CN113269476A - Scheduling method for calling multi-manufacturer AI (Artificial Intelligence) capability by intelligent cloud call center - Google Patents
Scheduling method for calling multi-manufacturer AI (Artificial Intelligence) capability by intelligent cloud call center Download PDFInfo
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Abstract
The invention discloses a scheduling method for calling multi-manufacturer AI (Artificial intelligence) capability by an intelligent cloud call center, which comprises the following steps: s1, extracting manufacturer data with certain conditions, and labeling the manufacturer process; s2, initiating an intelligent outbound call, acquiring the maximum total score of the intelligent call flow according to the object data label and the algorithm model, and initiating the call according to the intelligent call service flow; s3, calculating the service quality of the process in reverse direction according to the historical service data, and marking the data; s4, realizing intelligent calling multi-vendor multi-resource scheduling according to the step S2 and the step S3, and selecting the best vendor route. According to the intelligent outbound call multi-manufacturer resource scheduling method, the flexibility of intelligent outbound call multi-manufacturer resource scheduling is improved through the iterative algorithm, the problem of resource waste bottleneck caused by the fixed parameter algorithm is avoided, meanwhile, the efficiency of intelligent call multi-manufacturer resource scheduling is improved, the AI multi-manufacturer income is reasonably distributed, the service quality is improved, the intelligent call deployment risk is reduced, and the overall benefit of intelligent voice in the outbound call field is ensured.
Description
Technical Field
The invention relates to the technical field of intelligent cloud call centers, in particular to a scheduling method for scheduling AI (Artificial intelligence) capabilities of multiple manufacturers for an intelligent cloud call center.
Background
With the development and maturity of artificial intelligence and voice interaction technology, AI intelligent voice gradually moves into the life of consumers. Call centers, also known as customer service centers, originated in the 30's of the 20 th century and are service facilities consisting of a group of service personnel in a relatively centralized location. Call centers contain two broad categories of outgoing and incoming services. The calling type service is becoming more and more market mainstream service, actively calls the customer to carry out telemarketing and market research, and becomes one of more popular competitive means in the market. The AI robot exhales outwards, so that the popularization efficiency is improved, the exhaling popularization cost is saved, and enterprises tend to use the robot to exhale outwards more and more aiming at services with poor maneuverability.
In 2020, the total number of mobile phone users in China reaches 15.9 hundred million households and keeps on rising trend, so more and more enterprises and merchants begin to pay attention to telemarketing and to the business opportunity and necessity in outbound marketing. The call center outbound marketing has numerous advantages, and can accurately analyze customer requirements, improve the transaction rate, improve the brand image and the like.
At present, a relatively common method for a large number of projects of AI robot outbound service is to specify a robot model outbound: and a specific robot outbound model is created for matching the service default manual outbound to the user, and the model adopts a question-answering mode, which has the defects of single model and hard user experience. When multiple service scene models are adopted, the requirement on supporting concurrency of AI service engines (ASR/TTS/NLP) is high, and the single-point failure rate is also high.
In addition, a scheme of a multi-AI manufacturer integrated call center system is provided in the prior art, and the single-point failure rate is reduced while the AI service concurrency is improved. Due to the fact that the concurrency of services of every AI manufacturer is different from the maturity of industry fields which are good for all AI manufacturers, the telephone traffic marketing conversion rate is different. Therefore, when the optimal outbound resource is obtained by actually adopting a resource scheduling algorithm, how to ensure the intelligent outbound quality and efficiency while ensuring the rationalization of resource utilization of multiple AI service manufacturers (maximizing the benefits obtained by AI manufacturers) is the problem to be solved by the technical scheme.
For the AI intelligent outbound system, if the route of the dynamically acquired robot can be rapidly and accurately calculated, the concurrency utilization rate of each AI service manufacturer is improved, the obtained benefit of each AI manufacturer can be ensured, and the global benefit in the telecommunication industry can not be greatly influenced by one AI manufacturer.
Disclosure of Invention
The invention aims to solve the problem that the service quality cannot be improved due to uneven resource scheduling among intelligent outbound multiple service manufacturers of a call center in the prior art, and provides a scheduling method for calling AI (artificial intelligence) capabilities of multiple manufacturers based on a dynamic statistical process.
In order to achieve the purpose, the invention adopts the technical scheme that: a scheduling method for calling multi-manufacturer AI (Artificial Intelligence) capability by an intelligent cloud call center comprises the following steps:
s1: extracting manufacturer data under certain conditions, and labeling a manufacturer process;
s2: initiating an intelligent outbound call, acquiring the maximum total score of an intelligent call flow according to the object data mark and the algorithm model, and initiating a call according to an intelligent call service flow;
s3: reversely calculating the service quality of the process aiming at the historical service data, and marking the data;
s4: and realizing intelligent calling multi-resource scheduling among multiple manufacturers according to the step S2 and the step S3, and selecting the optimal manufacturer route.
Further, in step S1, the data attribute of the extracted data includes: the method comprises the following steps of intelligent calling process ID, scene belonging, field belonging, manufacturer name, manufacturer process identification, preset concurrency, residual concurrency, calling times, manufacturer profitability, manufacturer field score and service quality score.
Further, in step S2, a total score is calculated according to the following formula mode, and the total score is used to select the priority:
wherein Q represents the priority of the intelligent process at the time t, the larger the value is, the more the calling selection is, f (P) represents the income of the manufacturer in unit time at the time t, f (U) represents the intelligent calling concurrence utilization rate at the time t, M represents the service quality at the time t, S represents the field score at the time t,
wherein:
in the formula P, N represents the number of unit data sets, and mu represents the yield of manufacturers; in the formula U, E represents the unit time concurrency amount, and E represents the system uniform total concurrency.
Further, in step S2, the call is ended, the vendor service quality is marked, and the call record attribute includes a call ID, an AI process ID, a service completion status, and a call time.
Further, in step S3, the flow service quality is calculated by the following formula:
Further, in step S3, verifying the resource scheduling outbound algorithm data flag by the following formula, and adjusting the flag feature value;
wherein, P represents the income amount of the manufacturer in unit time, and U represents the concurrency utilization rate at a certain moment.
The invention has the beneficial effects that: the invention has the advantages of high flexibility, strong processing capability, high cost performance, high stability and the like; the method improves the flexibility of intelligent outbound multi-manufacturer resource scheduling, avoids the bottleneck problem of resource waste caused by a fixed parameter algorithm, improves the efficiency of intelligent calling multi-manufacturer resource scheduling, realizes reasonable distribution of AI multi-manufacturer income, improves the service quality, reduces the intelligent calling deployment risk, and ensures the global benefit of intelligent voice in the outbound field.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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FIG. 1 is a flow algorithm apparatus diagram of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
The invention provides a scheduling method for calling multi-manufacturer AI (Artificial intelligence) capability by an intelligent cloud call center, which comprises the following steps:
s1: and extracting manufacturer data under certain conditions, and labeling the manufacturer process.
S1.1: the data attributes include: the method comprises the following steps of intelligent calling process ID, a scene, a field, a manufacturer name, a manufacturer process identifier, preset concurrency, residual concurrency, calling times, manufacturer profitability, manufacturer field score (5-10) and service quality score (0-100).
The data of this example is as follows: y1{1, scene 1, Domain 1, vendor A, A001, 200, 50, 500, 0.01, 9,2} Y2{2, scene 1, Domain 1, vendor B, B001, 200, 30, 600, 0.02, 9,0.83}
S2: initiating an intelligent outbound call, acquiring the maximum total score of an intelligent call flow according to the object data mark and the algorithm model, and initiating a call according to an intelligent call service flow;
q represents the priority of the intelligent process, and the larger the value is, the more the calling selection is; respectively calculating to obtain:
Q1＝0.01*500/60+150/(200+200)+10/500*100+9＝11.121；
Q2＝0.02*600/60+180/(200+200)+5/600*100+9＝10.483；
s2.1: after the call is finished, marking the service quality of a manufacturer, wherein the call record attribute comprises a call ID, an AI process ID, a service achievement state and call time; x (i) ═ x1,x2,...xi,...,xN)。
S3: and calculating the service quality of the process in reverse direction according to the historical service data, and marking the data.
M1＝10/500*100＝2,M2＝5/600*100＝0.833
S3.1: verifying the data mark of the resource scheduling outbound algorithm and adjusting the mark characteristic value.
P1＝0.01*500/60＝0.083,P2＝0.02*600/60＝0.2。
U1＝150/(200+200)＝0.38,U2＝180/(200+200)＝0.45。
s4: according to the S2-3, multi-resource scheduling among multiple intelligent calling manufacturers is realized, and the optimal manufacturer route is selected, so that the concurrent use maximization of the manufacturers and the maximization of the out-call commercial marketing benefit are achieved.
In conclusion, by adopting the algorithm to perform test operation on the intelligent outbound history of the history item, the average resource scheduling occupation ratio of each AI manufacturer is greatly improved compared with the original average resource scheduling occupation ratio, the degree of contact between the call ratio of each scene flow and the market score among multiple operators must reach 95%, and the service quality is improved by 1.5%. Through practice, the algorithm quantifies indexes of the scheduling data of the historical manufacturers, service yield of each AI manufacturer is balanced, and total concurrency efficiency of the call center is improved.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It should be understood by those skilled in the art that the above embodiments do not limit the scope of the present invention in any way, and all technical solutions obtained by using equivalent substitution methods fall within the scope of the present invention.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (6)
1. A scheduling method for calling multi-manufacturer AI capability by an intelligent cloud call center is characterized by comprising the following steps:
s1: extracting manufacturer data under certain conditions, and labeling a manufacturer process;
s2: initiating an intelligent outbound call, acquiring the maximum total score of an intelligent call flow according to the object data mark and the algorithm model, and initiating a call according to an intelligent call service flow;
s3: reversely calculating the service quality of the process aiming at the historical service data, and marking the data;
s4: and realizing intelligent calling multi-resource scheduling among multiple manufacturers according to the step S2 and the step S3, and selecting the optimal manufacturer route.
2. The scheduling method for invoking the multi-vendor AI capability by the intelligent cloud call center according to claim 1, wherein in step S1, the extracting data attributes comprises: the method comprises the following steps of intelligent calling process ID, scene belonging, field belonging, manufacturer name, manufacturer process identification, preset concurrency, residual concurrency, calling times, manufacturer profitability, manufacturer field score and service quality score.
3. The scheduling method for calling the multi-vendor AI capability of the intelligent cloud call center according to claim 1, wherein in the step S2, the total score is calculated according to the following formula mode, and the total score value is used for selecting the priority:
wherein Q represents the priority of the intelligent process at the time t, the larger the value is, the more the calling selection is, f (P) represents the income of the manufacturer in unit time at the time t, f (U) represents the intelligent calling concurrence utilization rate at the time t, M represents the service quality at the time t, S represents the field score at the time t,
wherein:
in the formula P, N represents the number of unit data sets, and mu represents the yield of manufacturers; in the formula U, E represents the unit time concurrency amount, and E represents the system uniform total concurrency.
4. The scheduling method for calling multi-vendor AI capability by an intelligent cloud call center according to claim 1, wherein in step S2, the call is ended, vendor service quality is marked, and the call record attribute includes call ID, AI process ID, service achievement status, and call time.
5. The scheduling method for calling the multi-vendor AI capability by the intelligent cloud call center according to claim 1, wherein in the step S3, the flow service quality is calculated according to the following formula:
6. The scheduling method for calling the multi-vendor AI capability by the intelligent cloud call center according to claim 5, wherein in step S3, the resource scheduling outbound algorithm data flag is verified through the following formula, and the flag feature value is adjusted;
wherein, P represents the income amount of the manufacturer in unit time, and U represents the concurrency utilization rate at a certain moment.
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| US20030232623A1 (en) * | 2002-06-12 | 2003-12-18 | Motorola, Inc. | Call center access in a dispatch based call system |
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