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

Abstract

The present disclosure provides a method and system for intelligent monitoring and analysis of omni-channel business, relating to the field of intelligent monitoring technology. The method comprises: collecting and obtaining historical call data of staff members and business processing data of users; performing call content recognition on the historical call data to generate call features of staff members; performing business execution efficiency evaluation to generate business execution efficiency data of staff members; constructing a business processing database of the staff members; identifying real-time access call data, matching the business processing database to obtain staff matching values; obtaining real-time call information of staff members; and performing call access screening on the real-time access call data according to the real-time call information and the staff matching values. The method solves the technical problem in the prior art that no analysis and application of historical call data of staff members can be performed, which leads to low business processing efficiency and inability to match suitable staff members to customers.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent monitoring technology, and in particular to an omni-channel business intelligent monitoring and analysis method and system. Background Art

[0002] With the diversification of customer service channels, increasing industry regulation and customer demands for power supply services, customer service risks caused by information disparities between different channels are becoming increasingly high. Based on the center's planning and business integration development requirements, combined with the company's key tasks such as marketing surveys, the center's sharing capabilities urgently need to be further enhanced and optimized. In terms of operational sharing capabilities, it is necessary to further share omni-channel business monitoring data, optimize operational control models, and conduct omni-channel, full-business, and indicator monitoring and early warning work. In terms of omni-channel operational control, it is necessary to conduct more in-depth online research on on-site management expertise and processes, establish an omni-channel business intelligent prediction model, realize intelligent control of operational management and on-site management, improve the intelligence level of omni-channel customer service, eliminate regular business peaks, reduce the number of emergency startups, and mitigate operational service risks.

[0003] Currently, there is a technical problem in the existing technology that due to the lack of analysis and application of staff's historical call data, it is impossible to match customers with suitable staff, which leads to low business processing efficiency. Summary of the Invention

[0004] The present disclosure provides an omni-channel business intelligent monitoring and analysis method and system to solve the technical problem in the prior art that due to the lack of analysis and application of staff's historical call data, suitable staff cannot be matched to customers, which leads to low business processing efficiency.

[0005] According to the first aspect of the present disclosure, a method for intelligent monitoring and analysis of omni-channel business is provided, including: collecting and obtaining historical call data of staff members and business processing data of users, wherein the historical call data and the business processing data of users have a corresponding relationship; performing call content recognition on the historical call data, and generating call features of staff members based on the content recognition results; performing business execution efficiency evaluation based on the call duration of the historical call data and the business processing data of users, and generating business execution efficiency data of staff members; constructing a business processing database of the staff members through the business execution efficiency data and the staff call features; identifying real-time access call data, matching the business processing database according to the business type of the real-time access call data, and obtaining a staff matching value; obtaining real-time call information of staff members; and performing call access screening of the real-time access call data based on the real-time call information and the staff matching value.

[0006] According to a second aspect of the present disclosure, an omni-channel business intelligent monitoring and analysis system is provided, comprising: a data collection module, configured to collect and obtain historical call data of staff members and user business processing data, wherein the historical call data and the user business processing data have a corresponding relationship; a call content recognition module, configured to perform call content recognition on the historical call data and generate staff call features based on the content recognition results; a business execution efficiency evaluation module, configured to evaluate business execution efficiency based on call durations of the historical call data and the user business processing data to generate business execution efficiency data for the staff members; a business processing database construction module, configured to construct a business processing database for the staff members using the business execution efficiency data and the staff call features; a business matching module, configured to identify real-time access call data and match the business processing database according to the business type of the real-time access call data to obtain a staff matching value; a real-time call information acquisition module, configured to obtain real-time call information of the staff members; and a call access screening module, configured to perform call access screening on the real-time access call data based on the real-time call information and the staff matching value.

[0007] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0008] at least one processor; and

[0009] a memory communicatively connected to the at least one processor; wherein,

[0010] The memory stores instructions that can be executed by the at least one processor. The instructions are executed by the at least one processor to enable the at least one processor to perform the method according to the first aspect.

[0011] According to an omni-channel business intelligent monitoring and analysis method adopted by the present disclosure, historical call data of staff members and business processing data of users are collected, wherein the historical call data and the business processing data of users have a corresponding relationship; call content recognition is performed on the historical call data, and staff call features are generated based on the content recognition results; business execution efficiency is evaluated based on the call duration of the historical call data and the business processing data of users, and business execution efficiency data of staff members are generated; a business processing database of the staff members is constructed based on the business execution efficiency data and the staff call features; real-time access call data is identified, and the business processing database is matched according to the business type of the real-time access call data to obtain a staff matching value; real-time call information of staff members is obtained; and call access screening of the real-time access call data is performed based on the real-time call information and the staff matching value. The present disclosure analyzes and studies the historical call data of staff members and the business processing data of users, and then constructs a business processing database of staff members, matches staff members according to the business processing database, and performs call access screening based on the staff matching value, thereby achieving the technical effect of selecting suitable staff members for users and improving business processing efficiency.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the present disclosure or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without any creative work.

[0014] Figure 1 A flowchart of an omni-channel business intelligent monitoring and analysis method provided by an embodiment of the present disclosure;

[0015] Figure 2 A schematic diagram of a process for obtaining staff call characteristics in an embodiment of the present disclosure;

[0016] Figure 3 Schematic diagram of a process for optimizing supervision, evaluation and feedback of staff members in an embodiment of the present disclosure;

[0017] Figure 4 A schematic diagram of the structure of an omni-channel business intelligent monitoring and analysis system provided by an embodiment of the present disclosure;

[0018] Figure 5 A schematic structural diagram of an electronic device provided in an embodiment of the present disclosure.

[0019] Explanation of the reference numerals: data collection module 11, call content recognition module 12, business execution efficiency evaluation module 13, business processing database construction module 14, business matching module 15, real-time call information acquisition module 16, call access screening module 17, electronic device 800, processor 801, memory 802, bus 803. DETAILED DESCRIPTION

[0020] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0021] In order to solve the technical problem in the prior art that due to the lack of analysis and application of staff's historical call data, suitable staff cannot be matched to customers, which leads to low business processing efficiency, the inventors of the present invention have obtained an omni-channel business intelligent monitoring and analysis method and system through creative work.

[0022] Example 1

[0023] Figure 1 A diagram of an omni-channel business intelligent monitoring and analysis method provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0024] Step S100: collecting and obtaining staff's historical call data and user's business data, wherein the historical call data and the user's business data have a corresponding relationship;

[0025] Specifically, historical call data refers to the call data of staff in the past period of time, including call duration, call content and other information. User business processing data includes information such as the type of business handled by the user and business processing efficiency. Among them, historical call data and user business processing data have a corresponding relationship. By obtaining the historical call data of staff and the user business processing data, basic data is provided for subsequent business analysis.

[0026] Step S200: performing call content recognition on the historical call data, and generating staff call features based on the content recognition results;

[0027] Specifically, the call content of historical call data is recognized to identify the staff's speech skills, accuracy, business processing efficiency, etc., and then the staff call characteristics are generated based on the content recognition results. The staff call characteristics include the speech skills, accuracy, efficiency, etc. of each staff member.

[0028] Step S300: Evaluate the service execution efficiency based on the call duration of the historical call data and the user's service processing data to generate service execution efficiency data for the staff member;

[0029] Specifically, the business 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 higher the standard of completing the user's business processing, the higher the business execution efficiency. For example, the business execution efficiency evaluation can be based on expert evaluation to generate the staff's business execution efficiency data. The business execution efficiency data includes the business execution efficiency of each staff member.

[0030] Step S400: constructing a business processing database of the staff member based on the business execution efficiency data and the staff member's call characteristics;

[0031] Specifically, a business processing database for staff members is constructed based on business execution efficiency data and staff call characteristics. In other words, the business processing database contains business execution efficiency data and staff call characteristics.

[0032] Step S500: Identify real-time access call data, match the service handling database according to the service type of the real-time access call data, and obtain a staff matching value;

[0033] Specifically, the real-time access call data is identified, the business type of the real-time access call data is determined, and then the business type of the real-time access call data is matched in the business processing database to match the appropriate staff, and then the staff matching value is obtained. The staff matching value is the staff suitable for arranging to connect the call.

[0034] Step S600: obtaining the real-time call information of the staff;

[0035] Specifically, the real-time call information of the staff is obtained. The real-time call information refers to whether the staff is currently on the phone. If the staff is currently on the phone, it is not appropriate to arrange for them to answer the call.

[0036] Step S700: performing call access screening of the real-time access call data according to the real-time call information and the staff matching value.

[0037] Specifically, call access screening is performed on real-time access call data based on real-time call information and staff matching values. The staff matching value is the staff member who is suitable for accessing the call, but the staff member may be on a call. Therefore, after obtaining the staff matching value, it is necessary to screen out the staff member who is not currently on a call based on the staff member's real-time call information and arrange for them to connect the real-time access call.

[0038] Based on the above analysis, it can be seen that the present disclosure provides an omni-channel business intelligent monitoring and analysis method. In this embodiment, by analyzing and studying the staff's historical call data and user business processing data, a staff business processing database is constructed, and the staff is matched according to the business processing database. Call access screening is performed according to the staff matching value, so as to achieve the technical effect of selecting suitable staff for users and improving business processing efficiency.

[0039] In this embodiment, step S700 further includes:

[0040] Step S710: Obtaining the staff member's cumulative call data;

[0041] Step S720: Divide the accumulated call data into multiple levels of call cycles to obtain a multi-level call cycle division result;

[0042] Step S730: obtaining a preset weight distribution value of the multi-level call cycle division result;

[0043] Step S740: performing weighted calculation on the multi-level call cycle division results using the preset weight distribution values, and obtaining call access impact data based on the weighted calculation results;

[0044] Step S750: performing call access screening of the real-time access call data using the call access impact data, the real-time call information, and the staff matching value.

[0045] Specifically, the accumulated call data of the staff is obtained. The accumulated call data refers to the duration of each staff member's calls. The accumulated call data is divided according to multi-level call cycles to obtain the multi-level call cycle division results. Multi-level call cycle division refers to dividing the accumulated call data into multi-level call duration evaluation data according to different cycles based on multiple call duration requirement standards. For example, multi-level call cycle division can be performed according to three cycles: one day, one week, and one month. The multi-level call cycle division result is the result of dividing the accumulated call data into multi-level call cycles. Furthermore, the preset weight distribution value of the multi-level call cycle division result is obtained. Through the preset weight distribution value Perform weighted calculation on the multi-level call cycle division results, and obtain call access impact data based on the weighted calculation results. Simply put, each level of call cycle division results has a preset weight distribution value, which can be set according to actual conditions. Perform weighted calculation on the multi-level call cycle division results based on the preset distribution weight values to obtain the weighted calculation results. The weighted calculation results are the call access impact data. The call access impact data, real-time call information and staff matching values are used to screen the staff selected during call access of real-time access call data. Through the call access screening of real-time access call data, users can select more suitable staff to handle business.

[0046] The historical call data is subjected to call content recognition, and staff call features are generated based on the content recognition results, such as Figure 2 As shown, step S200 of the embodiment of the present application includes:

[0047] Step S210: constructing a speech feature set;

[0048] Step S220: performing speech matching on the historical call data using the speech feature set to obtain a speech matching result;

[0049] Step S230: performing a speech matching analysis based on the speech matching result and the user's business handling data to obtain a speech matching degree analysis result;

[0050] Step S240: Analyze the results based on the speech matching results and the speech matching degree.

[0051] Specifically, a speech feature set is constructed, which includes various speech features. According to the speech feature set, speech matching is performed on historical call data, that is, the call content of the historical call data is matched in the speech feature set to obtain the speech matching results of the historical call data. The speech matching results are the speech features of the historical call data. Furthermore, speech matching analysis is performed based on the speech matching results and the user's business processing data. That is, it is analyzed to determine whether the speech matching results and the user's business processing data match and how high the matching degree is, and then the speech matching degree analysis results are obtained. According to the speech matching results and the speech matching degree analysis results, the staff call features are obtained. The staff call features include the staff's speech features and the matching degree between the speech features and the business processing data. By obtaining the staff call features, the user can be matched with suitable staff based on the staff call features.

[0052] In this embodiment, step S240 further includes:

[0053] Step S241: Obtaining basic user information of the user;

[0054] Step S242: performing speech adaptation evaluation based on the user basic information and the speech matching result to obtain a speech adaptation evaluation result;

[0055] Step S243: Add the speech adaptation evaluation result to the staff call characteristics.

[0056] Specifically, the user's basic information is obtained, which includes the user's education level, language characteristics and other information. The speech adaptation evaluation is performed based on the user's basic information and the speech matching results. That is, based on the user's education level, language characteristics and other information, it is determined whether the speech matching result is suitable for the user and how high the adaptation degree is. Then, the speech adaptation evaluation result is obtained, and the speech adaptation evaluation result is added to the staff call characteristics. By obtaining the adaptation evaluation result and adding it to the staff call characteristics, the staff call characteristics are improved, which facilitates matching more suitable staff for users based on the staff call characteristics.

[0057] In this embodiment, step S800 further includes:

[0058] Step S810: performing a business processing speed impact analysis based on the user basic information to obtain a speed impact value analysis result;

[0059] Step S820: Compensate the business execution efficiency data according to the speed impact value analysis result, and modify the business processing database based on the compensation result.

[0060] Specifically, an analysis of the impact of business processing speed is performed based on the basic information of the user. For example, the user's language may be a dialect, which leads to communication difficulties and affects the business processing speed; the user's education level is relatively low, which makes it difficult to communicate, resulting in a slower business processing speed; the user's language is verbose and cumbersome, which also affects the business processing speed. The above are all used for the case where the user has difficulty in communicating, resulting in a slower business processing speed. Based on this, a speed impact value analysis result is obtained. The speed impact value analysis result is the impact value of the user himself on the business processing speed. Furthermore, the business execution efficiency data is compensated according to the speed impact value analysis result. That is, the business execution efficiency data of the staff member obtained in step S300 is only obtained based on the call duration and the user's business processing data, and does not take into account the problem of difficulty in communication caused by the user himself, which leads to inaccurate business execution efficiency. It is necessary to compensate the business execution efficiency data according to the speed impact value analysis result, and then correct the business processing database based on the compensation result. By compensating the business execution efficiency data and correcting the business processing database based on the compensation result, the matching accuracy is improved when the staff member is matched subsequently.

[0061] Among them, Figure 3 As shown, step S900 in this embodiment of the application further includes:

[0062] Step S910: constructing a staff training optimization direction database based on the business processing database;

[0063] Step S920: Feedback the training optimization direction database to the corresponding staff and generate a continuous monitoring interval;

[0064] Step S930: Optimizing supervision, evaluation and feedback of staff members are performed through the continuous monitoring interval.

[0065] Specifically, a staff training optimization direction database is constructed based on the business processing database. The business processing database contains business execution efficiency data and staff call characteristics. Based on this, the staff training optimization direction is determined, for example, improving business execution efficiency, changing call characteristics, etc., and then the staff training optimization direction database is constructed. The training optimization direction database is fed back to the corresponding staff, and a continuous monitoring interval is generated. Continuous monitoring is to monitor the staff over a continuous period of time. The continuous monitoring time is divided according to certain requirements into different time periods, namely the continuous monitoring interval. The staff is optimized, supervised, evaluated and feedback is given according to the continuous monitoring interval. For example, within a continuous monitoring interval, according to the staff training optimization direction database, the staff training optimization situation is supervised and evaluated based on expert evaluation, and the supervision and evaluation results are fed back to the staff. Through the optimization supervision, evaluation and feedback of the staff, the business execution efficiency of the staff is improved.

[0066] In this embodiment, step S1000 further includes:

[0067] Step S1010: determining whether the real-time access call data is for a new user;

[0068] Step S1020: When the real-time incoming call data is not about a new user, obtaining historical connection data;

[0069] Step S1030: performing connection priority evaluation based on the historical connection data, and performing call access screening based on the connection priority evaluation result.

[0070] Specifically, determine whether the real-time access call data is for a new user. When the real-time access call data is not for a new user, obtain historical connection data, evaluate the connection priority based on the historical connection data, and perform call access screening based on the connection priority evaluation results. That is to say, if the real-time access call data is not for a new user, obtain the user's historical connection data, evaluate the connection priority of the staff based on the historical connection data. If the historically connected staff often connects the user and has a high score, then the staff can continue to be arranged to connect. By evaluating the connection priority based on the historical connection data, and then performing call access screening based on the connection priority evaluation results, suitable staff can be screened out for the user to handle business, thereby improving the user's business handling efficiency.

[0071] Example 2

[0072] Based on the same inventive concept as the omni-channel business intelligent monitoring and analysis method in the above embodiment, Figure 4 As shown, the present application also provides an omni-channel business intelligent monitoring and analysis system, the system comprising:

[0073] The data collection module 11 is used to collect and obtain the staff's historical call data and user business data, wherein the historical call data and the user business data have a corresponding relationship;

[0074] A call content recognition module 12 is configured to perform call content recognition on the historical call data and generate staff call features based on the content recognition results;

[0075] A service execution efficiency evaluation module 13 is configured to evaluate service execution efficiency based on the call duration of the historical call data and the user service processing data, thereby generating service execution efficiency data for staff members;

[0076] A business processing database construction module 14 is used to construct a business processing database for the staff member based on the business execution efficiency data and the staff member's call characteristics;

[0077] A business matching module 15 is used to identify real-time access call data, match the real-time access call data with the business handling database according to the business type of the real-time access call data, and obtain a staff matching value;

[0078] A real-time call information acquisition module 16 is used to obtain the real-time call information of the staff;

[0079] The call access screening module 17 is used to perform call access screening of the real-time access call data according to the real-time call information and the staff matching value.

[0080] Furthermore, the system further comprises:

[0081] A cumulative call data acquisition module, which is used to obtain the cumulative call data of the staff member;

[0082] A call cycle division module, configured to divide the accumulated call data into multiple levels of call cycles to obtain a multi-level call cycle division result;

[0083] 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;

[0084] a weighted calculation module configured to perform weighted calculation on the multi-level call cycle division results using the preset weight distribution values, and obtain call access impact data based on the weighted calculation results;

[0085] An access call data screening module is used to perform call access screening of the real-time access call data based on the call access impact data, the real-time call information and the staff matching value.

[0086] Furthermore, the system further comprises:

[0087] A speech feature set construction module, wherein the speech feature set construction module is used to construct a speech feature set;

[0088] A speech matching module, the speech matching module is used to perform speech matching on the historical call data using the speech feature set to obtain a speech matching result;

[0089] A speech matching analysis module is used to perform speech matching analysis based on the speech matching results and the user's business processing data to obtain a speech matching degree analysis result;

[0090] A call feature acquisition module is used to obtain the staff member's call features based on the speech matching results and the speech matching degree analysis results.

[0091] Furthermore, the system further comprises:

[0092] A user basic information acquisition module, which is used to obtain the user basic information of the user;

[0093] A speech adaptation evaluation module, wherein the speech adaptation evaluation module is used to perform speech adaptation evaluation based on the user basic information and the speech matching result to obtain a speech adaptation evaluation result;

[0094] A speech adaptation evaluation result adding module is used to add the speech adaptation evaluation result to the staff call feature.

[0095] Furthermore, the system further comprises:

[0096] A business processing speed analysis module, which is used to perform business processing speed impact analysis based on the user basic information to obtain a speed impact value analysis result;

[0097] A business processing database correction module is used to compensate the business execution efficiency data according to the speed impact value analysis result, and correct the business processing database based on the compensation result.

[0098] Furthermore, the system further comprises:

[0099] A training optimization direction database construction module, wherein the training optimization direction database construction module is used to construct a training optimization direction database for staff based on the business processing database;

[0100] A continuous monitoring interval generation module, the continuous monitoring interval generation module is used to feed back the training optimization direction database to corresponding staff members and generate a continuous monitoring interval;

[0101] The optimization supervision evaluation feedback module is used to perform optimization supervision evaluation and feedback of staff through the continuous monitoring interval.

[0102] Furthermore, the system further comprises:

[0103] A new user access judgment module is used to judge whether the real-time access call data is a new user access;

[0104] A historical connection data acquisition module, wherein the historical connection data acquisition module is used to obtain historical connection data when the real-time access call data is not for access to a new user;

[0105] The connection priority evaluation module is used to evaluate the connection priority according to the historical connection data and perform call access screening according to the connection priority evaluation result.

[0106] The specific example of the omni-channel business intelligent monitoring and analysis method in the aforementioned embodiment 1 is also applicable to the omni-channel business intelligent monitoring and analysis system in this embodiment. Through the detailed description of the omni-channel business intelligent monitoring and analysis method, those skilled in the art can clearly understand the omni-channel business intelligent monitoring and analysis system in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0107] Example 3

[0108] Figure 5 is a schematic diagram according to the third embodiment of the present disclosure, as shown in Figure 5 As shown, the electronic device 800 in the present disclosure may include: a processor 801 and a memory 802 .

[0109] Memory 802 is used to store programs. Memory 802 may include volatile memory (volatile memory), such as random-access memory (RAM), such as static random-access memory (SRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc. Memory may also include non-volatile memory (non-volatile memory), such as flash memory. Memory 802 is used to store computer programs (such as applications, functional modules, etc. that implement the above-mentioned methods), computer instructions, etc. The above-mentioned computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802. Furthermore, the above-mentioned computer programs, computer instructions, data, etc. can be called by processor 801.

[0110] The aforementioned computer programs, computer instructions, etc. may be partitioned and stored in one or more memories 802 . Furthermore, the aforementioned computer programs, computer instructions, etc. may be called by the processor 801 .

[0111] The processor 801 is configured to execute the computer program stored in the memory 802 to implement the various steps in the method involved in the above embodiment.

[0112] For details, please refer to the relevant description in the previous method embodiment.

[0113] The processor 801 and the memory 802 may be independent structures or integrated structures. When the processor 801 and the memory 802 are independent structures, the memory 802 and the processor 801 may be coupled via a bus 803 .

[0114] The electronic device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0115] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0116] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0117] It should be understood that the various forms of the processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in a different order.

[0118] As long as the expected results of the technical solutions disclosed in this disclosure can be achieved, this document does not impose any limitations here.

[0119] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for intelligent monitoring and analysis of omni-channel business, characterized in that: The method comprises: Collecting and obtaining historical call data of staff members and business data of users, wherein the historical call data and the business data of users have a corresponding relationship; Performing call content recognition on the historical call data, and generating staff call features based on the content recognition results; Performing a business execution efficiency evaluation based on the call duration of the historical call data and the user's business handling data to generate business execution efficiency data for the staff; Building a business processing database for the staff member based on the business execution efficiency data and the staff member's call characteristics; Identify real-time access call data, match the business processing database according to the business type of the real-time access call data, and obtain a staff matching value; Get real-time call information of staff; Performing call access screening on the real-time access call data according to the real-time call information and the staff matching value; The performing call content recognition on the historical call data and generating staff call features based on the content recognition results further includes: Construct a set of speech feature sets; Perform speech matching on the historical call data using the speech feature set to obtain a speech matching result; Performing a speech matching analysis based on the speech matching result and the user's business handling data to obtain a speech matching degree analysis result; Obtaining the staff member's call characteristics according to the speech matching result and the speech matching degree analysis result; Obtaining basic user information of the user; Performing a speech adaptation evaluation based on the user basic information and the speech matching result to obtain a speech adaptation evaluation result; The speech adaptation evaluation result is added to the staff call characteristics.

2. The method according to claim 1, wherein The method further comprises: Get the staff's cumulative call data; Dividing the accumulated call data according to the multi-level call cycle to obtain a multi-level call cycle division result; Obtaining a preset weight distribution value of the multi-level call cycle division result; Performing weighted calculation on the multi-level call cycle division results using the preset weight distribution values, and obtaining call access impact data based on the weighted calculation results; The call access screening of the real-time access call data is performed using the call access impact data, the real-time call information and the staff matching value.

3. The method according to claim 1, wherein The method further comprises: Performing a business processing speed impact analysis based on the user basic information to obtain a speed impact value analysis result; 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.

4. The method according to claim 1, wherein The method further comprises: Building a staff training optimization direction database based on the business processing database; Feedback the training optimization direction database to corresponding staff and generate a continuous monitoring interval; Optimized supervision, evaluation and feedback of staff are carried out through the continuous monitoring interval.

5. The method according to claim 1, wherein The method further comprises: Determining whether the real-time access call data is for a new user; When the real-time access call data does not indicate access to a new user, obtaining historical connection data; A connection priority evaluation is performed based on the historical connection data, and call access screening is performed based on the connection priority evaluation result.

6. An omni-channel business intelligent monitoring and analysis system, the system comprising: A data collection module, wherein the data collection module is used to collect and obtain historical call data of staff members and user business data, wherein the historical call data and the user business data have a corresponding relationship; A call content recognition module, configured to perform call content recognition on the historical call data and generate staff call features based on the content recognition results; A service execution efficiency evaluation module, configured to evaluate service execution efficiency based on the call duration of the historical call data and the user service processing data, and generate service execution efficiency data for staff members; A business processing database construction module, the business processing database construction module is used to construct the business processing database of the staff member by using the business execution efficiency data and the staff member's call characteristics; A business matching module, which is used to identify real-time access call data, match the business processing database according to the business type of the real-time access call data, and obtain a staff matching value; A real-time call information acquisition module, which is used to obtain the real-time call information of the staff; a call access screening module, configured to perform call access screening on the real-time access call data based on the real-time call information and the staff matching value; The system further comprises: A speech feature set construction module, wherein the speech feature set construction module is used to construct a speech feature set; A speech matching module, the speech matching module is used to perform speech matching on the historical call data using the speech feature set to obtain a speech matching result; A speech matching analysis module is used to perform speech matching analysis based on the speech matching results and the user's business processing data to obtain a speech matching degree analysis result; A call feature acquisition module, configured to obtain the staff member's call features based on the speech matching result and the speech matching degree analysis result; A user basic information acquisition module, which is used to obtain the user basic information of the user; A speech adaptation evaluation module, wherein the speech adaptation evaluation module is used to perform speech adaptation evaluation based on the user basic information and the speech matching result to obtain a speech adaptation evaluation result; A speech adaptation evaluation result adding module is used to add the speech adaptation evaluation result to the staff call feature.

7. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

Images