User service data processing system based on call node

Abstract

The invention provides a user service data processing system based on call nodes, and relates to the technical field of user service data processing. The system comprises a user value division module, a node service data receiving module, a node service data processing module, a node service data analysis module, a node service data evaluation module and a node service processing optimization module. Calculating to obtain a call service value analysis index and a call connection quality evaluation index of each node, obtaining a business service response strategy coefficient of each node, constructing user value characteristics based on call data of users, dividing high-value users and low-frequency call users, performing user optimization processing based on the business service response strategy coefficient, and obtaining a call service value analysis index and a call connection quality evaluation index of each node. The stability of the whole calling system is ensured by evaluating the service processing capacity of each node, and the existing calling node response system is optimized by utilizing an advanced data analysis technology, so that the service requirement of a user can be ensured to be met.

Description

Technical Field

[0001] The present application relates to the technical field of user service data processing, and in particular to a user service data processing system based on a call node. Background Art

[0002] With the continuous development and popularization of Internet technology, more and more businesses are transferred to the Internet. In this context, artificial intelligence technology plays an increasingly important role in processing user business data. By using artificial intelligence technology to process user business data, call nodes can understand and respond to human language, realizing more natural and intelligent human-computer interaction.

[0003] The user business data processing technology of call nodes plays a vital role in mobile operations. By collecting and analyzing user call data in real time, it can gain insights into user behaviors, needs and preferences, thereby providing data support for the development and promotion of new products. It can not only enhance the personalized services of call centers, but also bring more business opportunities and market competitiveness to the company.

[0004] However, in actual use, it still has some shortcomings. For example, the existing call node user business data processing method is mostly to automatically assign manual or artificial intelligence to solve the user's business needs, but it is unable to provide corresponding service support based on the user's value. As a result, the enterprise has standardization in serving users and lacks differentiation, which limits the enterprise's ability to meet specific user needs.

[0005] Existing user business data processing methods lack the ability to deeply explore the value generated by user call data, and to conduct real-time monitoring and early warning of the service process. Based on the data analysis results, they provide data support for business decisions, help optimize resource allocation, improve service processes, and further improve operational efficiency. Summary of the invention

[0006] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0007] To this end, the first objective of the present application is to propose a user service data processing system based on a call node.

[0008] A second object of the present application is to provide a computer-readable storage medium.

[0009] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a user service data processing system based on a call node, including:

[0010] User value classification module: used to obtain the user's call data, where the user's call data includes network age, monthly consumption amount, and monthly call volume. Based on the user's call data, the user value characteristics are constructed, and high-value users and low-frequency call users are classified and numbered.

[0011] Node service data receiving module: used to collect call data of each node, the node service data receiving module includes a node service data receiving unit and a node connection data receiving unit, and the call data of each node includes node service data and node connection data;

[0012] Node service data processing module: used to receive the call data of each node transmitted by the node service data receiving module, calculate the call service value analysis index of each node according to the node service data receiving unit, and calculate the call connection quality evaluation index of each node according to the node connection data receiving unit;

[0013] Node business data analysis module: used to receive the call service value analysis index and call connection quality evaluation index of each node transmitted by the node business data processing module, and calculate the business service response strategy coefficient of each node;

[0014] Node business data evaluation module: used to obtain the business service response strategy coefficient of each node, compare it with the preset business service response strategy coefficient, and process it;

[0015] Node business processing optimization module: used to receive the business service answering strategy coefficient of each node, obtain the high-value user optimization processing coefficient and the low-frequency call user optimization processing coefficient based on the business service answering strategy coefficient of each node, and perform user optimization processing.

[0016] The user value classification module includes:

[0017] The data acquisition module is used to obtain the user's network age, monthly consumption amount, and monthly call volume, which are marked as hn i ,hm i ,hl i , i represents the number of the i-th user;

[0018] The average calculation module is used to calculate the user's average network age, average monthly consumption amount, and average monthly call volume, which are Δhn, Δhm, and Δhl respectively;

[0019] The feature calculation module is used by the formula: Get the user value characteristics, where S i Represented as the value feature of the i-th user, hn i is the Internet age of the ith user, Δhn is the average Internet age, and hm is iis the monthly consumption amount of the i-th user, Δhm is the average monthly consumption amount, and hl i It is represented as the monthly call volume of the ith user, Δhl is represented as the average monthly call volume, ε1, ε2, ε3 are the weight factors of the user's network age, monthly consumption amount, and monthly call volume, respectively;

[0020] A comparison module is used to obtain a user value feature and compare it with a preset user value feature. If the user value feature is greater than the preset user value feature, the user is classified as a high-value user; if the user value feature is less than the preset user value feature, the user is classified as a low-frequency calling user;

[0021] The numbering module is used to renumber high-value users and renumber low-frequency calling users.

[0022] The node service data receiving module includes:

[0023] The node service data receiving unit is used to collect the number of business transactions per day, the daily product transaction amount, and the daily user access time of each node, which are marked as fb u 、fj u , ft u , u represents the number of the u-th node;

[0024] The node connection data receiving unit is used to collect the daily average data transmission rate, daily average number of access users, and daily average number of access interruptions of each node, which are marked as lv u ,ly u ,ld u , u represents the number of the u-th node.

[0025] The calculation formula of the call service value analysis index is:

[0026] where α u It is represented as the call service value analysis index of the u-th node, fb u It represents the number of business transactions per day at the u-th node, fj u It is represented by the daily product transaction amount of the u-th node, ft u It is represented as the user access duration of the u-th node per day, ft0 is represented as the reference user access duration per day, and e is represented as a natural constant.

[0027] The calculation formula of the call connection quality evaluation index is:

[0028] where β u Expressed as the call connection quality evaluation index of the u-th node, lv u Expressed as the daily average data transmission rate of the u-th node, lyu It is expressed as the average number of users accessing the u-th node per day, ld u It is expressed as the average number of access interruptions per day of the u-th node.

[0029] The calculation formula of the business service response strategy coefficient is:

[0030] θ u =α u +β u , where θ u It is represented as the business service response strategy coefficient of the u-th node, α u It is represented as the call service value analysis index of the u-th node, β u It is represented as the call connection quality evaluation index of the u-th node.

[0031] The node service data evaluation module includes:

[0032] The coefficient comparison module is used to obtain the business service response strategy coefficient of each node and compare it with the preset business service response strategy coefficient; if the business service response strategy coefficient of a node is greater than the preset business service response strategy coefficient, the node business processing optimization module is executed; if the business service response strategy coefficient of a node is less than the preset business service response strategy coefficient, the node business data receiving module is repeated.

[0033] The node service processing optimization module includes:

[0034] High-value user optimization processing module, used to extract the user value features corresponding to high-value users, marked as S g , g represents the number of the g-th high-value user;

[0035] By the formula: The mean value of the user value characteristics of high-value users, ΔS, is obtained by the formula The standard deviation σ of the user value characteristics of high-value users is obtained, where m represents the number of high-value users;

[0036] The calculation formula for analyzing the high-value user optimization processing coefficient is:

[0037] in is the high-value user optimization processing coefficient, n is the number of users, σ is the standard deviation of the user value characteristics of high-value users, and θ u It is represented as the business service response strategy coefficient of the u-th node, θ 预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes;

[0038] The high-value user optimization processing coefficient is compared with the business service answering strategy coefficient of each node, and the node number whose business service answering strategy coefficient is greater than the high-value user optimization processing coefficient is selected and set as the high-value user call priority answering node.

[0039] The node service processing optimization module includes:

[0040] The low-frequency calling user optimization processing module is used to extract the user value features corresponding to the low-frequency calling users, marked as S v , v represents the number of the vth high-value user;

[0041] Extract the maximum and minimum values ​​of the user value features corresponding to low-frequency calling users, marked as S max , S min ;

[0042] The calculation formula for analyzing the optimization processing coefficient of low-frequency call users is:

[0043] Where ω represents the optimization processing coefficient for low-frequency calling users, d represents the number of low-frequency calling users, S max It is represented as the maximum value of the user value feature of low-frequency calling users, S min It is represented by the minimum value of the user value feature of low-frequency calling users, S v Represented as the user value feature of the vth high-value user, θ u It is represented as the business service response strategy coefficient of the u-th node, θ 预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes;

[0044] The low-frequency calling user optimization processing coefficient is compared with the business service answering strategy coefficient of each node, and the node number whose business service answering strategy coefficient is greater than the low-frequency calling user optimization processing coefficient is selected and set as the low-frequency calling user call priority answering node.

[0045] To achieve the above-mentioned purpose, a second aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0046] The memory stores computer-executable instructions;

[0047] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0048] To achieve the above-mentioned purpose, the third aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0049] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer program product, which implements any method in the first aspect when executed by a processor.

[0050] The user service data processing method, device, electronic device and storage medium based on the call node provided in the present application have the following beneficial effects:

[0051] Build user value features based on user call data, divide high-value users and low-frequency call users, receive the node number of the execution node business processing optimization module, obtain the high-value user optimization processing coefficient and the low-frequency call user optimization processing coefficient based on the business service answering strategy coefficient of each node, and perform user optimization processing. This realizes the use of advanced data analysis technology to deeply understand user needs and behaviors, and then accurately divide high-value users and low-frequency call users, optimize the existing call node answering system, and ensure that the business needs of different users can be quickly and accurately resolved;

[0052] By collecting the call data of each node, the call service value analysis index of each node is calculated according to the node service data receiving unit, and the call connection quality evaluation index of each node is calculated according to the node connection data receiving unit. The business service answering strategy coefficient of each node is further obtained and compared with the preset business service answering strategy coefficient. If the business service answering strategy coefficient of a node is greater than the preset business service answering strategy coefficient, it indicates that the call node has high business processing capability for users, and the node business processing optimization module is executed. If the business service answering strategy coefficient of a node is less than the preset business service answering strategy coefficient, it indicates that the call node has low business processing efficiency for users, and the node business data receiving module should be repeated, which is conducive to a comprehensive and accurate evaluation of the business processing capability of each node. For nodes with high business capabilities, the service efficiency and quality of the node can be further improved to ensure that users can have high-quality and efficient services. By timely discovering and processing inefficient nodes, the stability of the entire call system is ensured, thereby achieving continuous improvement in service quality.

[0053] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0055] Figure 1 A schematic diagram of the structure of a user service data processing system based on a call node provided in an embodiment of the present application;

[0056] Figure 2 A schematic diagram of the structure of a node service data receiving module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0058] The embodiment of the present application provides a user service data processing system based on a call node, Figure 1 This is a schematic diagram of the structure of a user service data processing system based on a call node provided in an embodiment of the present application. Figure 1 As shown, the system includes: a user value division module 10, a node business data receiving module 20, a node business data processing module 30, a node business data analysis module 40, a node business data evaluation module 50, and a node business processing optimization module 60.

[0059] The user value division module is connected to the node business data receiving module, the node business data receiving module is connected to the node business data processing module, the node business data processing module is connected to the node business data analysis module, the node business data analysis module is connected to the node business data evaluation module, and the node business data evaluation module is connected to the node business processing optimization module or the node business data receiving module.

[0060] The user value classification module is used to obtain the user's call data, wherein the user's call data includes network age, monthly consumption amount, and monthly call volume, construct user value features based on the user's call data, and classify high-value users and low-frequency call users, and number the high-value users and low-frequency call users.

[0061] In a possible embodiment, the specific division method of the user value division module is:

[0062] Step S01: Obtain the user's network age, monthly consumption amount, and monthly call volume, marked as hn i ,hm i ,hl i, where i = 1, 2, ... n, i represents the number of the i-th user;

[0063] Step S02: Calculate the average network age, average monthly consumption amount, and average monthly call volume of users, respectively. i ,hm i ,hl i Labeled as Δhn, Δhm, Δhl;

[0064] Step S03: According to the formula: Get the user value characteristics, where S i Represented as the value feature of the i-th user, hn i is the Internet age of the ith user, Δhn is the average Internet age, and hm is i is the monthly consumption amount of the i-th user, Δhm is the average monthly consumption amount, and hl i It is represented as the monthly call volume of the ith user, Δhl is represented as the average monthly call volume, ε1, ε2, ε3 are the weight factors of the user's network age, monthly consumption amount, and monthly call volume, respectively;

[0065] Step S04: obtaining a user value feature and comparing it with a preset user value feature. If the user value feature is greater than the preset user value feature, the user is classified as a high-value user. If the user value feature is less than the preset user value feature, the user is classified as a low-frequency calling user.

[0066] Step S05: renumber high-value users to 1, 2, ..., m, and renumber low-frequency calling users to 1, 2, ..., v.

[0067] Figure 2 A schematic diagram of a node service data receiving module structure provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the node service data receiving module is used to collect call data of each node, and the node service data receiving module includes a node service data receiving unit and a node connection data receiving unit. The call data of each node includes node service data and node connection data;

[0068] In a possible embodiment, the node service data receiving module is specifically:

[0069] Node service data receiving unit: collects the number of business transactions per day, the daily product transaction amount, and the daily user access time of each node, marked as fb u 、fj u , ft u , where u = 1, 2, ..., q, and u represents the number of the u-th node;

[0070] Node connection data receiving unit: collects the daily average data transmission rate, daily average number of access users, and daily average number of access interruptions of each node, marked as lv u ,ly u ,ld u , where u = 1, 2, ..., q, and u represents the number of the u-th node.

[0071] The node service data processing module is used to receive the call data of each node transmitted by the node service data receiving module, calculate the call service value analysis index of each node according to the node service data receiving unit, and calculate the call connection quality evaluation index of each node according to the node connection data receiving unit.

[0072] In a possible embodiment, the calculation formula of the call service value analysis index is:

[0073] where α u It is represented as the call service value analysis index of the u-th node, fb u It represents the number of business transactions per day at the u-th node, fj u It is represented by the daily product transaction amount of the u-th node, ft u It is represented as the user access duration of the u-th node per day, ft0 is represented as the reference user access duration per day, and e is represented as a natural constant.

[0074] In a possible embodiment, the calculation formula of the call connection quality evaluation index is:

[0075] where β u Expressed as the call connection quality evaluation index of the u-th node, lv u Expressed as the daily average data transmission rate of the u-th node, ly u It is expressed as the average number of users accessing the u-th node per day, ld u It is expressed as the average number of access interruptions per day of the u-th node.

[0076] The node service data analysis module is used to receive the call service value analysis index and call connection quality evaluation index of each node transmitted by the node service data processing module, and calculate the service response strategy coefficient of each node.

[0077] In a possible embodiment, the calculation formula of the business service response strategy coefficient is:

[0078] θ u =α u +β u , where θ u It is represented as the business service response strategy coefficient of the u-th node, αu It is represented as the call service value analysis index of the u-th node, β u It is represented as the call connection quality evaluation index of the u-th node.

[0079] The node service data evaluation module is used to obtain the service response strategy coefficient of each node, compare it with the preset service response strategy coefficient, and process it.

[0080] In a possible embodiment, the specific evaluation method of the node service data evaluation module is:

[0081] Obtain the business service response strategy coefficient of each node and compare it with the preset business service response strategy coefficient. If the business service response strategy coefficient of a node is greater than the preset business service response strategy coefficient, it indicates that the call node has a high business processing capability for the user, and the node business processing optimization module is executed. If the business service response strategy coefficient of a node is less than the preset business service response strategy coefficient, it indicates that the call node has low business processing efficiency for the user, and the node business data receiving module should be repeated.

[0082] The node service processing optimization module is used to receive the service answering strategy coefficient of each node, obtain the high-value user optimization processing coefficient and the low-frequency calling user optimization processing coefficient based on the service answering strategy coefficient of each node, and perform user optimization processing.

[0083] In a possible embodiment, the specific optimization method of the node service processing optimization module is:

[0084] High-value user optimization processing is as follows:

[0085] Step S001: Extract the user value features corresponding to high-value users, marked as S g , where g = 1, 2, ..., m, and g represents the number of the g-th high-value user;

[0086] Step S002: According to the formula: The mean value of the user value characteristics of high-value users, ΔS, is obtained by the formula The standard deviation σ of the user value characteristics of high-value users is obtained, where m represents the number of high-value users;

[0087] Step S003: Analyze the calculation formula of the high-value user optimization processing coefficient:

[0088] in is the high-value user optimization processing coefficient, n is the number of users, σ is the standard deviation of the user value characteristics of high-value users, and θ u It is represented as the business service response strategy coefficient of the u-th node, θ预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes;

[0089] Step S004: Compare the high-value user optimization processing coefficient with the business service answering strategy coefficient of each node, select the node number whose business service answering strategy coefficient is greater than the high-value user optimization processing coefficient, and set it as the high-value user call priority answering node;

[0090] The optimization process for low-frequency calling users is as follows:

[0091] Step S005: Extract the user value features corresponding to the low-frequency calling users, marked as S v , where v = 1, 2, ..., d, and v represents the number of the vth high-value user;

[0092] Step S006: Extract the maximum and minimum values ​​of the user value features corresponding to the low-frequency calling users, and mark them as S max , S min ;

[0093] Step S007: Analyze the calculation formula of the optimization processing coefficient of low-frequency calling users:

[0094] Where ω represents the optimization processing coefficient for low-frequency calling users, d represents the number of low-frequency calling users, S max It is represented as the maximum value of the user value feature of low-frequency calling users, S min It is represented by the minimum value of the user value feature of low-frequency calling users, S v Represented as the user value feature of the vth high-value user, θ u It is represented as the business service response strategy coefficient of the u-th node, θ 预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes;

[0095] Step S008: Compare the low-frequency calling user optimization processing coefficient with the business service answering strategy coefficient of each node, select the node number whose business service answering strategy coefficient is greater than the low-frequency calling user optimization processing coefficient, and set it as the low-frequency calling user call priority answering node.

[0096] In this embodiment, it should be specifically explained that when a high-value user conflicts with a low-frequency calling user, the service demand of the high-value user is answered first.

[0097] In the present embodiment, it should be specifically explained that the present invention constructs user value features based on the user's call data, and divides high-value users and low-frequency calling users, obtains high-value user optimization processing coefficients and low-frequency calling user optimization processing coefficients based on the business service answering strategy coefficients of each node by receiving the node number of the execution node business processing optimization module, and performs user optimization processing, thereby realizing the use of advanced data analysis technology to deeply understand user needs and behaviors, and then accurately divide high-value users and low-frequency calling users, optimize the existing call node answering system, and ensure that different user business needs can be solved quickly and accurately.

[0098] The present invention collects call data from each node, calculates a call service value analysis index of each node according to a node service data receiving unit, calculates a call connection quality evaluation index of each node according to a node connection data receiving unit, and further obtains a business service answering strategy coefficient of each node, which is compared with a preset business service answering strategy coefficient. If the business service answering strategy coefficient of a certain node is greater than the preset business service answering strategy coefficient, it indicates that the call node has a high business processing capability for the user, and the node business processing optimization module is executed. If the business service answering strategy coefficient of a certain node is less than the preset business service answering strategy coefficient, it indicates that the call node has a low business processing efficiency for the user, and the node business data receiving module should be repeated, which is conducive to a comprehensive and accurate evaluation of the business processing capability of each node. For nodes with high business capabilities, the service efficiency and quality of the node can be further improved to ensure that users can have high-quality and efficient services. By timely discovering and processing inefficient nodes, the stability of the entire call system is ensured, thereby achieving continuous improvement in service quality.

[0099] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the system provided by the above embodiments.

[0100] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the system provided by the above embodiments.

[0101] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the system provided by the above embodiments when executed by a processor.

[0102] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0103] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0104] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0105] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0106] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0107] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0109] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0110] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0111] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0112] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A user service data processing system based on a call node, characterized in that: include: User value classification module: used to obtain the user's call data, where the user's call data includes network age, monthly consumption amount, and monthly call volume. Based on the user's call data, the user value characteristics are constructed, and high-value users and low-frequency call users are classified and numbered. Node service data receiving module: used to collect call data of each node, the node service data receiving module includes a node service data receiving unit and a node connection data receiving unit, and the call data of each node includes node service data and node connection data; Node service data processing module: used to receive the call data of each node transmitted by the node service data receiving module, calculate the call service value analysis index of each node according to the node service data receiving unit, and calculate the call connection quality evaluation index of each node according to the node connection data receiving unit; Node business data analysis module: used to receive the call service value analysis index and call connection quality evaluation index of each node transmitted by the node business data processing module, and calculate the business service response strategy coefficient of each node; Node business data evaluation module: used to obtain the business service response strategy coefficient of each node, compare it with the preset business service response strategy coefficient, and process it; Node business processing optimization module: used to receive the business service answering strategy coefficient of each node, obtain the high-value user optimization processing coefficient and the low-frequency call user optimization processing coefficient based on the business service answering strategy coefficient of each node, and perform user optimization processing.

2. The system according to claim 1, characterized in that The user value classification module includes: The data acquisition module is used to obtain the user's network age, monthly consumption amount, and monthly call volume, which are marked as hn i ,hm i ,hl i , i represents the number of the i-th user; The average calculation module is used to calculate the user's average network age, average monthly consumption amount, and average monthly call volume, which are Δhn, Δhm, and Δhl respectively; The feature calculation module is used by the formula: Get the user value characteristics, where S i Represented as the value feature of the i-th user, hn i is the Internet age of the ith user, Δhn is the average Internet age, and hm is i is the monthly consumption amount of the i-th user, Δhm is the average monthly consumption amount, and hl i It is represented as the monthly call volume of the ith user, Δhl is represented as the average monthly call volume, ε1, ε2, ε3 are the weight factors of the user's network age, monthly consumption amount, and monthly call volume, respectively; A comparison module is used to obtain a user value feature and compare it with a preset user value feature. If the user value feature is greater than the preset user value feature, the user is classified as a high-value user; if the user value feature is less than the preset user value feature, the user is classified as a low-frequency calling user; The numbering module is used to renumber high-value users and renumber low-frequency calling users.

3. The system according to claim 2, characterized in that The node service data receiving module includes: The node service data receiving unit is used to collect the number of business transactions per day, the daily product transaction amount, and the daily user access time of each node, which are marked as fb u 、fj u , ft u , u represents the number of the u-th node; The node connection data receiving unit is used to collect the daily average data transmission rate, daily average number of access users, and daily average number of access interruptions of each node, which are marked as lv u ,ly u ,ld u , u represents the number of the u-th node.

4. The system according to claim 3, characterized in that The calculation formula of the call service value analysis index is: where α u It is represented as the call service value analysis index of the u-th node, fb u It represents the number of business transactions per day at the u-th node, fj u It is represented by the daily product transaction amount of the u-th node, ft u It is represented as the user access duration of the u-th node per day, ft0 is represented as the reference user access duration per day, and e is represented as a natural constant.

5. The system according to claim 4, characterized in that The calculation formula of the call connection quality evaluation index is: where β u Expressed as the call connection quality evaluation index of the u-th node, lv u Expressed as the daily average data transmission rate of the u-th node, ly u It is expressed as the average number of users accessing the u-th node per day, ld u It is expressed as the average number of access interruptions per day of the u-th node.

6. The system according to claim 5, characterized in that The calculation formula of the business service response strategy coefficient is: θ u =α u +β u , where θ u It is represented as the business service response strategy coefficient of the u-th node, α u It is represented as the call service value analysis index of the u-th node, β u It is represented as the call connection quality evaluation index of the u-th node.

7. The system according to claim 6, characterized in that The node service data evaluation module includes: The coefficient comparison module is used to obtain the business service response strategy coefficient of each node and compare it with the preset business service response strategy coefficient; if the business service response strategy coefficient of a node is greater than the preset business service response strategy coefficient, the node business processing optimization module is executed; if the business service response strategy coefficient of a node is less than the preset business service response strategy coefficient, the node business data receiving module is repeated.

8. The system according to claim 7, characterized in that The node service processing optimization module includes: High-value user optimization processing module, used to extract the user value features corresponding to high-value users, marked as S g , g represents the number of the g-th high-value user; By the formula: The mean value of the user value characteristics of high-value users, ΔS, is obtained by the formula The standard deviation σ of the user value characteristics of high-value users is obtained, where m represents the number of high-value users; The calculation formula for analyzing the high-value user optimization processing coefficient is: in is the high-value user optimization processing coefficient, n is the number of users, σ is the standard deviation of the user value characteristics of high-value users, and θ u It is represented as the business service response strategy coefficient of the u-th node, θ 预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes; The high-value user optimization processing coefficient is compared with the business service answering strategy coefficient of each node, and the node number whose business service answering strategy coefficient is greater than the high-value user optimization processing coefficient is selected and set as the high-value user call priority answering node.

9. The system according to claim 8, characterized in that The node service processing optimization module includes: The low-frequency calling user optimization processing module is used to extract the user value features corresponding to the low-frequency calling users, marked as S v , v represents the number of the vth high-value user; Extract the maximum and minimum values ​​of the user value features corresponding to low-frequency calling users, marked as S max , S uin ; The calculation formula for analyzing the optimization processing coefficient of low-frequency call users is: Where ω represents the optimization processing coefficient for low-frequency calling users, d represents the number of low-frequency calling users, S uax It is represented as the maximum value of the user value feature of low-frequency calling users, S uin It is represented by the minimum value of the user value feature of low-frequency calling users, S v Represented as the user value feature of the vth high-value user, θ u It is represented as the business service response strategy coefficient of the u-th node, θ 预 It is represented by the preset business service response strategy coefficient, and q is represented by the number of nodes; The low-frequency calling user optimization processing coefficient is compared with the business service answering strategy coefficient of each node, and the node number whose business service answering strategy coefficient is greater than the low-frequency calling user optimization processing coefficient is selected and set as the low-frequency calling user call priority answering node.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the system according to any one of claims 1 to 9 when executed by a processor.

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