CN116915910A

CN116915910A - Quality inspection data processing method, device, equipment and storage medium

Info

Publication number: CN116915910A
Application number: CN202310886670.7A
Authority: CN
Inventors: 秦蓁
Original assignee: China United Network Communications Group Co Ltd; China Information Technology Designing and Consulting Institute Co Ltd
Current assignee: China United Network Communications Group Co Ltd; China Information Technology Designing and Consulting Institute Co Ltd
Priority date: 2023-07-18
Filing date: 2023-07-18
Publication date: 2023-10-20

Abstract

The application provides a quality inspection data processing method, a quality inspection data processing device, quality inspection data processing equipment and a storage medium, wherein the quality inspection data processing method is used for obtaining user rank scores; determining a first user spot check proportion list according to the user rank scores and a preset proportion rule, wherein the user spot check proportion list comprises user identifications and spot check proportions of call data corresponding to users; acquiring call data of a user, and determining the total amount of spot checks according to the call data and a first user spot check proportion list; according to the total amount of the spot checks and a preset resource threshold, adjusting the spot check proportion list of the user to obtain a second spot check proportion list of the user; according to the second user sampling proportion list, quality inspection is carried out on call data, flexibility of a quality inspection data processing method is improved, and quality inspection efficiency is improved.

Description

Quality inspection data processing method, device, equipment and storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a quality inspection data processing method, apparatus, device, and storage medium.

Background

The call center quality inspection is to acquire the record of the telephone operator, inspect and evaluate the service quality of the telephone operator according to the record of the telephone operator, and the purpose is to ensure the whole service level of the telephone operator and improve the satisfaction degree of the user. Quality inspection is therefore an important link in call centers.

In the traditional mode, when the quality inspection of the call center is carried out, the quality inspection personnel generally carry out manual sampling inspection on the record of the quality inspection required, or carry out the sampling inspection on the record of the call center in a random mode through artificial intelligence.

However, the quality inspection data processing method in the prior art has poor flexibility, cannot dynamically and reasonably utilize quality inspection system resources, and has low quality inspection efficiency.

Disclosure of Invention

The application provides a quality inspection data processing method, a quality inspection data processing device, quality inspection data processing equipment and a storage medium, which are used for solving the technical problems that the quality inspection data processing method in the prior art is poor in flexibility, cannot dynamically and reasonably utilize quality inspection system resources and is low in quality inspection efficiency.

In a first aspect, the present application provides a quality inspection data processing method, including:

obtaining a user rank score;

determining a first user spot check proportion list according to the user rank score and a preset proportion rule, wherein the user spot check proportion list comprises a user identifier and spot check proportion of call data corresponding to the user;

acquiring call data of a user, and determining the total amount of spot checks according to the call data and the first user spot check proportion list;

according to the total amount of the spot checks and a preset resource threshold, the user spot check proportion list is adjusted to obtain a second user spot check proportion list;

And performing quality inspection on the call data according to the second user spot check proportion list.

The application provides a quality inspection data processing method, which can divide quality inspection grades of users according to user rank scores, determine voice quality inspection sampling proportion through classification grading, effectively combine information such as risk registration of the users, reasonably and efficiently utilize quality inspection resources, dynamically and reasonably utilize quality inspection system resources, flexibly adjust the user quality inspection proportion, fully inspect high-risk users, and perform quality inspection on low-risk users in proper quantity, and simultaneously combine a preset resource threshold value to adjust the sampling proportion, thereby ensuring that quality inspection cannot cause aging deterioration due to resource problems, improving flexibility of the quality inspection data processing method and improving quality inspection efficiency.

Optionally, the determining the first user spot check scale list according to the user rank score and the preset scale rule includes:

if the user rank score is higher than a first preset risk threshold, determining that the sampling rate of call data corresponding to the user is a first sampling rate; if the user rank score is not higher than the first preset risk threshold value and is higher than the second preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a second sampling rate; if the user rank score is not higher than the second preset risk threshold value and is higher than the third preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a third sampling rate; if the user rank score is not higher than a third preset risk threshold, determining that the sampling rate of the call data corresponding to the user is a fourth sampling rate; the first sampling rate is larger than the second sampling rate, and the second sampling rate is larger than the third sampling rate.

According to the application, the sampling rate of call data is determined according to the user rank score, the higher the rank is, the higher the risk is, the sampling rate is higher, the high-risk user is fully inspected, the low-risk user is inspected with a proper amount of quality, the flexible sampling is realized, the effectiveness of the quality inspection is ensured, and the safety risk is reduced.

Optionally, the step of adjusting the user spot check proportion list according to the spot check total amount and a preset resource threshold to obtain a second user spot check proportion list includes:

comparing the total amount of the spot check with the preset resource threshold; and if the total amount of the spot checks is larger than the preset resource threshold, performing equal proportion down-regulation on the second spot check proportion, the third spot check proportion and the fourth spot check proportion according to a preset rule to obtain a second user spot check proportion list.

The application can further adjust the user sampling proportion list by combining with the resource threshold value, thereby ensuring the timeliness and the efficiency of quality inspection data processing.

Optionally, after the quality inspection is performed on the call data according to the second user spot check proportion list, the method further includes;

detecting the residual quantity of resources; and if the resource remaining amount is smaller than a preset resource remaining amount threshold value, triggering resource early warning.

The application can monitor the residual quantity of the resources in real time and perform resource early warning according to the current resource condition so as to ensure the smooth performance of quality inspection, improve the reliability of quality inspection data processing and further improve the call quality of a call center.

Optionally, after triggering the resource early warning if the resource remaining amount is smaller than a preset resource remaining amount threshold, the method further includes:

and if the number of the resource early warning times is greater than a preset early warning threshold value in a preset time period, triggering the capacity expansion reminding.

The application can also send out capacity expansion reminding by combining with the resource use condition to prompt capacity expansion, thereby meeting the quality inspection requirement and improving the quality inspection reliability and the quality inspection efficiency.

Optionally, after determining the first user spot check scale list according to the user rank score and the preset scale rule, the method further includes:

according to a preset special user list, carrying out proportion adjustment on the first user spot check proportion list to obtain a third user spot check proportion list; correspondingly, the acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the first user spot check proportion list comprises the following steps: and acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the spot check proportion list of the third user.

Here, the application also combines the preset special user list to adjust the user sampling proportion list, considers the special user and other conditions, further improves the flexibility of quality inspection data processing and improves the quality inspection reliability.

Optionally, performing quality inspection on the call data according to the second user spot check proportion list includes: according to the second user sampling rate list, determining sampling total amount of target call data of users corresponding to different sampling rates in the second user sampling rate list; and periodically acquiring the target call data for quality inspection according to the sequence of the sampling inspection proportion corresponding to the target call data.

Optionally, the periodically obtaining the target call data for quality inspection according to the order of the sampling rate corresponding to the target call data includes:

determining a night quality inspection queue and a daytime quality inspection queue according to the preset quality inspection time length and the quality inspection time length of the target call data; and according to the order of the sampling rate corresponding to the target call data, acquiring the target call data of the daytime quality inspection queue for quality inspection in a preset daytime quality inspection period, and acquiring the target call data of the nighttime quality inspection queue for quality inspection in a preset nighttime period.

Here, the application can adjust the sampling test time and the sampling test proportion by combining the resource use condition, thereby ensuring the comprehensiveness and the reliability of the sampling test and improving the quality testing flexibility.

In a second aspect, the present application provides a quality control data processing apparatus comprising:

the acquisition module is used for acquiring the user rank scores;

the first determining module is used for determining a first user sampling rate list according to the user rank scores and a preset rate rule, wherein the user sampling rate list comprises a user identifier and sampling rate of call data corresponding to the user;

the second determining module is used for acquiring call data of the user and determining total amount of spot check according to the call data and the first user spot check proportion list;

the third determining module is used for adjusting the user spot check proportion list according to the spot check total amount and a preset resource threshold value to obtain a second user spot check proportion list;

and the quality inspection module is used for inspecting the quality of the call data according to the second user selective inspection proportion list.

Optionally, the first determining module is specifically configured to:

if the user rank score is higher than a first preset risk threshold, determining that the sampling rate of call data corresponding to the user is a first sampling rate; if the user rank score is not higher than the first preset risk threshold value and is higher than the second preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a second sampling rate; if the user rank score is not higher than the second preset risk threshold value and is higher than the third preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a third sampling rate; if the user rank score is not higher than a third preset risk threshold, determining that the sampling rate of the call data corresponding to the user is a fourth sampling rate;

The first sampling rate is larger than the second sampling rate, and the second sampling rate is larger than the third sampling rate.

Optionally, the third determining module is specifically configured to:

Optionally, after the quality inspection module performs quality inspection on the call data according to the second user sampling inspection proportion list, the device further includes a first trigger module, configured to;

Optionally, after the early warning module is configured to trigger the resource early warning if the resource remaining amount is smaller than a preset resource remaining amount threshold, the apparatus further includes a second triggering module configured to:

Optionally, after the first determining module determines the first user spot check scale list according to the user rank score and the preset scale rule, the apparatus further includes a fourth determining module, configured to:

According to a preset special user list, carrying out proportion adjustment on the first user spot check proportion list to obtain a third user spot check proportion list;

correspondingly, the second determining module is specifically configured to:

and acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the spot check proportion list of the third user.

Optionally, the quality inspection module is specifically configured to:

according to the second user sampling rate list, determining sampling total amount of target call data of users corresponding to different sampling rates in the second user sampling rate list; and periodically acquiring the target call data for quality inspection according to the sequence of the sampling inspection proportion corresponding to the target call data.

Optionally, the quality inspection module is further specifically configured to:

In a third aspect, the present application provides a quality control data processing apparatus comprising: at least one processor and memory;

The memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored in the memory, causing the at least one processor to perform the quality control data processing method as described above in the first aspect and the various possible designs of the first aspect.

In a fourth aspect, the present application provides a computer readable storage medium having stored therein computer executable instructions which, when executed by a processor, implement the quality inspection data processing method according to the first aspect and the various possible designs of the first aspect.

In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the quality control data processing method of the first aspect and the various possible designs of the first aspect.

According to the quality inspection data processing method, the device, the equipment and the storage medium, the quality inspection grades of the users can be classified according to the grade scores of the users, the voice quality inspection sampling proportion is determined through classification and grading, information such as risk registration of the users can be effectively combined, quality inspection resources can be reasonably and efficiently utilized, quality inspection system resources are dynamically and reasonably utilized, the quality inspection proportion of the users is flexibly adjusted, the high-risk users are fully inspected, the low-risk users are inspected in proper quantity, meanwhile, the sampling proportion is adjusted by combining with a preset resource threshold value, the condition that the quality inspection cannot be deteriorated due to the fact that the resource problem causes time-dependent quality inspection is guaranteed, the flexibility of the quality inspection data processing method is improved, and the quality inspection efficiency is improved.

Drawings

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.

FIG. 1 is a schematic diagram of a quality inspection data processing system according to an embodiment of the present application;

fig. 2 is a schematic diagram of a call center quality inspection system according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of a quality inspection data processing method according to an embodiment of the present application;

FIG. 4 is a flowchart illustrating another quality inspection data processing method according to an embodiment of the present application;

FIG. 5 is a flow chart of a quality inspection queue rule and a processing method according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a quality inspection data processing device according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a quality inspection data processing device according to an embodiment of the present application.

Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

The terms "first," "second," "third," and "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or fully authorized by each party, and the collection, use and processing of the related data need to comply with related laws and regulations and standards, and provide corresponding operation entries for the user to select authorization or rejection.

The voice quality inspection is to identify the voice of the user into characters and analyze the characters to improve the service quality of the whole company.

The existing quality inspection mode mainly comprises the following steps:

artificial intelligence speech quality inspection: firstly, the artificial voice robot can convert voice into characters through a voice recognition system in the communication process of customer service representatives and users, and 100% quality inspection coverage can be realized. Powerful voice robot can realize the recognition of slang and small languages.

Traditional post-recording quality inspection: the quality inspection mode is a quality inspection mode used by most of the current call centers, and is mainly performed by a mode that a later quality inspector listens to a customer service representative record on line, registers a record result in a form and performs data analysis on the form.

And (5) on-screen voice quality inspection: the on-screen voice quality inspection refers to that a quality inspector can perform real-time quality inspection on a telephone operator through a system, can see a direct operation interface of the telephone operator through system management, and directly records a quality inspection structure with the system for data analysis.

With the rise of artificial intelligence (Artificial Intelligence, AI) technology at present, intelligent and automatic quality inspection is realized by means of voice recognition and natural language processing (Natural Language Processing, NLP) technology, voice is generally converted into text, and then complete automation of the manual quality inspection items of companies is realized through keyword or regular matching, event classification, emotion analysis, problem discovery and the like.

Intelligent voice quality inspection function: based on a voice analysis technology, a text index is quickly established, unstructured voice files are converted into to-be-detected data in a text format, and the to-be-detected data is subjected to quality detection rules preset by quality detection personnel in a quality detection engine to generate quality detection results and statistical data. General functions such as: the double support of voice and text, such as telephone recording of telephone customer service agents and text recording of online customer service agents; powerful algorithm and rule configuration, wherein the rules cover the core of intelligent analysis and support the functions of context logic detection, speech speed detection, semantic matching and the like; emotion recognition, namely performing non-contact intelligent analysis on potential emotion of a person in telephone recording by utilizing the principle of human biology, and performing induction, statistics and comparison analysis on the sampled data by utilizing a related algorithm to obtain a corresponding emotion index. Problems that can be solved: service specification setting: setting a service specification based on the intelligent rule; quality of service analysis: accurately capturing existing service non-compliance or errors; the whole amount is timely: no manual spot check, 100% coverage, timely analysis and timely reporting.

In the traditional mode, when the quality inspection of the call center is carried out, the quality inspection personnel generally carry out manual sampling inspection on the record of the quality inspection required, or carry out the sampling inspection on the record of the call center in a random mode through artificial intelligence. However, the quality inspection data processing method in the prior art has poor flexibility, cannot dynamically and reasonably utilize quality inspection system resources, and has low quality inspection efficiency.

In order to solve the technical problems, the embodiment of the application provides a quality inspection data processing method, a device, equipment and a storage medium, wherein the quality inspection data processing method can divide quality inspection grades of users according to user grading scores, determine voice quality inspection sampling inspection proportions through classification and grading, can effectively combine information such as risk registration of the users, and meanwhile, adjust sampling inspection proportions by combining preset resource thresholds, so that flexibility of the quality inspection data processing method is improved, and quality inspection efficiency is improved.

Optionally, fig. 1 is a schematic diagram of a quality inspection data processing system according to an embodiment of the present application. In fig. 1, the above architecture includes at least one of a data acquisition device 101, a processing device 102, and a display device 103.

It will be appreciated that the architecture illustrated by embodiments of the present application is not intended to constitute a particular limitation on the architecture of the texture data processing system. In other possible embodiments of the present application, the architecture may include more or less components than those illustrated, or some components may be combined, some components may be split, or different component arrangements may be specifically determined according to the actual application scenario, and the present application is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.

In a specific implementation, the data acquisition device 101 may include an input/output interface, or may include a communication interface, where the data acquisition device 101 may be connected to the processing device through the input/output interface or the communication interface.

The processing device 102 may rank the users according to their rank scores, determine the voice quality inspection sampling rate by classifying and grading, and effectively combine information such as risk registration of the users, and adjust the sampling rate by combining with a preset resource threshold.

The display device 103 may also be a touch display screen or a screen of a terminal device for receiving a user instruction while displaying the above content to enable interaction with a user.

It will be appreciated that the processing device described above may be implemented by a processor reading instructions in a memory and executing the instructions, or by a chip circuit.

Optionally, fig. 2 is a schematic diagram of a call center quality inspection system architecture provided in an embodiment of the present application, as shown in fig. 2, where the main functions of the system are user grading obtained by a user security grading system, and a quality inspection grading device grades users according to the user grading by a quality inspection grading principle to obtain quality inspection proportions, and sends the quality inspection proportions to a quality inspection system to complete user quality inspection grading. The quality inspection grade classification device comprises a management module, a storage module, a processing module and a communication module, and finishes quality inspection grade classification together.

The module functions of the quality inspection grading device are as follows:

and a management module: configuration management, parameter configuration, etc. for quality control grading devices.

And a storage module: the system is used for storing the user rating information acquired from the security rating system and other parameter information related to the quality inspection grading calculation mode.

The processing module is used for: the quality inspection grade classification method is used for calculating quality inspection grade classification results and obtaining final classification results according to the dynamic resource occupation condition.

And a communication module: the quality control grading device is used for completing communication between the quality control grading device and the security grading system and the quality control system and supporting a required communication protocol.

In addition, the network architecture and the service scenario described in the embodiments of the present application are for more clearly describing the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided by the embodiments of the present application, and as a person of ordinary skill in the art can know, with evolution of the network architecture and occurrence of a new service scenario, the technical solution provided by the embodiments of the present application is also applicable to similar technical problems.

The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.

Fig. 3 is a flow chart of a quality inspection data processing method according to an embodiment of the present application, where the embodiment of the present application may be applied to the processing device 102 in fig. 1, and a specific execution body may be determined according to an actual application scenario. As shown in fig. 3, the method comprises the steps of:

s301: a user rank score is obtained.

Optionally, the quality inspection ranking means obtains a user rank score from the security ranking system.

Alternatively, the user rank score may be automatically generated by AI based on historical data and rules, or may be manually entered.

S302: and determining a first user spot check proportion list according to the user rank scores and the preset proportion rule.

The user spot check proportion list comprises user identifications and spot check proportions of call data corresponding to the users.

Optionally, determining the first user spot check scale list according to the user rank score and the preset scale rule includes:

It will be appreciated that, the preset risk threshold and the sampling rate may be determined according to practical situations, which is not particularly limited in the embodiment of the present application.

In the embodiment of the application, the sampling rate of the call data is determined according to the user rank score, and the higher the rank is, the higher the risk is, the sampling rate is, the full quality inspection is carried out on high-risk users, and the proper quality inspection is carried out on low-risk users, so that the flexible sampling is realized, the effectiveness of the quality inspection is ensured, and the safety risk is reduced.

In one possible implementation, according to the user rank score and the preset proportion rule, the specific manner of determining the first user spot check proportion list is as follows:

the quality inspection grading device determines the corresponding sampling rate of the user according to the score according to the sampling rate corresponding to the score section according to the score of the user to form a first user sampling rate list, for example, the higher the score is, the higher the risk is, the more than 80 minutes, the corresponding sampling rate is 100%, the 70 (without) -80 (with) points, the corresponding sampling rate is 20%, the 40 (without) -70 (with) points, the corresponding sampling rate is 10%, and the 40 points and below correspond to the sampling rate of 5%.

According to a preset special user list, performing proportion adjustment on the first user spot check proportion list to obtain a third user spot check proportion list; correspondingly, acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the first user spot check proportion list, wherein the method comprises the following steps: and acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the spot check proportion list of the third user.

In one possible manner, the third user spot check ratio list is determined as follows:

comparing the first user sampling rate list with a special user list stored in the grading device, taking the sampling rate in the special user list as a priority rate set value (for example, 1%, and reducing the possible sampling rate of some users from 5%, 10% and the like to 1%), updating the first user sampling rate list to form a third user sampling rate list, for example, taking the users in the special user list as high-quality users, comprehensively obtaining the user list in view of dimensions including enterprise scale, long-term quality inspection results, complaint verification reduction conditions and the like.

Here, the embodiment of the application also combines the preset special user list to adjust the user sampling proportion list, considers the special user and other conditions, further improves the flexibility of quality inspection data processing and improves the quality inspection reliability.

S303: and acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the first user spot check proportion list.

S304: and adjusting the user spot check proportion list according to the spot check total amount and the preset resource threshold value to obtain a second user spot check proportion list.

Optionally, adjusting the user spot check proportion list according to the spot check total amount and a preset resource threshold to obtain a second user spot check proportion list, including: comparing the total amount of the spot check with a preset resource threshold; and if the total amount of the spot checks is greater than a preset resource threshold, performing equal proportion down-regulation on the second spot check proportion, the third spot check proportion and the fourth spot check proportion according to a preset rule to obtain a second user spot check proportion list.

The embodiment of the application can further adjust the user sampling proportion list by combining the resource threshold value, thereby ensuring the timeliness and the efficiency of quality inspection data processing.

In one possible manner, the second user spot check ratio list is determined as follows:

and calculating the total quantity of the quality inspection sampling inspection according to the sampling inspection proportion in the third user sampling inspection proportion list and the real-time conversation condition of the user acquired from the security rating system, and comparing the total quantity of the quality inspection sampling inspection with the condition of the quality inspection resource occupation scheduled preset in the quality inspection rating system.

According to the predicted quality inspection resource setting, the user quality inspection proportion is adjusted, if the total quality inspection sampling amount exceeds or is lower than the threshold value adjusted by the resource setting, the user sampling amount is adjusted down or up in proportion to obtain a second user sampling proportion list of the user sampling proportion, for example, the required quality inspection resource obtained according to the sampling proportion of a third user sampling proportion list is higher than the existing resource by 5%, according to rules, the sampling proportion is adjusted down by 20%, 10% and 5% on the premise of ensuring the quality inspection of the total high risk user (100%) and the minimum quality inspection amount of the user by 1%, and finally the resource requirement is met (a certain proportion of resource standby can be considered).

S305: and carrying out quality inspection on the call data according to the second user spot check proportion list.

The quality inspection data processing method provided by the embodiment of the application can divide the quality inspection grades of the users according to the grade scores of the users, and can effectively combine information such as risk registration of the users through classification grading to determine the voice quality inspection sampling proportion, so that quality inspection resources can be reasonably and efficiently utilized, quality inspection system resources are dynamically and reasonably utilized, the quality inspection proportion of the users is flexibly adjusted, the high-risk users are fully inspected, the low-risk users are inspected with proper quality, and meanwhile, the sampling proportion is adjusted by combining with a preset resource threshold value, so that the quality inspection is ensured not to be deteriorated due to timeliness caused by resource problems, the flexibility of the quality inspection data processing method is improved, and the quality inspection efficiency is improved.

Optionally, the embodiment of the present application may further trigger the sampling rate adjustment according to the real-time resource usage, and accordingly, fig. 4 is a schematic flow chart of another quality inspection data processing method provided by the embodiment of the present application, as shown in fig. 4, where the method includes:

s401: a user rank score is obtained.

S402: and determining a first user spot check proportion list according to the user rank scores and the preset proportion rule.

S403: and acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the first user spot check proportion list.

S404: and adjusting the user spot check proportion list according to the spot check total amount and the preset resource threshold value to obtain a second user spot check proportion list.

The implementation of steps S401 to S404 is similar to that of steps S301 to S304, and the embodiments of the present application are not described herein.

S405: and carrying out quality inspection on the call data according to the second user spot check proportion list.

Optionally, according to the second user sampling rate list, performing quality inspection on call data, including: according to the second user spot check proportion list, determining spot check total amounts of target call data of users corresponding to different spot check proportions in the second user spot check proportion list; and periodically acquiring the target call data for quality inspection according to the sequence of the sampling inspection proportion corresponding to the target call data.

Optionally, according to the order of the sampling rate corresponding to the target call data, periodically acquiring the target call data for quality inspection, including:

determining a night quality inspection queue and a daytime quality inspection queue according to the preset quality inspection time length and the quality inspection time length of the target call data; according to the sequence of the sampling inspection proportion corresponding to the target call data, acquiring the target call data of the daytime quality inspection queue for quality inspection in a preset daytime quality inspection period, and acquiring the target call data of the nighttime quality inspection queue for quality inspection in a preset nighttime period.

Here, the embodiment of the application can adjust the sampling test time and the sampling test proportion by combining the use condition of resources, thereby ensuring the comprehensiveness and the reliability of the sampling test and improving the flexibility of quality test.

S406: detecting the residual quantity of resources; and if the resource remaining amount is smaller than the preset resource remaining amount threshold value, triggering resource early warning.

The embodiment of the application can monitor the resource residual quantity in real time and perform resource early warning according to the current resource condition so as to ensure the smooth performance of quality inspection, improve the reliability of quality inspection data processing and further improve the call quality of a call center.

Optionally, after triggering the resource early warning if the resource remaining amount is smaller than the preset resource remaining amount threshold, the method further includes:

The preset time period may be determined according to practical situations, which is not specifically limited in the embodiment of the present application. The embodiment of the application can also send out capacity expansion reminding by combining with the use condition of resources to prompt capacity expansion, thereby meeting the quality inspection requirement and improving the quality inspection reliability and the quality inspection efficiency.

In one possible implementation manner, when the quality inspection system performs quality inspection according to the second user sampling inspection proportion list, in the sampling inspection process of the same day, the resource early warning is triggered and fed back to the quality inspection grading device, and the device adjusts the sampling inspection proportion according to the resource to form a new list and synchronizes to the quality inspection system. And in the set time period, triggering the capacity expansion reminding of the quality inspection system when the number of times that the quality inspection grading device receives the resource early warning message reaches a certain threshold value.

In a possible implementation manner, fig. 5 is a flow chart of a quality inspection queue rule and a processing method provided by an embodiment of the present application, as shown in fig. 5, a user record is extracted at regular intervals each day, and a specific queuing manner is as follows:

step 1: the time point of extracting the record every day, such as the record extracted for the first time at 9 earlier points, can be set, all records from the last record extracted yesterday to 9 earlier points of the day are extracted, the user record of 100% spot check is arranged at the front end of the queue, and the follow-up sequence is 20%, 10%, 5% and 1%.

Step 2: the system calculates quality inspection resources required by the volume of the extraction record at this time, and the system can set an extraction period, such as T minutes as the longest period, (wherein T is any positive number and can be determined according to actual conditions), and the system is designed according to the following two conditions:

1) Calculating the quality inspection time length required by the first extracted sound recording, if the time length is less than T minutes, calculating the quality inspection completion time of the current extracted sound recording, if s minutes, and if the operation time length for extracting the first sound recording is assumed to be T minutes, starting to extract the call sound recording of 9:00- (9 points +s-T) at the early stage (9 points +s-T), namely extracting the sound recording for the second time;

2) If the quality inspection duration of the extracted sound records is greater than T minutes, placing the user sound records with 1% of sampling inspection proportion into a night quality inspection queue, calculating the quality inspection duration again, and still being greater than T minutes, starting to extract the call sound records of 9:00- (9 points +T-T) at the (9 points +T-T), namely, extracting the sound records for the second time, and if the quality inspection duration of the extracted sound records is less than T minutes, extracting the call sound records for the first time in advance of T minutes on the basis of the calculated completion time point, namely, extracting the sound records for the second time.

Step 3: the second extracted recordings are still arranged in the order of 100%, 20%, 10%, 5% spot check ratio recordings, and if a night queue has been previously generated, the second extracted 1% spot check ratio recordings are arranged at the end of the night queue.

Step 4: the subsequent batch of recordings are extracted according to the steps until all recordings generated on the same day (generally 9 a.k.m. to 21 a.k.m.) are completed, and the quality inspection of the night-time queued recordings is started immediately after the quality inspection of the recordings in 100%, 20%, 10% and 5% spot check proportions is completed.

And when the quality inspection time of the extracted sound records is longer than T minutes, and the user sound records with the lowest sampling inspection proportion (1%) are all discharged into a night quality inspection queue, the quality inspection time can not be reduced within T minutes, the queue position of the finished quality inspection is calculated when the T minutes, when the quality inspection is started for T-T minutes, a new sound record is extracted again, the new sound record and the non-inspected sound records in the original quality inspection queue are queued from high to low according to the quality inspection proportion, and the steps 2-4 are repeated.

According to the method, the call center customer safety rating system and the quality inspection system are matched, the quality inspection grade grading device carries out quality inspection grade grading on customers according to the quality inspection grade grading rule and the safety grade grading, the voice quality inspection sampling proportion is determined through classification grading, the comparison list is synchronized to the quality inspection system, meanwhile, in the running process, the quality inspection system feeds back the resource use condition and triggers resource early warning, the quality inspection grade grading device carries out readjustment on the quality inspection proportion, and the regenerated quality inspection proportion list is synchronized to the quality inspection system. It should be emphasized here that the update period of quality inspection grading depends on the data update of the security rating system, a reasonable rating period can be set according to the security rating model, and the sampling inspection proportion adjustment triggered by the quality inspection system is determined by the real-time resource use condition, so that the sampling inspection proportion adjustment and the sampling inspection proportion adjustment do not interfere with each other. Even if the quality inspection system triggers the sampling inspection proportion adjustment, the cycle time of the security rating system is not influenced by the time node of the adjustment.

Fig. 6 is a schematic structural diagram of a quality inspection data processing apparatus according to an embodiment of the present application, where, as shown in fig. 6, the apparatus according to the embodiment of the present application includes: an acquisition module 601, a first determination module 602, a second determination module 603, a third determination module 604 and a quality inspection module 605. The quality control data processing means may be a server or a terminal device or a chip or an integrated circuit implementing the functions of the server or the terminal device. Here, the division of the acquisition module 601, the first determination module 602, the second determination module 603, the third determination module 604, and the quality inspection module 605 is only a division of a logic function, and both may be integrated or independent physically.

The acquisition module is used for acquiring the user rank scores;

the first determining module is used for determining a first user spot check proportion list according to the user rank scores and a preset proportion rule, wherein the user spot check proportion list comprises spot check proportions of user identifications and call data corresponding to users;

the second determining module is used for acquiring call data of the user and determining total spot check amount according to the call data and the first user spot check proportion list;

and the quality inspection module is used for inspecting the quality of the call data according to the second user sampling inspection proportion list.

Optionally, the first determining module is specifically configured to:

Optionally, the third determining module is specifically configured to:

Comparing the total amount of the spot check with a preset resource threshold; and if the total amount of the spot checks is greater than a preset resource threshold, performing equal proportion down-regulation on the second spot check proportion, the third spot check proportion and the fourth spot check proportion according to a preset rule to obtain a second user spot check proportion list.

Optionally, after the quality inspection module performs quality inspection on the call data according to the second user sampling inspection proportion list, the device further comprises a first trigger module, which is used for;

detecting the residual quantity of resources; and if the resource remaining amount is smaller than the preset resource remaining amount threshold value, triggering resource early warning.

Optionally, after the early warning module is configured to trigger the resource early warning if the resource remaining amount is smaller than the preset resource remaining amount threshold, the apparatus further includes a second triggering module configured to:

according to a preset special user list, performing proportion adjustment on the first user spot check proportion list to obtain a third user spot check proportion list;

Correspondingly, the second determining module is specifically configured to:

Optionally, the quality inspection module is specifically configured to:

according to the second user spot check proportion list, determining spot check total amounts of target call data of users corresponding to different spot check proportions in the second user spot check proportion list; and periodically acquiring the target call data for quality inspection according to the sequence of the sampling inspection proportion corresponding to the target call data.

Referring to fig. 7, there is shown a schematic diagram of a quality inspection data processing apparatus 700 suitable for use in implementing embodiments of the present disclosure, the quality inspection data processing apparatus 700 may be a terminal device or a server. The terminal device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a personal digital assistant (Personal Digital Assistant, PDA for short), a tablet (Portable Android Device, PAD for short), a portable multimedia player (Portable Media Player, PMP for short), an in-vehicle terminal (e.g., an in-vehicle navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The quality inspection data processing apparatus illustrated in fig. 7 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 7, the quality inspection data processing apparatus 700 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 701 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage device 708 into a random access Memory (Random Access Memory, RAM) 703. In the RAM 703, various programs and data required for the operation of the quality inspection data processing apparatus 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other through a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

In general, the following devices may be connected to the I/O interface 705: input devices 706 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 707 including, for example, a liquid crystal display (Liquid Crystal Display, LCD for short), a speaker, a vibrator, and the like; storage 708 including, for example, magnetic tape, hard disk, etc.; and a communication device 709. Communication means 709 may allow quality control data processing apparatus 700 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 shows quality control data processing apparatus 700 having various devices, it should be understood that not all of the illustrated devices are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via communication device 709, or installed from storage 708, or installed from ROM 702. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 701.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The computer readable medium may be embodied in the quality control data processing apparatus; or may be present alone without being assembled into the quality control data processing apparatus.

The computer readable medium carries one or more programs which, when executed by the quality inspection data processing apparatus, cause the quality inspection data processing apparatus to perform the method shown in the above embodiments.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a local area network (Local Area Network, LAN for short) or a wide area network (Wide Area Network, WAN for short), or it may be connected to an external computer (e.g., connected via the internet using an internet service provider).

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A quality control data processing method, comprising:

obtaining a user rank score;

2. The method of claim 1, wherein determining a first user spot check scale list based on the user rank score and a preset scale rule comprises:

If the user rank score is higher than a first preset risk threshold, determining that the sampling rate of call data corresponding to the user is a first sampling rate;

if the user rank score is not higher than the first preset risk threshold value and is higher than the second preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a second sampling rate;

if the user rank score is not higher than the second preset risk threshold value and is higher than the third preset risk threshold value, determining that the sampling rate of the call data corresponding to the user is a third sampling rate;

if the user rank score is not higher than a third preset risk threshold, determining that the sampling rate of the call data corresponding to the user is a fourth sampling rate;

3. The method of claim 2, wherein the adjusting the user spot check ratio list according to the spot check total amount and a preset resource threshold to obtain a second user spot check ratio list includes:

comparing the total amount of the spot check with the preset resource threshold;

and if the total amount of the spot checks is larger than the preset resource threshold, performing equal proportion down-regulation on the second spot check proportion, the third spot check proportion and the fourth spot check proportion according to a preset rule to obtain a second user spot check proportion list.

4. The method of claim 3, further comprising, after said quality inspection of said call data according to said second user spot check scale list;

detecting the residual quantity of resources;

and if the resource remaining amount is smaller than a preset resource remaining amount threshold value, triggering resource early warning.

5. The method of claim 4, further comprising, after triggering the resource pre-warning if the resource remaining is less than a preset resource remaining threshold:

6. The method of any one of claims 1 to 5, further comprising, after said determining a first list of user spot check proportions according to said user rank score and a preset proportion rule:

correspondingly, the acquiring call data of the user, and determining the total amount of the spot checks according to the call data and the first user spot check proportion list comprises the following steps:

7. The method according to any one of claims 1 to 5, wherein the quality inspection of the call data according to the second user spot check scale list comprises:

according to the second user sampling rate list, determining sampling total amount of target call data of users corresponding to different sampling rates in the second user sampling rate list;

and periodically acquiring the target call data for quality inspection according to the sequence of the sampling inspection proportion corresponding to the target call data.

8. The method of claim 7, wherein periodically acquiring the target call data for quality inspection according to the order of the sampling rate corresponding to the target call data comprises:

determining a night quality inspection queue and a daytime quality inspection queue according to the preset quality inspection time length and the quality inspection time length of the target call data;

and according to the order of the sampling rate corresponding to the target call data, acquiring the target call data of the daytime quality inspection queue for quality inspection in a preset daytime quality inspection period, and acquiring the target call data of the nighttime quality inspection queue for quality inspection in a preset nighttime period.

9. A quality control data processing apparatus, comprising:

At least one processor; and

a memory communicatively coupled to the at least one processor; wherein, the liquid crystal display device comprises a liquid crystal display device,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the quality assurance data processing method of any one of claims 1 to 8.

10. A computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, which when executed by a processor are adapted to carry out the quality inspection data processing method according to any one of claims 1 to 8.