CN115860252A - Intelligent monitoring and analyzing method and system for full-channel business - Google Patents

Abstract

The invention provides a method and a system for intelligently monitoring and analyzing full-channel services, which relate to the technical field of intelligent monitoring and comprise the following steps: acquiring historical call data of workers and business handling data of users; carrying out call content identification on the historical call data to generate call characteristics of workers; performing service execution efficiency evaluation to generate service execution efficiency data of workers; constructing a business handling database of the staff; identifying real-time access call data, and matching the service handling database to obtain a staff matching value; acquiring real-time call information of a worker; and performing call access screening of the real-time access call data according to the real-time call information and the staff matching value, so that the technical problem that the service handling efficiency is low because no analysis application is performed on the call historical call data of the staff, and the proper staff cannot be matched for the client in the prior art is solved.

Description

A method and system for intelligent monitoring and analysis of omni-channel business

technical field

The present disclosure relates to the technical field of intelligent monitoring, in particular to a method and system for intelligent monitoring and analysis of omni-channel business.

Background technique

With the diversification of customer service channels, industry regulation and customer requirements for power supply services continue to increase, customer service risks caused by information differences between different channels are getting higher and higher. According to the center's planning and business integration development requirements, combined with the promotion of the company's key work such as marketing surveys, the center's sharing capabilities need to be further improved and optimized. In terms of operation sharing capabilities, it is necessary to further share omni-channel business monitoring data, optimize operation control models, and carry out full-scale Channel full business and indicator monitoring and early warning work. In terms of omni-channel operation control, it is necessary to conduct more in-depth on-site management professional and process-based online research, establish an omni-channel business intelligent prediction model, realize operation management and on-site management intelligent control, improve the intelligent level of omni-channel customer service, and eliminate regularity Business peaks, reducing the number of emergency start-ups and reducing operational service risks.

At present, there is a technical problem in the prior art that due to the lack of analysis and application of the call history call data of the staff, it is impossible to match suitable staff for customers, which leads to low business handling efficiency.

Contents of the invention

This disclosure provides a method and system for intelligent monitoring and analysis of omni-channel business, which is used to solve the problem in the prior art that due to the lack of analysis and application of the historical call data of staff members, it is impossible to match suitable staff for customers, which leads to business Handle technical issues with low efficiency.

According to the first aspect of the present disclosure, a method for intelligent monitoring and analysis of omni-channel business is provided, including: collecting historical call data of staff members and user handling business data, wherein the historical call data and the user handling business data There is a corresponding relationship; the call content is identified on the historical call data, and the call characteristics of the staff are generated based on the content identification result; the service execution efficiency is evaluated according to the call duration of the historical call data and the user handling business data, and the staff member is generated. business execution efficiency data; construct the staff’s business handling database through the business execution efficiency data and the staff’s call characteristics; identify the real-time access call data, and classify the real-time access call data according to the business type Matching with the above-mentioned business processing database to obtain staff matching values; obtaining real-time call information of staff; performing call access screening of the real-time access call data according to the real-time call information and the staff matching values.

According to the second aspect of the present disclosure, an omni-channel service intelligent monitoring and analysis system is provided, including: a data collection module, the data collection module is used to collect and obtain the historical call data of the staff and the business data of the user, wherein, the The historical call data and the user handling business data have a corresponding relationship; the call content identification module is used to identify the call content of the historical call data, and generate staff call features based on the content identification results; An execution efficiency evaluation module, the business execution efficiency evaluation module is used to evaluate the business execution efficiency according to the call duration of the historical call data and the user handling business data, and generate the business execution efficiency data of the staff; the business handling database construction module The business handling database construction module is used to construct the staff's business handling database through the business execution efficiency data and the staff's call characteristics; a business matching module, the business matching module is used to identify real-time access calls Data, matching the business handling database according to the business type of the real-time access call data to obtain the staff matching value; real-time call information acquisition module, the real-time call information acquisition module is used to obtain the real-time call information of the staff A call access screening module, configured to perform call access screening of the real-time access call data according to the real-time call information and the matching value of the staff.

According to a third aspect of the present disclosure, an electronic device is provided, including:

at least one processor; and

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

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method described in the first aspect.

According to an omni-channel business intelligent monitoring and analysis method adopted in the present disclosure, the historical call data of the staff and the user handling business data are collected, wherein the historical call data and the user handling business data have a corresponding relationship; Perform call content identification on historical call data, and generate staff call features based on content identification results; perform service execution efficiency evaluation according to the call duration of the historical call data and the user's business handling data, and generate staff's business execution efficiency data; The business execution efficiency data and the staff call characteristics construct the staff's business handling database; identify the real-time access call data, and match the business handling database according to the business type of the real-time access call data, Obtaining a staff matching value; obtaining real-time call information of the staff; performing call access screening of the real-time access call data according to the real-time call information and the staff matching value. This disclosure analyzes and studies the historical call data of the staff and the user's business handling data, and then builds the business handling database of the staff, matches the staff according to the business handling database, and performs call access screening according to the matching value of the staff, to achieve Select the right staff for the user and improve the technical effect of business handling efficiency.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood through the following description.

Description of drawings

In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following will briefly introduce the drawings that need to be used in the embodiments or the description of the prior art. Obviously, the drawings in the following description are only examples In fact, for those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.

FIG. 1 is a schematic flowchart of an omni-channel business intelligent monitoring and analysis method provided by an embodiment of the present disclosure;

FIG. 2 is a schematic flow diagram of obtaining staff call features in an embodiment of the present disclosure;

FIG. 3 is a schematic flow diagram of optimizing supervision, evaluation and feedback to staff members in an embodiment of the present disclosure;

FIG. 4 is a schematic structural diagram of an omni-channel business intelligent monitoring and analysis system provided by an embodiment of the present disclosure;

FIG. 5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure.

Explanation of reference numerals: data acquisition module 11, call content identification module 12, business execution efficiency evaluation module 13, business handling database construction module 14, business matching module 15, real-time call information acquisition module 16, call access screening module 17 , an electronic device 800, a processor 801, a memory 802, and a bus 803.

Detailed ways

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In order to solve the technical problem in the prior art that due to the lack of analysis and application of the call history call data of the staff, it is impossible to match the appropriate staff for the customer, resulting in low business handling efficiency, the inventors of the present disclosure have worked creatively, A method and system for intelligent monitoring and analysis of omni-channel business of the present disclosure are obtained.

Embodiment one

Figure 1 is a diagram of an omni-channel business intelligent monitoring and analysis method provided in the embodiment of the present application. As shown in Figure 1, the method includes:

Step S100: Collect and obtain the historical call data of the staff and the user handling business data, wherein the historical call data and the user handling business data have a corresponding relationship;

Specifically, the historical call data refers to the call data of the staff in the past period of time, including call duration, call content and other information. It has a corresponding relationship with the user's business data, and provides basic data for subsequent business analysis by obtaining the historical call data of the staff and the user's business data.

Step S200: Perform call content identification on the historical call data, and generate staff call features based on the content identification result;

Specifically, call content identification is performed on historical call data to identify the staff's speaking skills, accuracy, and business handling efficiency, etc., and then generate staff call features based on the content identification results. The staff call features include the words of each staff member. technology, accuracy, efficiency, etc.

Step S300: Evaluate the service execution efficiency according to the call duration of the historical call data and the user handling business data, and generate the service execution efficiency data of the staff;

Specifically, the service execution efficiency is evaluated based on the call duration of historical call data and the user's business processing data. The shorter the call time, and the user's business processing can be completed to a high standard, the higher the business execution efficiency is. Exemplary Yes, the business execution efficiency can be evaluated based on expert evaluation, and then the business execution efficiency data of the staff can be generated. The business execution efficiency data includes the business execution efficiency of each staff member.

Step S400: Construct the staff's business handling database through the business execution efficiency data and the staff's call features;

Specifically, according to the business execution efficiency data and the staff's call characteristics, the staff's business handling database is constructed. In other words, the business handling database includes the staff's business execution efficiency data and the staff's call features.

Step S500: Identify the real-time access call data, match the business handling database according to the business type of the real-time access call data, and obtain the staff matching value;

Specifically, identify real-time access call data, determine the business type of real-time access call data, and then match in the business processing database according to the business type of real-time access call data, match suitable staff, and then obtain staff Matching value, the worker matching value is the worker who is suitable for scheduling calls.

Step S600: Obtain the real-time call information of the staff;

Specifically, the real-time call information of the staff is obtained. The real-time call information refers to whether the staff is on the phone. If the staff is on the phone, it is not suitable to arrange for them to connect the phone.

Step S700: Perform call access screening of the real-time access call data according to the real-time call information and the staff matching value.

Specifically, the call access screening for real-time access to call data is carried out according to the real-time call information and the staff matching value. After the staff members match the values, it is necessary to filter out the staff members who are not currently on the call according to the real-time call information of the staff members, and arrange for them to connect to the real-time access call.

Based on the above analysis, it can be known that the present disclosure provides a method for intelligent monitoring and analysis of omni-channel business. In this embodiment, the staff's business handling database is constructed by analyzing and researching the staff's historical call data and user handling business data. , according to the business handling database to match the staff, according to the matching value of the staff to carry out call access screening, to achieve the technical effect of selecting the appropriate staff for the user and improving the efficiency of business handling.

Wherein, step S700 in the embodiment of the present application further includes:

Step S710: Obtain the accumulated call data of the staff;

Step S720: Divide the accumulative call data according to the multi-level call cycle, and obtain the multi-level call cycle division result;

Step S730: Obtaining the preset weight distribution value of the multi-level call cycle division result;

Step S740: Carry out weighted calculation of the multi-level call cycle division result by using the preset weight distribution value, and obtain call access impact data according to the weighted calculation result;

Step S750: Perform call access screening of the real-time access call data through the call access impact data, the real-time call information and the staff matching value.

Specifically, the cumulative call data of the staff is obtained. The cumulative call data refers to the duration of calls made by each staff member. The cumulative call data is divided according to the multi-level call cycle, and the multi-level call cycle division results are obtained. The multi-level call cycle division It refers to the call duration evaluation data that divides the cumulative call data into multi-level call duration according to different cycle requirements according to multiple call duration requirements. The call cycle division result is the result of dividing the accumulated call data into multi-level call cycles. Further, the preset weight distribution value of the multi-level call cycle division result is obtained, and the multi-level call cycle division result is obtained through the preset weight distribution value. Weighted calculation, the call access impact data is obtained according to the weighted calculation results. Simply put, the call cycle division results of each level have preset weight distribution values. The preset weight distribution values can be set according to the actual situation. According to the preset The distribution weight value performs weighted calculation on the multi-level call cycle division results, and then obtains the weighted calculation result. The weighted calculation result is the call access impact data, and real-time access is performed through the call access impact data, real-time call information and staff matching values Screening of the selected staff during the call access of call data, through the call access screening of real-time access to call data, select more suitable staff for the user to handle business.

Wherein, the call content identification is performed on the historical call data, and the staff call features are generated based on the content identification result, as shown in FIG. 2 , the step S200 of the embodiment of the present application includes:

Step S210: Construct a speech feature set;

Step S220: Perform speech matching of the historical call data through the speech feature set to obtain a speech matching result;

Step S230: Perform a speech matching analysis according to the speech matching result and the user's business data, and obtain a speech matching degree analysis result;

Step S240: Analyzing the result according to the matching result of the speech and the matching degree of the speech.

Specifically, a speech feature set is constructed, which contains various speech features. According to the speech feature set, the speech matching of historical call data is performed, that is, according to the call content of the historical call data, in the speech feature set Perform matching to obtain the speech matching results of the historical call data. The speech matching results are the speech characteristics of the historical call data. Further, the speech matching analysis is performed according to the speech matching results and the user's business data, which is to analyze and determine the speech matching Whether the results match with the user’s business data, and how high the matching degree is, and then obtain the speech matching degree analysis results. According to the speech matching results and speech matching degree analysis results, the staff’s call characteristics are obtained. The staff’s call features include the staff’s words. According to the matching degree of technical features and speech features and business processing data, by obtaining staff call features, users can be matched with suitable staff according to staff call features.

Wherein, step S240 in the embodiment of the present application further includes:

Step S241: Obtain the basic user information of the user;

Step S242: Perform speech skill adaptation evaluation according to the user basic information and the speech skill matching result, and obtain the speech skill adaptation evaluation result;

Step S243: Add the speech skill adaptation evaluation result to the staff call feature.

Specifically, the basic user information of the user is obtained. The basic user information includes the user's education background, language characteristics and other information, and the speech adaptation evaluation is performed based on the user basic information and the speech matching results, that is, based on the user's education background, language characteristics and other information. Determine whether the speech skill matching result is suitable for the user and how high the degree of adaptation is, and then obtain the speech skill adaptation evaluation result, add the speech skill adaptation evaluation result to the staff's call characteristics, and obtain the adaptation evaluation result and add it to the work Personnel call features, improve the staff call features, so that users can be matched with more suitable staff according to the staff call features.

Wherein, step S800 of the embodiment of the present application further includes:

Step S810: Analyze the impact of business processing speed according to the basic user information, and obtain the analysis result of the speed impact value;

Step S820: Compensate the business execution efficiency data according to the analysis result of the speed impact value, and correct the business handling database based on the compensation result.

Specifically, analyze the impact of business processing speed based on the user's basic information. For example, the user's language may be a dialect, resulting in inconvenient communication and affecting the speed of business processing; the user's education is relatively low, making it difficult to communicate, resulting in slow business processing speed ;The user's language is more verbose and cumbersome, which will also affect the speed of business processing. The above are all used for the low speed of business processing due to the difficulty of communication between users. Based on this, the analysis results of the speed impact value are obtained. For the impact value of the business processing speed, further, the business execution efficiency data is compensated according to the analysis result of the speed impact value, that is to say, the business execution efficiency data of the staff obtained in step S300 is only based on the call duration and the user business processing data It does not take into account the difficulty of communication caused by the user itself, which leads to inaccurate business execution efficiency. It is necessary to compensate the business execution efficiency data based on the analysis results of the speed impact value, and then carry out the business processing database based on the compensation results. Correction, by compensating the business execution efficiency data, the business processing database is corrected based on the compensation results, and the matching accuracy is improved when the staff is subsequently matched.

Wherein, as shown in FIG. 3 , step S900 in the embodiment of the present application further includes:

Step S910: Construct a staff training and optimization direction database based on the business handling database;

Step S920: Feedback the training optimization direction database to the corresponding staff, and generate a continuous monitoring interval;

Step S930: Carry out staff optimization supervision evaluation and feedback through the continuous monitoring interval.

Specifically, build a staff training and optimization direction database based on the business processing database. The business processing database contains business execution efficiency data and staff call characteristics. Based on this, determine the staff training and optimization direction, for example, to improve business execution efficiency. Change the call characteristics, etc., and then build the training optimization direction database of the staff, feed back the training optimization direction database to the corresponding staff, and generate a continuous monitoring interval. Continuous monitoring is to monitor the staff for a continuous period of time. Continuous monitoring The time is divided according to certain requirements and divided into different time periods, which are continuous monitoring intervals. According to the continuous monitoring interval, the staff are optimized, supervised, evaluated and given feedback. For example, within a continuous monitoring interval, according to the staff's The training optimization direction database, based on expert evaluation, supervises and evaluates the staff's training optimization situation, and feeds back the results of the supervision and evaluation to the staff. Through the optimization supervision, evaluation and feedback of the staff, the staff's business execution efficiency is improved.

Wherein, step S1000 of the embodiment of the present application further includes:

Step S1010: judging whether the real-time access call data is a new user;

Step S1020: when the real-time access call data is not a new user access, obtain historical connection data;

Step S1030: Perform connection priority evaluation according to the historical connection data, and perform call access screening according to the connection priority evaluation result.

Specifically, it is judged whether the real-time access call data is a new user. Call access screening based on the priority evaluation results, that is, if the real-time access call data is not a new user, obtain the user's historical connection data, and evaluate the connection priority of the staff based on the historical connection data , the history connection staff often connects to the user and has a high score, then you can continue to arrange for the staff to connect, evaluate the connection priority based on the historical connection data, and then make calls according to the connection priority evaluation results Access screening, screening out suitable staff for users to handle business, improving the efficiency of user business processing.

Embodiment two

Based on the same inventive concept as an omni-channel business intelligent monitoring and analysis method in the foregoing embodiments, as shown in FIG. 4 , the present application also provides an omni-channel business intelligent monitoring and analysis system, which includes:

Data collection module 11, described data collection module 11 is used for collecting and obtaining the historical conversation data of staff member, user handles business data, and wherein, described historical call data and described user handle business data have correspondence;

Call content recognition module 12, described call content recognition module 12 is used for carrying out call content recognition to described historical call data, generates staff member's call characteristic based on content recognition result;

A business execution efficiency evaluation module 13, the business execution efficiency evaluation module 13 is used to evaluate the business execution efficiency according to the call duration of the historical call data and the user handling business data, and generate the business execution efficiency data of the staff;

A business handling database construction module 14, the business handling database construction module 14 is used to construct the staff's business handling database through the business execution efficiency data and the staff's call characteristics;

Business matching module 15, the business matching module 15 is used to identify the real-time access call data, and matches the business handling database according to the service type of the real-time access call data to obtain a staff matching value;

Real-time call information acquisition module 16, said real-time call information acquisition module 16 is used to obtain the real-time call information of staff;

A call access screening module 17, configured to perform call access screening of the real-time access call data according to the real-time call information and the staff matching value.

Further, the system also includes:

An accumulative call data acquisition module, the accumulative call data acquisition module is used to obtain the accumulative call data of the staff;

A call cycle division module, the call cycle division module is used to divide the accumulated call data according to multi-level call cycles to obtain multi-level call cycle division results;

A preset weight value acquisition module, the preset weight value acquisition module is used to obtain the preset weight distribution value of the multi-level call cycle division result;

A weighted calculation module, the weighted calculation module is used to perform weighted calculations of the multi-level call cycle division results through the preset weight distribution value, and obtain call access impact data according to the weighted calculation results;

An access call data screening module, the access call data screening module is used to perform the call access of the real-time access call data through the call access impact data, the real-time call information and the staff matching value filter.

Further, the system also includes:

A speech feature set building module, which is used to build a speech feature set;

A speech matching module, the speech matching module is used to perform speech matching of the historical call data through the speech feature set, and obtain a speech matching result;

A speech matching analysis module, the speech matching analysis module is used to perform speech matching analysis according to the speech matching result and the user handling business data, and obtain the speech matching degree analysis result;

A call feature acquisition module, the call feature acquisition module is used to obtain the staff call feature according to the speech matching result and the speech matching degree analysis result.

Further, the system also includes:

A user basic information acquisition module, the user basic information acquisition module is used to obtain the user basic information of the user;

A speech skill adaptation evaluation module, the speech skill adaptation evaluation module is used to perform speech skill adaptation evaluation according to the user basic information and the speech skill matching result, and obtain a speech skill adaptation evaluation result;

A speech technique adaptation evaluation result adding module, the speech technique adaptation evaluation result adding module is used to add the speech technique adaptation evaluation result to the staff's call feature.

Further, the system also includes:

A business processing speed analysis module, the business processing speed analysis module is used to analyze the impact of business processing speed according to the basic user information, and obtain the analysis result of the speed impact value;

A business processing database correction module, the business processing database correction module is used to compensate the business execution efficiency data according to the analysis result of the speed impact value, and correct the business processing database based on the compensation result.

Further, the system also includes:

A training and optimization direction database construction module, the training and optimization direction database construction module is used to construct a staff training and optimization direction database based on the business handling database;

A continuous monitoring interval generation module, the continuous monitoring interval generation module is used to feed back the training optimization direction database to the corresponding staff, and generate a continuous monitoring interval;

An optimization supervision and evaluation feedback module, which is used to perform optimization supervision, evaluation and feedback of staff through the continuous monitoring interval.

Further, the system also includes:

A new user access judgment module, the new user access judgment module is used to judge whether the real-time access call data is a new user access;

A historical connection data acquisition module, the historical connection data acquisition module is used to obtain historical connection data when the real-time access call data is not connected to a new user;

A connection priority evaluation module, the connection priority evaluation module is used to perform connection priority evaluation according to the historical connection data, and perform call access screening according to the connection priority evaluation results.

The specific example of an omni-channel business intelligent monitoring and analysis method in the first embodiment is also applicable to an omni-channel business intelligent monitoring and analysis system in this embodiment. Through the foregoing detailed description of an omni-channel business intelligent monitoring and analysis method, Those skilled in the art can clearly know an omni-channel service intelligent monitoring and analysis system in this embodiment, so for the sake of brevity, details are not described here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and for the related information, please refer to the description of the method part.

Embodiment three

FIG. 5 is a schematic diagram according to a third embodiment of the present disclosure. As shown in FIG. 5 , an electronic device 800 in the present disclosure may include: a processor 801 and a memory 802 .

The memory 802 is used to store programs; the memory 802 may include a volatile memory (English: volatile memory), such as a random-access memory (English: random-access memory, abbreviation: RAM), such as a static random-access memory (English: static random-access memory, abbreviation: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviation: DDR SDRAM), etc.; memory can also include non-volatile memory (English: : non-volatile memory), such as flash memory (English: flash memory). The memory 802 is used to store computer programs (such as application programs, functional modules, etc. for realizing the above methods), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories 802 . And the above-mentioned computer programs, computer instructions, data, etc. can be invoked by the processor 801 .

The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories 802 in partitions. And the above-mentioned computer programs, computer instructions, etc. may be invoked by the processor 801 .

The processor 801 is configured to execute the computer program stored in the memory 802, so as to implement each step in the method involved in the foregoing embodiments.

For details, refer to the related descriptions in the foregoing method embodiments.

The processor 801 and the memory 802 may be an independent structure, or may be an integrated structure integrated together. When the processor 801 and the memory 802 have independent structures, the memory 802 and the processor 801 may be coupled and connected through the bus 803 .

The electronic device in this embodiment can execute the technical solution in the above method, and its specific implementation process and technical principle are the same, and will not be repeated here.

According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, the computer program product including: a computer program, the computer program is stored in a readable storage medium, and at least one processor of an electronic device can read from the readable storage medium Taking a computer program, at least one processor executes the computer program so that the electronic device executes the solution provided by any one of the above embodiments.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present disclosure may be executed in parallel, sequentially, or in a different order.

As long as the desired result of the technical solution disclosed in the present disclosure can be achieved, no limitation is imposed herein.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims (9)

1. A method for intelligent monitoring and analysis of omni-channel business, characterized in that the method comprises: Collect and obtain the historical call data of the staff and the user handling business data, wherein the historical call data and the user handling business data have a corresponding relationship; Carrying out call content identification to the historical call data, and generating staff call features based on the content identification result; Evaluate the service execution efficiency according to the call duration of the historical call data and the user handling business data, and generate the service execution efficiency data of the staff; Constructing the staff's business handling database through the business execution efficiency data and the staff's call characteristics; Identifying the real-time access call data, matching the business handling database according to the business type of the real-time access call data, and obtaining a staff matching value; Obtain the real-time call information of the staff; The call access screening of the real-time access call data is performed according to the real-time call information and the staff matching value. 2. The method of claim 1, further comprising: Obtain the accumulated call data of the staff; Dividing the accumulated call data according to multi-level call cycles to obtain multi-level call cycle division results; Obtaining the preset weight distribution value of the multi-level call cycle division result; performing weighted calculation of the multi-level call cycle division result by using the preset weight distribution value, and obtaining call access impact data according to the weighted calculation result; The call access screening of the real-time access call data is performed through the call access impact data, the real-time call information and the staff matching value. 3. The method as claimed in claim 1, wherein said performing call content identification on said historical call data, and generating staff call features based on content identification results, further comprising: Construct a collection of speech features; Performing speech matching of the historical call data through the speech feature set to obtain a speech matching result; performing a speech matching analysis according to the speech matching result and the user handling business data, and obtaining a speech matching degree analysis result; The staff's call characteristics are obtained according to the speech matching result and the speech matching degree analysis result. 4. The method of claim 3, further comprising: Obtain the basic user information of the user; Performing speech skill adaptation evaluation according to the user basic information and the speech skill matching result, and obtaining a speech skill adaptation evaluation result; The speech adaptation evaluation result is added to the staff conversation feature. 5. The method of claim 4, further comprising: Performing an impact analysis on the speed of business processing according to the basic user information, and obtaining an analysis result of the speed impact value; The business execution efficiency data is compensated according to the speed impact value analysis result, and the business processing database is corrected based on the compensation result. 6. The method of claim 1, further comprising: Constructing staff's training optimization direction database based on the business handling database; Feedback the training optimization direction database to the corresponding staff, and generate a continuous monitoring interval; The optimization supervision evaluation and feedback of the staff are carried out through the continuous monitoring interval. 7. The method of claim 1, further comprising: judging whether the real-time access call data is a new user; When the real-time access call data is not access to a new user, obtain historical connection data; The connection priority evaluation is performed according to the historical connection data, and the call access screening is performed according to the connection priority evaluation results. 8. An omni-channel business intelligent monitoring and analysis system, said system comprising: A data collection module, the data collection module is used to collect and obtain historical call data of staff members and user handling business data, wherein the historical call data and the user handling business data have a corresponding relationship; A call content recognition module, the call content recognition module is used to carry out call content recognition to the historical call data, and generates staff call features based on the content recognition results; A business execution efficiency evaluation module, the business execution efficiency evaluation module is used to evaluate the business execution efficiency according to the call duration of the historical call data and the user handling business data, and generate the staff's business execution efficiency data; A business handling database building module, the business handling database building module is used to build the staff's business handling database through the business execution efficiency data and the staff's call characteristics; A business matching module, the business matching module is used to identify real-time access call data, and match the business handling database according to the service type of the real-time access call data to obtain a staff matching value; A real-time call information acquisition module, the real-time call information acquisition module is used to obtain the staff's real-time call information; A call access screening module, configured to perform call access screening of the real-time access call data according to the real-time call information and the staff matching value. 9. An electronic device comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor can perform any one of claims 1-7. Methods.

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