Extended quantitative service classification method and system

Abstract

The invention discloses a quantitative business classification method and system supporting extension, and the method is characterized in that the method comprises the following steps: collecting customer complaints and suggestions, and carrying out the semantic extraction of the customer complaints and suggestions; performing key point identification and analysis according to the extracted semantic information to obtain key semantics; performing intelligent key point analysis on the key semantics to realize customer complaint classification; and based on the customer complaint classification, automatically assigning current customer complaints and suggestions to a business unit executing customer complaint processing. The method has good diversity, comprehensiveness, complementarity and robustness.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and more specifically, to a quantitative business classification method and system supporting expansion. Background Art

[0002] Power supply service complaints refer to power customers within the power company's business scope expressing dissatisfaction with the damage to their rights and interests caused by the power supply company's responsibility in terms of power supply services, business operations, power outages, power supply quality, and grid construction, and demanding that their rights and interests be protected.

[0003] Customer complaints mainly include service complaints, business complaints, power outage complaints, power supply quality complaints, power grid construction complaints and other types. In service complaints, users complain about the irregular service behavior of power supply company employees and the blocked service channels. In business complaints, power supply companies have problems such as overtime, negligence, and errors in business expansion and registration, electricity consumption changes, meter reading and collection, electricity charges and prices, and energy metering. Therefore, it is necessary to make business improvements based on the content of the complaints. In power outage complaints, there are service deviations in power outage information announcements, power outage plan execution, emergency repair quality, and the opening of value-added services. Therefore, it is necessary to improve the service content based on complaints. In addition, in power supply quality complaints, customers may raise opinions on power supply quality problems such as voltage deviation, frequency deviation, voltage imbalance, voltage fluctuation or flicker in the transmitted electricity. Therefore, in order to optimize the power supply capacity in response to power supply quality complaints, it is necessary to make substantial improvements to transmission parameters such as precise voltage deviation and frequency deviation.

[0004] At present, power companies mainly provide customer service through WeChat business halls, online business halls, 95598 call hotlines, physical business halls, etc. Customer service for complaints mainly relies on manual customer service for classification and dispatch, and the power industry has not yet truly realized intelligent power customer service.

[0005] In the traditional complaint handling process, operators usually answer user complaints over the phone, then compile them into text messages for recording, and then assign them to relevant departments and personnel for processing. However, due to the wide variety of complaints and the involvement of many departments and personnel, qualified operators often need a lot of training before they can take up their posts, and they are unable to handle emergency issues during special hours at night. Therefore, there is an urgent need for automated dispatching applications to improve the efficiency of customer complaint handling.

[0006] Secondly, the categories of complaint cases are stored and classified in the database in a tree-like hierarchical structure. When accepting a complaint, it is necessary to first correctly classify it at each level of the tree, and finally accurately trace the complaint. If a category in an upper layer is classified incorrectly, the classification of the next level will also be wrong, which leads to the accumulation of error rates layer by layer.

[0007] On the other hand, with the upgrade of the customer service platform, various forms of complaints such as voice, text, pictures and videos can be obtained during the complaint service process. The complaint contents of various modes are interrelated and complementary, but it is difficult to provide complete complaint information depending on the source and access method of the complaint. These problems lead to the accumulation of classification model error rates.

[0008] When the error rate of the classification model is high, the automatic real-time dispatching service cannot be implemented accurately and effectively.

[0009] In view of the above problems, there is an urgent need for a quantitative business classification method and system that supports expansion. Summary of the invention

[0010] In order to address the deficiencies in the prior art, the present invention provides a quantitative business classification method and system that supports expansion. The method uses an artificial intelligence model to collect and summarize customer complaints and suggestions, extracts text from complaint voice, text, and video, and then uses semantic understanding technology to obtain the main transactions and specific customer service content in the customer service information to achieve quantitative business classification.

[0011] The present invention adopts the following technical solution.

[0012] A first aspect of the present invention relates to a quantitative business classification method that supports expansion, the method comprising the following steps: collecting customer complaints and suggestions, and performing semantic extraction on the customer complaints and suggestions; performing key point identification and analysis based on the extracted semantic information to obtain key semantics; performing intelligent key point analysis on the key semantics to achieve customer complaint classification; based on the customer complaint classification, automatically assigning current customer complaints and suggestions to the business unit that executes customer complaint processing.

[0013] Preferably, customer complaints and suggestions include image information, video information, sound information and text information; semantic extraction is performed on the image information, video information, sound information and text information respectively, and the extracted semantic information is mapped into a unified feature space.

[0014] Preferably, semantic extraction is performed on the image information, video information, sound information and text information respectively, including: preprocessing the video information and image information through a computer vision algorithm; preprocessing the sound information through a speech recognition algorithm; and semantic understanding and keyword extraction of the text information, preprocessed image information, video information, and sound information through a natural language processing algorithm.

[0015] Preferably, key point identification and analysis are performed based on the extracted semantic information to obtain key semantics, including: constructing a semantic data set based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and using the semantic data set as the key semantics.

[0016] Preferably, the key semantics are intelligently analyzed to achieve customer complaint classification, including: inputting the semantic data set into a deep neural network, and using the deep neural network to convert the semantic data set into a semantic data set matrix; the semantic data set matrix includes common semantic features and distinguishing semantic features.

[0017] Preferably, intelligent key point analysis is performed on the key semantics to achieve customer complaint classification, including: achieving classification of the key semantics based on the common semantic features and the distinguishing semantic features.

[0018] Preferably, based on the customer complaint classification, current customer complaints and suggestions are automatically assigned to the business unit that performs customer complaint handling, including: performing key semantic extraction and intelligent key point analysis on the current customer complaints and suggestions, and realizing automatic assignment of business units based on the positions of the analyzed intelligent key points.

[0019] The second aspect of the present invention relates to a quantitative business classification system that supports expansion using the method in the first aspect of the present invention, an extraction module for collecting customer complaints and suggestions, and performing semantic extraction on the customer complaints and suggestions; an identification module for performing key point identification and analysis based on the extracted semantic information to obtain key semantics; a classification module for performing intelligent key point analysis on the key semantics to achieve customer complaint classification; and a dispatch module for automatically dispatching current customer complaints and suggestions to business units that perform customer complaint processing based on the customer complaint classification.

[0020] Preferably, in the extraction module, the customer complaints and suggestions include image information, video information, sound information and text information; semantic extraction is performed on the image information, video information, sound information and text information respectively, and the extracted semantic information is mapped into a unified feature space.

[0021] Preferably, the extraction module is used to perform semantic extraction on the image information, video information, sound information and text information respectively, including: preprocessing the video information and image information through a computer vision algorithm; preprocessing the sound information through a speech recognition algorithm; and performing semantic understanding and keyword extraction on the text information, preprocessed image information, video information, and sound information through a natural language processing algorithm.

[0022] Preferably, the recognition module is used to perform key point recognition and analysis based on the extracted semantic information to obtain key semantics, including: constructing a semantic data set based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and using the semantic data set as the key semantics.

[0023] Preferably, the classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: inputting the semantic data set into a deep neural network, and using the deep neural network to convert the semantic data set into a semantic data set matrix; the semantic data set matrix includes common semantic features and distinguishing semantic features.

[0024] Preferably, the classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: achieving classification of the key semantics based on the common semantic features and the distinguishing semantic features.

[0025] Preferably, the dispatching module is used to automatically dispatch current customer complaints and suggestions to business units that perform customer complaint handling based on the customer complaint classification, including: performing key semantic extraction and intelligent key point analysis on current customer complaints and suggestions, and realizing automatic dispatch of business units based on the positions of the analyzed intelligent key points.

[0026] A third aspect of the present invention relates to a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method in the first aspect of the present invention.

[0027] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method in the first aspect of the present invention.

[0028] The beneficial effect of the present invention is that, compared with the prior art, the present invention provides a quantitative business classification method and system that supports expansion, which uses an artificial intelligence model to collect and summarize customer complaints and suggestions, extracts text from complaint voice, text, and video, and then uses semantic understanding technology to obtain the main transaction and specific customer service content in customer service information to achieve quantitative business classification. The method has good diversity, comprehensiveness, complementarity, and robustness.

[0029] The beneficial effects of the present invention also include:

[0030] 1. The method can process multiple input modes at the same time, which enables it to obtain more information, provide users with answers more accurately, and provide users with answers more comprehensively. Each information mode can complement each other, which can make up for the shortcomings of each mode and improve the answer quality of the intelligent customer service system.

[0031] 2. The method can still correctly analyze and output information even when multiple types and formats of information are input for the same problem.

[0032] 3. The method supports different ways to synchronously process complaint pictures, images, videos, sounds, texts and other information, and maps them to a unified feature space. Finally, the method ensures that each system handles customer complaints and suggestions accordingly through intelligent analysis. The core of the present invention is to integrate and process various types of data, accurately understand and analyze information from different sources, and effectively combine them to obtain more accurate and comprehensive analysis results.

[0033] 4. In the process of analyzing video information, the method also supports the processing of image data. Combined with natural language understanding, the method can also analyze the essential content of images and videos, and extract the actual content of the customer's complaint from the complaint images and complaint videos.

[0034] 5. The method supports processing unstructured data content through methods such as convolutional neural networks and recursive neural networks, and extracting useful features, so as to achieve synchronous analysis of information of different dimensions and provide a more comprehensive and in-depth understanding of multi-dimensional complaint information. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of a flow chart of a quantitative service classification method supporting expansion according to the present invention;

[0036] Figure 2 The present invention is a schematic diagram of the module structure of a quantitative business classification system supporting expansion. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in the present invention are only embodiments of a part of the present invention, not all embodiments. Based on the spirit of the present invention, all other embodiments not recorded in the present invention obtained by ordinary technicians in this field according to the embodiments recorded in the present invention without making creative work should belong to the protection scope of the present invention.

[0038] Figure 1 The first aspect of the present invention relates to a method for classifying quantized services supporting extensions, and the method includes steps 1 to 4.

[0039] Step 1: Collect customer complaints and suggestions, and perform semantic extraction on the customer complaints and suggestions.

[0040] With the widespread application of multimedia technology and the enrichment of data description methods, a large amount of data content in different forms is widely available, and the description of a description object is jointly realized from multiple angles, in multiple languages, and in multiple visual forms. In the complaint information of the power system, the same complaint content may include multiple different forms such as audio, image, text, and video. Pictures and videos from different sources may have different pixel intensity, grayscale, and texture information. How to integrate various types of information and realize decision-making and prediction based on multi-modal data is the key issue considered in this invention.

[0041] To this end, the method collects complaint voice, text, image and video information through a variety of different complaint channels. And constructs a data set by using the different dimensions of the collected data and the data content. For example, information is collected from WeChat business halls, online business halls, 95598 call hotlines and physical business halls.

[0042] Preferably, customer complaints and suggestions include image information, video information, sound information and text information.

[0043] In one embodiment of the present invention, semantic extraction is performed on image information, video information, sound information and text information respectively, including: preprocessing the video information and image information through a computer vision algorithm; preprocessing the sound information through a speech recognition algorithm; and semantic understanding and keyword extraction of text information, preprocessed image information, video information, and sound information through a natural language processing algorithm.

[0044] Step 2: identify and analyze key points based on the extracted semantic information to obtain key semantics.

[0045] Obtaining key semantics from the extracted semantic information needs to be implemented through semantic information mapping. Specifically, the method constructs a semantic data set based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and uses the semantic data set as the key semantics.

[0046] Through the above method, the method can obtain a preliminary semantic understanding of the above multi-type information and extract keywords. Therefore, in order to achieve unified mapping, the method needs to construct a corresponding data set.

[0047] In the present invention, the data set is

[0048]

[0049] m is the dimension number of complaint voice, complaint text, complaint picture and complaint video,

[0050] is the feature matrix of complaint information under the current dimension m,

[0051] is the difference between the current dimension m and The matrix composed of other data instances of .

[0052] Among them, the complaint voice, text, picture and video are identified in the form of dimensions 1, 2, 3 and 4 respectively. The data content of different dimensions can represent the same complaint issues. Therefore, the data set includes 4 different data dimensions and multiple data instances belonging to each data dimension. Among them, the information of some data instances can be complete, including all the information of the four dimensions and the correlation between the information in each dimension. For this reason, the present invention records it as That is, the matrix composed of instances of the complete data instance in the current dimension, that is, the feature matrix mentioned above.

[0053] Through the above method, the method can effectively extract instances with multiple different dimensional information, thereby obtaining the distance between instances based on the emphasis of these instances, constructing the multidimensional space where the instances are located, ensuring the validity of the data set, and providing a basis for the subsequent type classification of data instances.

[0054] On the other hand, other multiple data instances may not be complete data instances, and the number of data instances in each dimension is also different, so they are recorded as L, that is, for the current dimension, the number of all data instances is C+Lm. The number of L1, L2, L3 and L4 is not equal.

[0055] In this process, the method will not abandon other incomplete data instance contents. By constructing the integration of instance information of different dimensions, it ensures that all complaint information is taken into account, thereby providing a model data basis for subsequent data classification.

[0056] Preferably, semantic extraction is performed on the image information, video information, sound information and text information respectively, and the extracted semantic information is mapped into a unified feature space.

[0057] For a complete data instance, its content in different dimensions is different, but it actually has the same semantic description information. Therefore, the present invention uses a deep neural network to extract the semantic description.

[0058] Specifically, a deep neural network is used to convert the X-dimensional dataset into a feature dataset matrix In the formula, and They are and The deep feature transformation matrix of .

[0059] The method can convert the dataset matrix into a matrix of deep feature representation. Then, the method decomposes the dataset matrix to extract the common information of multiple dimensions in the dataset matrix and the feature information of each dimension.

[0060] In one embodiment, complaint information of different dimensions is decomposed into a first decomposition matrix and a second decomposition matrix, including: the product of the feature data set matrix V and the first decomposition matrix and the second decomposition matrix is ​​equal. The first decomposition matrix W and the second decomposition matrix H can respectively characterize the data features of the original data set.

[0061] For example, H can be used to represent data because is the information content matrix of the complete data instance, so for different dimensions, have the same semantic information. Therefore, The potential meaning of the parameter H can be consistent in multiple dimensions.

[0062] Through the decomposed second decomposition matrix, the method extracts all the information of the most simplified and lossless complete instance without difference and defines it as the second decomposition matrix. The second decomposition matrix is ​​solved in an iterative manner to prevent local optimality and excessive errors in the solution results. These common information in multiple dimensions supports the construction of instance space and ensures spatial scalability, thereby providing redundancy for the multi-level classification process.

[0063] After obtaining the first decomposition matrix and the second decomposition matrix through the above method, the method can calculate the common semantic features, that is, key semantics, in complaint information of different dimensions.

[0064] Step 3: Perform intelligent key point analysis on key semantics to achieve customer complaint classification.

[0065] Preferably, intelligent key point analysis is performed on key semantics to achieve customer complaint classification, including: inputting the semantic data set into a deep neural network, and using the deep neural network to convert the semantic data set into a semantic data set matrix; the semantic data set matrix includes common semantic features and distinguishing semantic features.

[0066] In the present invention, the key semantic features under multiple different dimensions can ensure that the classification will not be inaccurate due to the additional features of the data in a certain dimension. Since the information of multiple dimensions restricts and cooperates with each other, the classification errors caused by the incompleteness of the data collection process are overcome, the effectiveness of the classification is greatly improved, and a semantic basis is provided for automatic dispatching.

[0067] In one embodiment, the key semantics are:

[0068]

[0069] In the formula, H C is the common feature matrix for all types of information,

[0070] They are different from the shared feature matrix H in different dimensions. C The matrix composed of other data instances of .

[0071] As mentioned above, the common semantic features here are actually the common features H of the parameter H in multiple dimensions. C , and separate features in each dimension By calculating the common semantic features, the method can obtain the basis for complaint classification. By inputting the common semantic features into the pre-constructed knowledge graph related to power business complaints, the corresponding business improvement methods for complaints should be obtained, so as to automatically dispatch orders in real time.

[0072] Through this method, semantic features under multiple different dimensions can ensure that classification will not be inaccurate due to the additional features of a certain dimension of data. Since the information of multiple dimensions constrains and cooperates with each other, it overcomes the classification errors caused by incompleteness in the data collection process, greatly improves the effectiveness of classification, and provides a semantic basis for automatic dispatching.

[0073] The objective function for calculating the above parameters is:

[0074]

[0075]

[0076] Where W m is the first decomposition matrix under the current dimension m,

[0077] Tr(·) is the trace of the matrix,

[0078] α m is the dimension parameter,

[0079] L m is a Laplace matrix.

[0080] The method solves the minimization target by constructing an objective function. The solved minimization target includes the parameter H to be solved. C and The value of .

[0081] The above objective function includes two aspects: the first term decomposition error and the second term error in the spatial query positioning process of the data instance. By minimizing the sum of the errors in the two aspects, it is fully ensured that the result of each iteration converges accurately enough.

[0082] In the present invention, H C and are all matrices greater than or equal to 0. According to the matrix compilation method, the information it carries, etc., the method can set the capacity of the matrix, that is, the number of rows and columns. The objective function also includes some constraints, such as W m It is also a matrix greater than or equal to 0, and the parameters can be pre-defined according to the actual situation. During multiple iterations of the deep learning network, the value of the parameter does not change.

[0083] Laplace matrix L m =D m -S m , where D m is a diagonal matrix, and the value of each element on the diagonal is S m The sum of each row or column in a matrix. S m is a similarity matrix, which is used to characterize the similarity between two data instances. For example, two complaint information are for the same power business, the problems are exactly the same or similar, and the customers who made the complaints are close to each other. In this case, the similarity between the two instances is high. If the locations, actual problems, business types, etc. of the two complaint information are different, the distance between the two instances is far.

[0084] Therefore, through the similarity matrix, the method can not only fully define the construction method of the initial data set matrix where the complaint information is located, but also consider various factors to ensure the accuracy of instance distance calculation.

[0085] By constructing the spatial relationship between any two complaint information, we can obtain accurate basis for complaint classification and ensure the accuracy of information classification results. Moreover, this classification method takes into account different information in multiple dimensions and will not cause low classification accuracy due to incomplete information in different dimensions, which greatly improves classification accuracy.

[0086] In one embodiment, the method uses the Euclidean distance as a basis to characterize the similarity between two instances. In the similarity matrix, the distance between any two instances p and q is an element in a matrix. Therefore, the number of rows and columns of the matrix is ​​equal and equal to the total number of all data instances in the data set.

[0087] With the above method as the goal, the method obtains the common semantic features in complaint information of different dimensions.

[0088] Step 4: Based on the customer complaint classification, the current customer complaints and suggestions are automatically assigned to the business unit that handles the customer complaint.

[0089] Preferably, key semantics extraction and intelligent key point analysis are performed on current customer complaints and suggestions, and automatic allocation of business units is achieved based on the positions of the analyzed intelligent key points.

[0090] In one embodiment, the method utilizes the output of a deep neural network to confirm business classification to achieve real-time dispatch of complaints.

[0091] It is easy to think that the way to confirm the business classification can be to learn the knowledge of the complaint information content and construct a knowledge graph. In the present invention, the current complaint is input into the deep neural network, and the business type is found and located through the pre-constructed knowledge graph.

[0092] In the knowledge graph, we can not only learn the business type of the complaint, but also learn the solution and technical solution for the complaint. By finding the corresponding node on the knowledge graph, the method can implement accurate dispatch and provide technical support.

[0093] When the automatic order dispatching system can associate the content of customer complaints and suggestions, fault type, customer location and other information with the subordinate unit providing specific technical support or operation and maintenance, the method can implement the classification of the current complaint into the corresponding business unit for response and assign the work order to the business unit.

[0094] Figure 2 FIG. 1 is a schematic diagram of a module structure of a system for supporting extended quantitative business classification according to the present invention. Figure 2 The second aspect of the present invention relates to a quantitative business classification system that supports expansion using the method in the first aspect of the present invention, an extraction module for collecting customer complaints and suggestions from WeChat business halls, online business halls, 95598 call hotlines and physical business halls, and performing semantic extraction on customer complaints and suggestions; an identification module for performing key point identification and analysis based on the extracted semantic information to obtain key semantics; a classification module for performing intelligent key point analysis on key semantics to achieve customer complaint classification; and a dispatching module for automatically dispatching current customer complaints and suggestions to business units that perform customer complaint processing based on customer complaint classification.

[0095] Preferably, the customer complaints and suggestions collected by the extraction module include image information, video information, sound information and text information; the extraction module performs semantic extraction, including: performing semantic extraction on the image information, video information, sound information and text information respectively, and mapping the extracted semantic information into a unified feature space.

[0096] Preferably, the extraction module is used to perform semantic extraction on the image information, video information, sound information and text information respectively, including: preprocessing the video information and image information through a computer vision algorithm; preprocessing the sound information through a speech recognition algorithm; and performing semantic understanding and keyword extraction on the text information, preprocessed image information, video information, and sound information through a natural language processing algorithm.

[0097] Preferably, the recognition module is used to perform key point recognition and analysis based on the extracted semantic information to obtain key semantics, including: constructing a semantic data set based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and using the semantic data set as the key semantics.

[0098] Preferably, the classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: inputting the semantic data set into a deep neural network, and using the deep neural network to convert the semantic data set into a semantic data set matrix; the semantic data set matrix includes common semantic features and distinguishing semantic features.

[0099] Preferably, the classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: achieving classification of the key semantics based on the common semantic features and the distinguishing semantic features.

[0100] Preferably, the complaint assignment module is used to automatically assign current customer complaints and suggestions to business units that perform customer complaint handling based on the customer complaint classification, including: performing key semantic extraction and intelligent key point analysis on current customer complaints and suggestions, and realizing automatic assignment of business units based on the positions of the analyzed intelligent key points.

[0101] A third aspect of the present invention relates to a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method in the first aspect of the present invention.

[0102] A fourth aspect of the present invention relates to a computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method in the first aspect of the present invention.

[0103] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0104] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0105] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0108] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quantitative service classification method supporting expansion, characterized in that: include: Collect customer complaints and suggestions, and perform semantic extraction on the customer complaints and suggestions; Identify and analyze key points based on the extracted semantic information to obtain key semantics; Intelligent key point analysis is performed on the key semantics to achieve customer complaint classification; Based on the customer complaint classification, current customer complaints and suggestions are automatically assigned to the business unit that performs customer complaint handling.

2. The method for classifying quantized services supporting expansion according to claim 1, characterized in that: The customer complaints and suggestions include image information, video information, sound information and text information; Semantic extraction is performed on the image information, video information, sound information and text information respectively, and the extracted semantic information is mapped into a unified feature space.

3. The method for supporting extended quantitative service classification according to claim 2, characterized in that: The semantic extraction of the image information, video information, sound information and text information respectively includes: Preprocessing the video information and image information by using a computer vision algorithm; Preprocessing the sound information through a speech recognition algorithm; Through natural language processing algorithms, semantic understanding and keyword extraction are performed on the text information, pre-processed image information, video information, and sound information.

4. The method for classifying quantized services supporting expansion according to claim 3, characterized in that: The key point identification and analysis is performed based on the extracted semantic information to obtain key semantics, including: A semantic data set is constructed based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and the semantic data set is used as the key semantics.

5. The method for classifying quantized services supporting expansion according to claim 4, characterized in that: The intelligent key point analysis of the key semantics to achieve customer complaint classification includes: Inputting the semantic data set into a deep neural network, and converting the semantic data set into a semantic data set matrix using the deep neural network; The semantic data set matrix includes common semantic features and distinctive semantic features.

6. The method for classifying quantized services supporting expansion according to claim 5, characterized in that: The intelligent key point analysis of the key semantics to achieve customer complaint classification includes: The key semantics are classified based on the common semantic features and the distinctive semantic features.

7. The method for classifying quantized services supporting expansion according to claim 6, characterized in that: Based on the customer complaint classification, the current customer complaints and suggestions are automatically assigned to the business unit that performs the customer complaint handling, including: The current customer complaints and suggestions are subjected to key semantic extraction and intelligent key point analysis, and the business units are automatically assigned based on the positions of the analyzed intelligent key points.

8. A quantitative service classification system supporting expansion, characterized in that: An extraction module, used to collect customer complaints and suggestions, and perform semantic extraction on the customer complaints and suggestions; The recognition module is used to identify and analyze key points based on the extracted semantic information to obtain key semantics; A classification module, used for intelligently analyzing the key semantics, thereby realizing customer complaint classification; The dispatching module is used to automatically dispatch current customer complaints and suggestions to the business unit that executes customer complaint handling based on the customer complaint classification.

9. A quantitative service classification system supporting expansion according to claim 8, characterized in that: In the extraction module, the customer complaints and suggestions include image information, video information, sound information and text information; Semantic extraction is performed on the image information, video information, sound information and text information respectively, and the extracted semantic information is mapped into a unified feature space.

10. A quantitative service classification system supporting expansion according to claim 9, characterized in that: The extraction module is used to perform semantic extraction on the image information, video information, sound information and text information respectively, and includes: Preprocessing the video information and image information by using a computer vision algorithm; Preprocessing the sound information through a speech recognition algorithm; Through natural language processing algorithms, semantic understanding and keyword extraction are performed on the text information, pre-processed image information, video information, and sound information.

11. A quantitative service classification system supporting expansion according to claim 10, characterized in that: The recognition module is used to identify and analyze key points based on the extracted semantic information to obtain key semantics, including: A semantic data set is constructed based on the extracted image information semantics, video information semantics, sound information semantics and text information semantics, and the semantic data set is used as the key semantics.

12. A system for supporting extended quantitative business classification according to claim 11, characterized in that: The classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: Inputting the semantic data set into a deep neural network, and converting the semantic data set into a semantic data set matrix using the deep neural network; The semantic data set matrix includes common semantic features and distinctive semantic features.

13. A system for supporting extended quantitative business classification according to claim 12, characterized in that: The classification module is used to perform intelligent key point analysis on the key semantics to achieve customer complaint classification, including: The key semantics are classified based on the common semantic features and the distinctive semantic features.

14. A quantitative service classification system supporting expansion according to claim 13, characterized in that: The dispatching module is used to automatically dispatch current customer complaints and suggestions to the business unit that performs customer complaint handling based on the customer complaint classification, including: The current customer complaints and suggestions are subjected to key semantic extraction and intelligent key point analysis, and the business units are automatically assigned based on the positions of the analyzed intelligent key points.

15. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-7.

16. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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