CN114567703A - Government affair call center optimization method and system - Google Patents
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Abstract
The invention relates to the technical field of data identification and processing, and discloses a government affair call center optimization method and system, which are used for improving the reusability, normalization and reliability of a review planning mechanism in government affair work. The method comprises the following steps: the intelligent outbound server acquires a first outbound task, calls key contents of a review project corresponding to the first outbound task and the identity information of experts capable of bearing the corresponding review task by using a first template, acquires the participation willingness of all experts capable of bearing the corresponding review task to at least two review periods and automatically obtains corresponding statistical data; automatically determining an optimal review time period according to the statistical data by a preset first algorithm, searching association relations meeting avoidance rules based on big data, and generating a grouping review pairing relation avoiding the corresponding association relations; and then, calling key contents, time, grouping and other planning results of the corresponding evaluation projects by using the second template to inform the key contents, the time, the grouping and other planning results to each expert in the optimal evaluation period.
Description
Technical Field
The invention relates to the technical field of data identification and processing, in particular to a government affair call center optimization method and system.
Background
More and more enterprises utilize AI technology to complete the work with lower technical level and high repetition degree, thereby not only saving the cost, but also improving the working efficiency and income.
In governmental affairs, there is a large number of project reviews. Most of the existing methods are that after a customs worker schedules a review plan, a corresponding review expert is reserved through a manual seat; on one hand, the lack of the normative and integrity of information transmission is easy to cause; on the other hand, most of the manually scheduled review plans are unilateral decision behaviors under the condition of no investigation, and the number of experts receiving the review task is lower than the expected number of bad results due to factors such as time conflicts of the experts; moreover, the method of manually screening the evaluation experts cannot check and evade the evasive relationship between the evaluation experts and the evaluated object, and is easily questioned by fairness and fairness, etc.
Disclosure of Invention
The invention aims to disclose a government affair call center optimization method and system to improve the reusability, normalization and reliability of a review planning mechanism in government affair work.
To achieve the above object, the present invention discloses a government affairs call center optimization method, which comprises:
step S1, setting a first template and a second template in an intelligent outbound server connected with an expert database and a review project database, wherein the first template and the second template are shared by at least two review projects;
step S2, the intelligent outbound server acquires a first outbound task, calls the key content of the review project corresponding to the first outbound task and the identity information of the experts capable of bearing the corresponding review task by the first template, collects the participation willingness of all the experts capable of bearing the corresponding review task to at least two review periods and automatically obtains corresponding statistical data;
step S3, the intelligent outbound server automatically determines an optimal review period according to the statistical data by a preset first algorithm, searches whether each expert participating in the optimal review period has an association relation meeting an avoidance rule with any one reviewed object based on big data, and then inputs the number of the reviewed objects, the number of the experts in the optimal review period and the association relation between the corresponding reviewed object meeting the avoidance rule and the corresponding expert into a preset second algorithm to automatically generate a corresponding group review pairing relation;
and step S4, the intelligent outbound server acquires a second outbound task, and calls the key content of the corresponding review project, the distribution information of the optimal review period and the grouping condition of each expert determined based on the grouping pairing relationship to inform each expert of the optimal review period by the second template.
To achieve the above object, the present invention also discloses a government affairs call center optimization system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above method when executing the computer program.
The invention has the following beneficial effects:
on one hand, no matter the early-stage outbound acquisition investigation data and statistics, the decision of the optimal evaluation period and the grouped evaluation pairing relation in the middle stage, or the outbound notification of the subsequent decision result, the whole process can automatically run based on the program after the user sets the first outbound task and the second outbound task; the manual intervention is greatly reduced, the efficiency is improved, and simultaneously the scientificity, the privacy and the fairness of the system in the whole review planning process are ensured.
On the other hand, in the system operation process, the standardized first template and the standardized second template are used for facing a plurality of evaluation projects and each evaluation expert, the template reuse rate is improved, and the normalization, the integrity and the reliability in the information outbound process are ensured.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a government affairs call center optimization method disclosed by an embodiment of the invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses a method for optimizing a government affair call center, as shown in fig. 1, including:
and step S1, setting a first template and a second template in the intelligent outbound server connected with the expert database and the review project database, wherein the first template and the second template are shared by at least two review projects.
Preferably, before the step, a call center data processing system is set up in advance, and Identity information and an ID (Identity, identification/label) of an item which can be responsible for evaluation, which correspond to each expert, are recorded in an expert database; wherein the identity information at least comprises a unique identity ID and a contact phone in the library; the evaluation project database is provided with key contents corresponding to at least two evaluation projects and a list of evaluated objects corresponding to each evaluation project, and the key contents at least comprise evaluation project names and abstracts.
And step S2, the intelligent outbound server acquires the first outbound task, calls the key content of the corresponding review item of the first outbound task and the identity information of the experts capable of bearing the corresponding review task by using the first template, collects the participation willingness of all the experts capable of bearing the corresponding review task to at least two review periods and automatically obtains corresponding statistical data.
In this step, the first outbound task is typically provided with the start time and the ID of the review item. By setting the starting time, the user can delay the execution time of the first outbound task after the first outbound task is generated, and the flexibility of user operation is improved.
Preferably, the process of executing the first outbound task in this step specifically includes: the method comprises the steps of calling key contents and expert identity information of a review project corresponding to a current first outbound task by using a first template to obtain text contents corresponding to each node in each expert interaction process of the optimal review period, then initiating a call request, switching on a call line, converting the text contents of the corresponding node into voice signals according to the interaction process, sending the voice signals to a called expert, tracking, identifying and recording information fed back by the called expert, selecting the text contents corresponding to a next node according to a feedback result of the current node, converting the text contents into the voice signals, sending the voice signals to the called expert, and so on until a call is ended.
Step S3, the intelligent outbound server automatically determines the optimal evaluation period according to the statistical data by a preset first algorithm, searches whether each expert participating in the optimal evaluation period has an association relation meeting the avoidance rule with any evaluated object based on big data, and then inputs the number of the evaluated objects, the number of the experts in the optimal evaluation period and the association relation between the corresponding evaluated object meeting the avoidance rule and the corresponding expert into a preset second algorithm to automatically generate a corresponding group evaluation pairing relation.
In the searching process based on big data in the step, the data of platforms such as enterprise investigation, sky eye investigation and letter opener can be called and the household registration data in the public security system is combined for investigation. Wherein the specific avoidance rule is based on a rule common to all the projects multiplexing the first and second templates; for example, if the evaluated project is an enterprise, the avoidance rules may be specifically set as: the review expert or its immediate relatives are the natural stakeholders or the main people of the corresponding enterprise. And as a support, the identification card number corresponding to each expert is required to be set in an expert database; correspondingly, if the third-party platform stores the identity card numbers of the natural shareholders or the main personnel of each enterprise, the third-party platform does not need to be repeatedly arranged in the list of the evaluated objects in the evaluation project database. In the specific big data checking process, the identity card number of the expert to be evaluated and the direct relatives obtained by the account data in the public security system, the complete name information of the object to be evaluated or the unique identity of the tax number and the like in the whole network can be sent to the third party platform and the checking result returned by the third party platform is waited. If the evaluation project is of personal reputation type, because the common phenomenon of renegotiation exists in the registration process of the household registration, the personal identity number of the evaluated object needs to be carried in the investigation request sent to the third-party platform.
In this step, the specific first algorithm can be flexibly set according to the usual rules of project scheduling. For example: the evaluation period selected by the expert with the largest number can be taken as the optimal evaluation period; if more than two groups of evaluation time intervals with equal numbers of participating experts are met, the earliest time interval is taken as the optimal evaluation time interval.
Similarly, in this step, the second algorithm can be flexibly set according to the conventional rules of project arrangement, including but not limited to the equipartition algorithm and the like; and the constraint conditions of each group are constrained, such as the lowest group number is more than or equal to 2, the lowest number of people in each group and whether the total number of people in each group is odd, etc. In addition, the first and second algorithms in this step also have universality for each review item sharing the first and second templates.
And step S4, the intelligent outbound server acquires a second outbound task, and calls the key content of the corresponding review project, the distribution information of the optimal review period and the grouping condition of each expert determined based on the grouping pairing relationship by using a second template to inform each expert of the optimal review period.
Similarly, the specific execution process of the second outbound task may refer to the first outbound task, which is not described in detail.
In this embodiment, the multiplexed first, second, and subsequent third templates include contents with universality for different experts and different review projects between nodes preset according to the interactive logic, and for differences between each project and each review expert (such as names and abstracts of review projects, screening review time periods, or determined review time, etc.), corresponding contents are called from the expert database and the review project database and automatically filled to combine into complete text contents corresponding to each node in the interactive process with the outbound target expert; wherein the results determined based on the first and second algorithms are considered to be content in the review project database. For example: in the opening of the market, worship words such as "respected" and the like are the solidified contents of corresponding nodes of the template, and corresponding expert names, genders such as "mr.", "ms" and the like can be automatically filled after the corresponding expert names, the "mr. and the" ms "and the like are acquired through identity information of an identity ID inquiry expert database. The multiple key information (such as name, gender, etc.) can be classified and stored through fields in the database form for convenient calling, and the calling relationship between the content to be filled in each template and the corresponding fields in the corresponding database is pre-established, which is a conventional technology of the database and is not described in detail.
Preferably, the method of this embodiment may further include: the intelligent outbound server acquires a third outbound task, calls the key content of the corresponding evaluation project and the distribution information of the optimal evaluation time period by facing a third template for at least two evaluation projects, and informs each evaluated object corresponding to the corresponding evaluation project. Thus, in addition to the above-mentioned common features of combination and adaptive filling, the first, second and third templates of the present embodiment are different in specific function configuration, that is, at least some node functions between the templates are different in division and the oriented outbound objects are different.
In this embodiment, the function of converting text content into speech and converting speech in the expert feedback information into text can be implemented by calling an API interface of a third-party platform (e.g., a speech platform such as keda news). In the process of semantic recognition of feedback information, a corresponding model can be established in advance aiming at various feedback conditions corresponding to each node, a plurality of deformed similar sentences are considered together besides a standard sentence pattern when modeling, a similar series of sentence patterns are divided into a plurality of parts through a word segmentation technology when the model is trained, then the characteristics in the parts are learned, and finally a classification model which can output at least two different feedback results is formed. The analytical correspondence of the model is: request → word segmentation → word vector → CNN (convolution → pooling) → determining a result type corresponding to the feedback information.
Preferably, the embodiment may further store voiceprint feature information of each expert in the expert database, record a voice signal fed back by the expert at each node when identifying that the corresponding expert feeds back information in a voice manner during a call process of the outbound call by the intelligent outbound server, and set an intermediate node for executing the following logic in the first template interaction flow:
and combining the voice signals of the recorded nodes of the call of the corresponding expert into a whole, extracting corresponding voiceprint characteristics, and comparing the extracted voiceprint characteristics with the prestored voiceprint characteristic information to verify the identity of the corresponding expert.
In general, the most common in voiceprint authentication is a broadband voiceprint image. It is a voiceprint analyzed with a bandpass filter with a bandwidth of 300 HZ. The horizontal axis of the voiceprint is time, the vertical axis is frequency, and the shade represents the sound intensity. The front part (striae) of the voiceprint of each character is the frequency spectrum of the unvoiced consonant, and the rear part is the frequency spectrum of the vowel; the horizontal black band formed by the reinforced vertical lines in the vowel spectrum is a formant. The number, orientation and frequency of the formants are important features for voiceprint analysis. The specific procedures for voiceprint identification are known to those skilled in the art and will not be described in detail. Alternatively, if voiceprint authentication fails, the call can be terminated after the called reason and the corresponding complaint path are informed.
Through voiceprint identification, the expected purpose can be achieved under the condition that an expert is unaware, and the user experience is improved. In addition, the check node is arranged in the middle of the main process and combined with the voice signals of the recorded nodes of the current call of the corresponding expert to form a whole, and then the voiceprint features are extracted, so that misjudgment caused by few voice input samples is effectively avoided.
As a variation, the manner of feeding back information by the corresponding expert may be replaced by a key-press manner. In addition, the prior art supports the mixed use of two information feedback modes in the call center, which is not described in detail.
Further, the embodiment can notify the corresponding expert in advance by a short message before initiating the call request for executing the first outbound task, so as to improve the success rate of outbound call connection. On the basis of reminding by the corresponding short message in advance, if the called party is refused to take part for more than two times continuously or the called party is hung up after the called party is put through and before the called party expert definitely participates in commitment, the method can uniformly count the refused taking part in evaluation; thereby improving the timeliness of data acquisition and statistics.
Example 2
Corresponding to the above embodiments, the present embodiment discloses a government affair call center optimization system, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the relevant steps corresponding to the above method.
In summary, the government affair call center optimization method and system respectively disclosed by the above embodiments of the present invention have the following advantages:
on one hand, no matter the early-stage outbound acquisition investigation data and statistics, the decision of the optimal evaluation period and the grouped evaluation pairing relation in the middle stage, or the outbound notification of the subsequent decision result, the whole process can automatically run based on the program after the user sets the first outbound task and the second outbound task; the manual intervention is greatly reduced, the efficiency is improved, and simultaneously the scientificity, the privacy and the fairness of the system in the whole review planning process are ensured.
On the other hand, in the system operation process, the standardized first template and the standardized second template are used for facing a plurality of evaluation projects and each evaluation expert, the template reuse rate is improved, and the normalization, the integrity and the reliability in the information outbound process are ensured.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for government call center optimization, comprising:
step S1, setting a first template and a second template in an intelligent outbound server connected with an expert database and a review project database, wherein the first template and the second template are shared by at least two review projects;
step S2, the intelligent outbound server acquires a first outbound task, calls the key content of the review project corresponding to the first outbound task and the identity information of the experts capable of bearing the corresponding review task by the first template, collects the participation willingness of all the experts capable of bearing the corresponding review task to at least two review periods and automatically obtains corresponding statistical data;
step S3, the intelligent outbound server automatically determines an optimal review period according to the statistical data by a preset first algorithm, searches whether each expert participating in the optimal review period has an association relation meeting an avoidance rule with any one reviewed object based on big data, and then inputs the number of the reviewed objects, the number of the experts in the optimal review period and the association relation between the corresponding reviewed object meeting the avoidance rule and the corresponding expert into a preset second algorithm to automatically generate a corresponding group review pairing relation;
and step S4, the intelligent outbound server acquires a second outbound task, and calls the key content of the corresponding review project, the distribution information of the optimal review period and the grouping condition of each expert determined based on the grouping pairing relationship to inform each expert of the optimal review period by the second template.
2. The method according to claim 1, wherein the expert database is entered with identity information and an ID of an item which can be responsible for review, which correspond to each expert, the identity information at least including a unique identity ID and a contact phone in the database; the review project database is provided with key contents corresponding to at least two review projects and a list of the reviewed objects corresponding to each review project, wherein the key contents at least comprise review project names and abstracts; and the first outbound task is provided with starting time and an ID of a review item.
3. The method of claim 2, wherein in performing the first outbound task, comprising:
and calling key contents and expert identity information of the evaluation project corresponding to the current first outbound task by using the first template to obtain text contents corresponding to each node in each expert interaction flow of the optimal evaluation period, then initiating a call request, switching on a call line, converting the text contents of the corresponding node into voice signals according to the interaction flow, sending the voice signals to a called expert, tracking, identifying and recording information fed back by the called expert, selecting the text contents corresponding to the next node according to the feedback result of the current node, converting the text contents into the voice signals, sending the voice signals to the called expert, and so on until the call is finished.
4. The method according to any one of claims 1 to 3, wherein the expert database further stores voiceprint feature information of each expert, the intelligent outbound server records voice signals fed back by the experts at each node when identifying that the corresponding experts feed back information in a voice mode in the process of outbound call, and an intermediate node for executing the following logic is arranged in the first template interaction flow:
and combining the voice signals of the recorded nodes of the call of the corresponding expert into a whole, extracting corresponding voiceprint characteristics, and comparing the extracted voiceprint characteristics with the prestored voiceprint characteristic information to verify the identity of the corresponding expert.
5. The method of claim 4, wherein the means for the called expert to feed back information comprises a push button means.
6. The method of claim 4, wherein the corresponding expert is pre-notified with a short message before initiating the call request to perform the first outbound task.
7. The method of claim 4, further comprising:
the intelligent outbound server acquires a third outbound task, so as to call the key content of the corresponding evaluation project and the distribution information of the optimal evaluation time period facing a third template multiplexed by at least two evaluation projects to inform each evaluated object corresponding to the corresponding evaluation project.
8. A government call center optimization system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program performs the steps of the method of any one of claims 1 to 7.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133332A (en) * | 2018-01-17 | 2018-06-08 | 政和科技股份有限公司 | The method, apparatus and server that a kind of expert extracts |
| CN108388996A (en) * | 2018-03-08 | 2018-08-10 | 青岛东方深蓝信息科技股份有限公司 | A kind of scientific research service integration management system |
| CN108416571A (en) * | 2018-03-08 | 2018-08-17 | 青岛东方深蓝信息科技股份有限公司 | A kind of application method of scientific research service integration management system |
| CN109376167A (en) * | 2018-09-26 | 2019-02-22 | 国家海洋信息中心 | Selection of specialists method, apparatus and server |
| CN109992642A (en) * | 2019-03-29 | 2019-07-09 | 华南理工大学 | A kind of automatic method of selecting of single task expert and system based on scientific and technological entry |
| CN110097246A (en) * | 2019-03-14 | 2019-08-06 | 郭琳琳 | Intelligent reviewing method and system, computer installation, computer readable storage medium |
| CN112801530A (en) * | 2021-02-05 | 2021-05-14 | 江西清能高科技术有限公司 | Intelligent review system based on semantic splitting and working method |
-
2022
- 2022-04-13 CN CN202210383811.9A patent/CN114567703B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133332A (en) * | 2018-01-17 | 2018-06-08 | 政和科技股份有限公司 | The method, apparatus and server that a kind of expert extracts |
| CN108388996A (en) * | 2018-03-08 | 2018-08-10 | 青岛东方深蓝信息科技股份有限公司 | A kind of scientific research service integration management system |
| CN108416571A (en) * | 2018-03-08 | 2018-08-17 | 青岛东方深蓝信息科技股份有限公司 | A kind of application method of scientific research service integration management system |
| CN109376167A (en) * | 2018-09-26 | 2019-02-22 | 国家海洋信息中心 | Selection of specialists method, apparatus and server |
| CN110097246A (en) * | 2019-03-14 | 2019-08-06 | 郭琳琳 | Intelligent reviewing method and system, computer installation, computer readable storage medium |
| CN109992642A (en) * | 2019-03-29 | 2019-07-09 | 华南理工大学 | A kind of automatic method of selecting of single task expert and system based on scientific and technological entry |
| CN112801530A (en) * | 2021-02-05 | 2021-05-14 | 江西清能高科技术有限公司 | Intelligent review system based on semantic splitting and working method |
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