1, and the instructions are executed by the at least one processor 1 to cause the at least one processor 1 to execute various steps in the real-time scheduling method for customer service as described above.

The real-time scheduling method for customer service, the non-volatile computer-readable storage medium, and the electronic device according to the embodiments of the present invention are based on the same online customer service queue, and adopt pressure partition sorting, first sub-area and second sub-area sorting, same-area concurrent sorting, etc. The two-level partition strategy has great advantages in improving the quality of customer service, service efficiency, and the consistency of the state of the dispatcher. At the same time, the present invention also provides the offset sorting value under the concurrency of the same area, thereby improving the ability to handle concurrent scheduling. In addition, there are no redundant assumptions and constraints in the present invention, so it has strong scalability and can adapt to a variety of scheduling scenarios. Applying the technical solution of the present invention to a distributed cache queue can also make its performance better. big boost.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the present invention. within the scope of protection.

Claims (10)

1. A customer service real-time scheduling method comprises the following steps:

partitioning the customer service queue according to the pressure state of the customer service;

determining a pressure sorting value of the customer service in the customer service queue according to the pressure value of the customer service, and inserting the customer service into the customer service queue according to the pressure sorting value;

dividing each subarea in the customer service queue into a first subarea and a second subarea, putting all the customer services which are not allocated to the customers under the subarea into the second subarea, and transferring the customer services which are allocated to the customers in the second subarea and hope to be continuously allocated to the customers from the second subarea to the first subarea.

2. The method of claim 1, wherein transferring customer service in the second sub-area that is assigned to customers and that wishes to continue to be assigned to customers from the second sub-area to the first sub-area comprises:

adjusting a pressure ranking value of the customer service with a time-shift factor to shift the customer compliance with the second sub-zone into the first sub-zone.

3. The customer service real-time scheduling method according to claim 1, wherein:

the pressure sequencing value of the customer service in the second subregion is:

score₁＝N_a+[(c_i-c_a)/(c_b-c_a)]×(N_b-N_a)

wherein, score₁A pressure ranking value, N, for customer service in the second subregion_aA first boundary value, N, for the second subregion and the subregion to which the first subregion belongs_bA second boundary value for the second subregion and the subregion to which the first subregion belongs, c_aTo correspond to N_aPressure value of c_bTo correspond to N_bPressure value of c_iA pressure value for any customer service in the second sub-zone, wherein N_b>N_a，c_b>c_i>c_a，c_b、c_aAnd c_iAre all integers.

4. The customer service real-time scheduling method according to claim 2, wherein:

the pressure sequencing values of customer service in the first sub-zone are:

score₂＝N_a+[offset/(c_b-c_a)]×(N_b-N_a)

wherein, score₂A pressure ranking value, N, for customer service in the first sub-zone_aA first boundary value, N, for the second subregion and the subregion to which the first subregion belongs_bA second boundary value for the second subregion and the subregion to which the first subregion belongs, c_aTo correspond to N_aPressure value of c_bTo correspond to N_bIs the time offset factor, which is the ratio of the timestamp when the customer service was distributed to the customer to the maximum value of the long integer, where N is_b>N_a，c_b>c_a，c_bAnd c_aAre integers.

5. The customer service real-time scheduling method according to claim 1, wherein:

and for a plurality of customer services with the same pressure sequencing values in the second sub-area, finely adjusting the pressure sequencing values among the customer services by using a time offset factor, and sequencing the customer services according to the finely adjusted pressure sequencing values.

6. The real-time customer service scheduling method according to claim 5, wherein:

the pressure ranking values after the customer service fine adjustment with the same pressure ranking value in the second sub-area are as follows:

score₃＝N_a+[(c_i-c_a+offset)/(c_b-c_a)]×(N_b-N_a)

wherein, score₃Sorting the values of the pressure after the fine adjustment，N_aA first boundary value, N, for the second subregion and the subregion to which the first subregion belongs_bA second boundary value for the second subregion and the subregion to which the first subregion belongs, c_aTo correspond to N_aPressure value of c_bTo correspond to N_bPressure value of c_iThe offset is the time offset factor which is the ratio of the timestamp of any customer service inserted into the customer service queue to the maximum value of the long integer, wherein N is the pressure value of any customer service in the second subarea_b>N_a，c_b>c_i>c_a，c_b、c_aAnd c_iAre all integers.

7. The customer service real-time scheduling method according to claim 1, wherein:

the partitions include idle partitions, active partitions, saturated partitions, and burst partitions.

8. The customer service real-time scheduling method according to claim 1, wherein:

when any customer service event comes, the customer service pressure is evaluated to obtain the pressure value, the pressure ranking value is further determined, and the customer service is inserted into the customer service queue according to the pressure ranking value.

9. A non-transitory computer readable storage medium storing instructions, wherein the instructions, when executed by a processor, cause the processor to perform the steps in the customer service real-time scheduling method of any one of claims 1 to 8.

10. An electronic device, comprising:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps in the real-time customer service dispatch method of any of claims 1-8.

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