Consumer protection method and device based on artificial intelligence and storage medium

Abstract

The invention discloses a consumer protection method and device based on artificial intelligence and a storage medium. The method comprises the following steps: acquiring multi-modal customer communication corpus data; performing feature extraction on the client communication corpus data to obtain target features, and determining an emotion type corresponding to the client and emotion intensity corresponding to the emotion type according to the target features; according to the individual user portrait, the emotion type and the emotion intensity of the customer, the complaint probability corresponding to the customer is determined, and the individual user portrait is at least used for representing the historical complaint preference of the customer; and when the complaint probability exceeds a probability threshold value corresponding to the target business handled by the client, sending complaint early warning information to a responsible department corresponding to the target business. The technical problem that the customer complaint early warning effect is poor and the consumer demand cannot be met due to the fact that complaint management in the related technology still stays in a manual judgment and processing mode is solved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and more specifically, to an artificial intelligence-based consumer protection method, device, and storage medium. Background Art

[0002] Customer complaint management is not only the core concern of regulatory authorities, but also an important breakthrough for banks to enhance their competitiveness. However, at present, the complaint management platform in related technologies faces many challenges, and there are deficiencies in customer service, process design, internal management, etc. For example, the complaint management in related technologies still remains in the mode of manual judgment and processing, and there are problems such as low accuracy of early warning of customer complaints and poor complaint handling effect, which cannot meet consumer needs and effectively protect the rights and interests of consumers.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a consumer protection method, device and storage medium based on artificial intelligence, so as to at least solve the technical problem that the early warning effect of customer complaints is poor and the needs of consumers cannot be met because the complaint management in the related technology still remains in the manual judgment and processing mode.

[0005] According to one aspect of an embodiment of the present application, there is provided an artificial intelligence-based consumer protection method, comprising: obtaining multimodal customer communication corpus data, wherein the customer communication corpus data comprises at least one of the following: text data, video data, and audio data of customers collected in the process of handling business through various service channels; performing feature extraction on the customer communication corpus data to obtain target features, and determining the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features; determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business.

[0006] Optionally, feature extraction of customer communication corpus data includes: performing word segmentation on text data in the customer communication corpus data, and determining the part of speech corresponding to each word obtained after word segmentation; performing syntactic analysis on the text data based on the part of speech corresponding to each word, and obtaining semantic structure information corresponding to the text data, wherein the semantic structure information is used to characterize the contextual association relationship between each word in the text data; determining keywords in the text data based on the semantic structure information, and mapping the keywords to a preset vector space to obtain a first feature corresponding to the text data, wherein the keywords are words in the text data used to characterize customer emotions.

[0007] Optionally, feature extraction is performed on the customer communication corpus data to obtain the target feature, which also includes: determining the key frames of the video data, and determining the face area and the limb area in the key frames; determining the facial key points in the face area, and determining the limb key points in the limb area; determining the customer's facial expression features based on the position change information of the facial key points in adjacent key frames, and determining the customer's body language features based on the position change information of the limb key points in adjacent key frames; determining the second feature corresponding to the video data based on the facial expression features and the body language features; and / or extracting a third feature for characterizing the customer's emotion from the audio data of the customer communication corpus data, wherein the third feature includes: acoustic features and rhythmic features, the acoustic features include at least one of the following: fundamental frequency features, energy features, and Mel-frequency cepstral coefficients, and the rhythmic features include at least one of the following: speaking rate features and pause features; and determining the target feature based on the first feature, the second feature, and the third feature.

[0008] Optionally, determining the emotion type and emotion intensity corresponding to the customer based on the target characteristics includes: using an emotion classification model to determine the emotion type and emotion intensity based on the target characteristics, wherein the emotion classification model is trained based on a training data set, the training data set includes multiple training samples, each training sample includes: historical customer communication corpus data, and emotion labels corresponding to the historical customer communication corpus data for characterizing the emotion type and emotion intensity, the emotion type includes at least one of the following: anger, sadness, joy, neutrality, and the emotion intensity includes: mild, moderate, and strong; determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity includes: obtaining relevant information of the customer, wherein the relevant information includes: personal basic information, historical business handling records, and historical complaint records; determining the customer's corresponding personal user portrait based on the relevant information; using a complaint probability prediction model to determine the complaint probability based on the personal user portrait and the emotion type and emotion intensity, wherein the complaint probability is used to characterize the probability of the customer initiating a complaint under the emotion type and emotion intensity.

[0009] Optionally, when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business includes: obtaining complaint record data of the target business within a preset historical time period, wherein the complaint record data includes at least one of the following: the number of complaints, the type of complaint, and the result of complaint handling; determining the complaint trend curve and complaint handling satisfaction corresponding to the target business based on the complaint record data, wherein the complaint trend curve is at least used to characterize the trend of the number of complaints received by the target business over time; updating the probability threshold corresponding to the target business based on the complaint trend curve and the complaint handling satisfaction, and judging whether there is a problem within the target time period. In the policy update related to the target business, wherein the target time period is the time period from the first moment to the second moment, the second moment is the moment when the probability threshold corresponding to the target business is most recently updated, and the first moment is the moment when the probability threshold corresponding to the target business is last updated; in the case that there is a policy update related to the target business within the target time period, the policy impact coefficient corresponding to the target business is determined based on the most recently updated policy, and the probability threshold corresponding to the target business is adjusted based on the policy impact coefficient; in the case that the complaint probability exceeds the adjusted probability threshold corresponding to the target business, a complaint warning information is generated, and the complaint warning information is sent to the responsible department corresponding to the target business.

[0010] Optionally, the method also includes: upon receiving customer complaint information, determining the target business corresponding to the customer complaint information, and backtracking the target business complained about to obtain historical business handling information corresponding to the target business complained about, wherein the customer complaint information includes at least one of the following: complaint content, complaint event, and complaint object, and the historical business handling information includes: business handling time, location, handler, business process, and customer information; determining complaint text features corresponding to the customer complaint information, and business handling features corresponding to the historical business handling information, wherein the complaint text features are at least used to characterize the customer's complaint keywords and emotional tendencies, and the business handling features are at least used to characterize the processing time and results of each link in the business process, as well as the service attitude of the handler; performing attribution analysis based on the complaint text features, business handling features, and the customer's personal user portrait to obtain analysis results corresponding to the customer complaint information, wherein the analysis results are used to characterize the cause of the complaint corresponding to the customer complaint information; based on the analysis results, determining the complaint handling process, and generating a complaint handling work order corresponding to the customer complaint information, and sending the complaint handling work order to the person in charge corresponding to the initial node in the complaint handling process.

[0011] Optionally, the method also includes: based on the analysis results, statistics are collected on the causes of various types of complaints for various types of target businesses, and based on the statistical results, visualization charts are generated for display, wherein the causes of complaints include at least one of the following: product problems, service attitude problems, and business process problems, and the visualization charts include at least one of the following: a pie chart used to characterize the proportion of various causes of complaints in the total number of complaints, a bar chart used to characterize the number of different types of causes of complaints, and a heat map used to characterize the distribution of different causes of complaints in different time periods or different regions; and / or, determining the timestamps corresponding to each process node in the complaint handling process, wherein the timestamp is used to characterize the moment of executing the process node and the processing time of the customer complaint information at the process node in the complaint handling process; based on the timestamp, determining the customer complaint information processed by each process node in the selected time period, and obtaining a process heat map corresponding to the selected time period, wherein the position of each process node in the process heat map is characterized by different thermal values ​​to characterize the number of customer complaint information processed by the process node and / or the processing time of the customer complaint information at the process node.

[0012] According to another aspect of the embodiment of the present application, there is also provided a consumer protection device based on artificial intelligence, including: a data acquisition module, used to acquire multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data of customers collected in the process of handling business in various service channels; a feature extraction module, used to extract features from the customer communication corpus data to obtain target features, and determine the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features; a probability prediction module, used to determine the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preferences; a complaint warning module, used to send complaint warning information to the responsible department corresponding to the target business when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored computer program, wherein the device where the non-volatile storage medium is located executes an artificial intelligence-based consumer protection method by running the computer program.

[0014] According to another aspect of the embodiments of the present application, a computer program product is also provided, including a computer program, which implements the steps of the artificial intelligence-based consumer protection method when the computer program is executed by a processor.

[0015] In an embodiment of the present application, multimodal customer communication corpus data is obtained, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data of customers collected in the process of handling business in various service channels; feature extraction is performed on the customer communication corpus data to obtain target features, and based on the target features, the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type are determined; based on the customer's personal user portrait, emotion type and emotion intensity, the complaint probability corresponding to the customer is determined, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, a complaint warning information is sent to the responsible department corresponding to the target business. Through comprehensive analysis of multimodal fusion data, the purpose of accurately understanding the customer's emotional state and complaint tendency, warning of potential complaint risks in advance, and effectively improving customer satisfaction and enterprise service quality is achieved, thereby solving the technical problem that the warning effect of customer complaints is poor and consumer needs cannot be met due to the fact that complaint management in related technologies still remains in the manual judgment and processing mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 It is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a method for consumer protection based on artificial intelligence provided in an embodiment of the present application;

[0018] Figure 2 It is a schematic diagram of a method flow of consumer protection based on artificial intelligence provided according to an embodiment of the present application;

[0019] Figure 3 is a schematic diagram of a policy acquisition and analysis process provided according to an embodiment of the present application;

[0020] Figure 4 It is a structural schematic diagram of an artificial intelligence-based consumer protection device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The complaint management platform in related technologies lacks data processing and analysis capabilities. One of the core values ​​of complaint management platforms is to gain insights into service problems and improvement directions through data analysis. If the platform's data processing capabilities are insufficient, such as lack of real-time data analysis capabilities, difficulty in supporting large-scale data processing, or poor data visualization, its decision-making support role will be limited. In addition, the complaint management platform in related technologies lacks intelligent support and remains in the traditional manual processing mode. It lacks the support of intelligent technologies such as natural language processing, intelligent recommendation, decision analysis, and intelligent alarms, and is difficult to meet the automatic classification, rapid response, and accurate decision-making of business complaints.

[0024] In order to solve the above problems, relevant solutions are provided in the embodiments of the present application, which are described in detail below.

[0025] According to an embodiment of the present application, an embodiment of a method for consumer protection based on artificial intelligence is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1The hardware structure block diagram of a computer terminal (or electronic device) for implementing a consumer protection method based on artificial intelligence is shown. Figure 1 As shown, the computer terminal 10 (or electronic device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0027] It should be noted that the above-mentioned one or more processors 102 and / or other artificial intelligence-based consumer protection circuits may generally be referred to herein as "artificial intelligence-based consumer protection circuits". The artificial intelligence-based consumer protection circuit may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the artificial intelligence-based consumer protection circuit may be a single independent processing module, or may be fully or partially integrated into any of the other components in the computer terminal 10 (or electronic device). As involved in the embodiments of the present application, the artificial intelligence-based consumer protection circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0028] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the artificial intelligence-based consumer protection method in the embodiment of the present application. The processor 102 executes various functional applications and artificial intelligence-based consumer protection by running the software programs and modules stored in the memory 104, that is, the above-mentioned artificial intelligence-based consumer protection method is realized. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0029] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or electronic device).

[0031] In the above operating environment, the embodiment of the present application provides a consumer protection method based on artificial intelligence. Figure 2 is a schematic diagram of a method flow of consumer protection based on artificial intelligence provided according to an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0032] Step S202, obtaining multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data collected from customers during the business handling process of various service channels;

[0033] Step S204, extracting features from the customer communication corpus data to obtain target features, and determining the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features;

[0034] Step S206, determining the complaint probability corresponding to the customer based on the customer's personal user portrait, emotion type, and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference;

[0035] Step S208: When the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, a complaint warning message is sent to the responsible department corresponding to the target business.

[0036] Through the above steps and comprehensive analysis of multimodal fusion data, we can accurately understand the emotional state and complaint tendency of customers, warn of potential complaint risks in advance, and effectively improve customer satisfaction and corporate service quality. This solves the problem that the warning effect of customer complaints is poor and the technical problem of being unable to meet consumer needs is caused by the fact that complaint management in related technologies still remains in the manual judgment and processing mode.

[0037] The following is a further introduction to the artificial intelligence-based consumer protection method in steps S202 to S208 of the embodiment of the present application.

[0038] First, in an embodiment of the present application, customer feedback information can be collected through various service channels of the bank (such as online banking, mobile banking, telephone banking, self-service terminals, branch counters, etc.), including text data such as customers' inquiries, feedback, suggestions, complaints, etc. when handling business, as well as video and audio data of customers handling business at branches (for example, audio data when customers communicate with customer service over the phone), etc., to obtain customer communication corpus data.

[0039] For example, through the data acquisition interface, it is possible to connect with the bank's customer service system, social media monitoring system, telephone service center system, etc., to automatically capture customer communication records, such as online chat records, social media comments, call recordings and videos, etc., for subsequent analysis.

[0040] After that, feature extraction can be performed on the multimodal customer communication corpus data. The specific steps are as follows.

[0041] In some embodiments of the present application, feature extraction of customer communication corpus data includes the following steps: performing word segmentation on text data in the customer communication corpus data, and determining the part of speech corresponding to each word obtained after word segmentation; performing syntactic analysis on the text data based on the part of speech corresponding to each word, and obtaining semantic structure information corresponding to the text data, wherein the semantic structure information is used to characterize the contextual association relationship between each word in the text data; determining keywords in the text data based on the semantic structure information, and mapping the keywords to a preset vector space to obtain a first feature corresponding to the text data, wherein the keywords are words in the text data used to characterize customer emotions.

[0042] In some embodiments of the present application, feature extraction is performed on customer communication corpus data to obtain target features, and the following steps are also included: determining key frames of video data, and determining face areas and limb areas in the key frames; determining facial key points in the face area, and determining limb key points in the limb area; determining the customer's facial expression features based on position change information of facial key points in adjacent key frames, and determining the customer's body language features based on position change information of limb key points in adjacent key frames; determining a second feature corresponding to the video data based on the facial expression features and the body language features; and / or extracting a third feature for characterizing the customer's emotion from the audio data of the customer communication corpus data, wherein the third feature includes: acoustic features and prosodic features, the acoustic features include at least one of the following: fundamental frequency features, energy features, and Mel-frequency cepstral coefficients, and the prosodic features include at least one of the following: speaking rate features and pause features; determining the target feature based on the first feature, the second feature, and the third feature.

[0043] Specifically, for the text data in the customer communication corpus data, the text data can be cleaned first to remove irrelevant characters, noise information, etc. to ensure the accuracy and consistency of the data. The text is then processed by word segmentation and stop word removal to prepare for subsequent analysis. After that, natural language processing technology is used in combination with sentiment dictionaries to identify keywords such as sentiment words and intensity adverbs in the text, and pre-trained word embedding models (such as Word2Vec, BERT, etc.) are used to convert the text data into word vector representations, extract the semantic features of the keywords used to represent customer sentiment in the text, and obtain the first feature corresponding to the text data.

[0044] For example, the system will use the word segmentation tool to divide the text into vocabulary units, and then determine the grammatical role of each word through part-of-speech tagging, such as subject, predicate, object, etc. Then, use syntactic analysis technology to parse the sentence structure and understand the logical relationship between words. Through the mapping of sentiment words, the keywords are converted into vector representations to facilitate subsequent machine learning model processing. For example, the system identifies "bad service attitude" as a negative sentiment keyword and converts it into a vector for subsequent sentiment analysis.

[0045] For the video data in the customer communication corpus data, the facial expressions and body language in the video will be analyzed. Specifically, key frame images are first extracted from the video file. Frames with large motion changes can be extracted at fixed time intervals (such as one frame per second) or through motion detection algorithms. The extracted video frame images are cropped, scaled, normalized, and other processes are performed to facilitate subsequent feature extraction. After that, facial expression features can be extracted from the pre-processed video key frames, and face detection algorithms (such as Haar feature classifiers, etc.) can be used to locate the face area in the video frame images. Use a deep learning model to detect facial key points (such as eyebrows, eyes, nose, corners of the mouth, etc.) in the face area, and extract facial expression features based on the position changes of facial key points, such as the movement amplitude of the eyebrows, the degree of opening and closing of the eyes, the upward or downward movement of the corners of the mouth, etc., to obtain facial expression features; and, you can also use posture estimation algorithms (such as OpenPose, AlphaPose, etc.) to detect the body key points of the characters in the video, such as shoulders, elbows, wrists, knees, etc., and extract body movement features based on the movement trajectory and relative position changes of the body key points, such as the swing amplitude of the arms, the swing frequency of the legs, etc., to obtain body language features, so as to obtain the second feature corresponding to the video data based on the customer's facial expression features and body language features.

[0046] For example, the system will identify key frames in a video and analyze the faces and body movements in them. Through facial expression recognition and body language analysis, the system can capture the customer's emotional changes and non-verbal signals. For example, if the system recognizes that the customer frowns or shakes his head in the video, it can be judged that the customer may be dissatisfied with the service.

[0047] In addition, as an optional implementation, when analyzing video data, scene features can also be extracted, for example, the background environment in the video can be analyzed to extract scene features, such as the layout of bank branches, the relative positions of customers and staff, etc.; or object detection algorithms can be used to detect relevant objects in the video, such as counters, ATM machines, seats, etc. The relative position changes between customers and staff or facilities in the scene can be used to assist in judging customer emotions.

[0048] For the audio data in the customer communication corpus data, it can be analyzed by identifying features such as speech rate, pitch and emotional vocabulary. Specifically, the collected audio signal can be first subjected to noise reduction processing to remove background noise, improve the clarity of the voice signal, and divide the continuous voice signal into multiple short-term voice segments for subsequent feature extraction. Then, the pre-processed audio data can be subjected to acoustic feature extraction, including: using methods such as autocorrelation method and cepstrum method to extract the fundamental frequency (pitch) characteristics of the voice signal (the fundamental frequency under different emotional states will have obvious differences, such as the fundamental frequency is usually higher when angry and lower when sad); calculating the short-term energy characteristics of the voice signal to reflect the intensity of the voice signal (indicating the loudness or intensity of the sound, which is usually related to the speaker's excitement. High energy may indicate excitement or anger, while low energy may indicate sadness or calmness); extracting Mel frequency cepstrum coefficient features to capture the spectral information of the voice signal; in addition, the pre-processed audio data can also be subjected to rhythm feature extraction, including: calculating the speech rate of the voice signal, that is, the length of the voice per unit time, which can reflect the speaker's tension or emotional state. For example, people usually speak faster when they are angry or excited, and slower when they are sad or thoughtful. We detect pause points in speech signals, extract the duration and frequency of pauses (which can reflect the speaker's thinking process and emotional changes. Frequent pauses may indicate hesitation or uncertainty, while long pauses may indicate silence or emotional repression), etc., and finally obtain the third feature corresponding to the audio data.

[0049] For example, the system will extract acoustic and prosodic features in the audio, such as fundamental frequency, energy, speech rate, and pauses, which can reflect the customer's emotional state. For example, identifying high fundamental frequency, fast speech rate, and frequent pauses in a call recording may indicate that the customer is nervous or angry.

[0050] After that, the final target feature can be determined based on the first feature, the second feature, and the third feature. For example, the text, video, and audio feature vectors can be concatenated to form a comprehensive feature vector; or different weights can be assigned to features of different modalities, and then weighted summed to obtain fused features. The weights can be adjusted according to the importance of each modality feature to the sentiment analysis task.

[0051] After obtaining the target features corresponding to the customer communication corpus data, the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type can be determined based on the target features. The specific steps are as follows.

[0052] In some embodiments of the present application, determining the emotion type and emotion intensity corresponding to the customer according to the target feature includes the following steps: using an emotion classification model to determine the emotion type and emotion intensity according to the target feature, wherein the emotion classification model is trained based on a training data set, and the training data set includes multiple training samples, each training sample includes: historical customer communication corpus data, and an emotion label corresponding to the historical customer communication corpus data for characterizing the emotion type and emotion intensity, the emotion type includes at least one of the following: anger, sadness, joy, neutral, and the emotion intensity includes: mild, moderate, and strong;

[0053] Specifically, a multimodal emotion classification model can be constructed, and the fused target features can be used to classify the customer's emotion type and quantify the customer's emotion level. In this embodiment, a deep learning model (such as Transformer, etc.) can be used for training. The model can identify the customer's emotion type, such as anger, sadness, joy, neutrality, etc., and divide the emotion intensity into different levels, such as mild, medium, strong, etc. Through the deep learning method, the system can automatically learn the characteristic performance under different emotion types and improve the accuracy of classification.

[0054] Afterwards, the corresponding complaint probability of the customer is determined based on the customer's personal user portrait, emotion type and emotion intensity. The specific steps are as follows.

[0055] In some embodiments of the present application, determining the complaint probability corresponding to a customer based on the customer's personal user portrait and emotion type and emotion intensity includes the following steps: obtaining relevant information of the customer, wherein the relevant information includes: personal basic information, historical business handling records, and historical complaint records; determining the personal user portrait corresponding to the customer based on the relevant information; using a complaint probability prediction model to determine the complaint probability based on the personal user portrait and emotion type and emotion intensity, wherein the complaint probability is used to characterize the probability of a customer initiating a complaint under the emotion type and emotion intensity.

[0056] Specifically, we can collect data such as the customer's basic information (such as age, gender, occupation, etc.), historical transaction records, historical complaint records, service preferences, etc. to build a personal user portrait of the customer. Specifically: Encode the customer's age, gender, occupation and other basic information, such as using one-hot encoding or label encoding; extract key features from the customer's historical transaction records, such as transaction frequency, transaction amount, transaction type, etc.; extract features such as complaint frequency, complaint type, complaint handling satisfaction, etc. from historical complaint records; analyze the customer's service preferences, such as preferred service channels, service hours, etc.

[0057] Then, a complaint probability prediction model can be constructed by combining the customer's personal user portrait, emotion type and emotion intensity, as well as the customer's business type, historical complaint record and other information. Machine learning algorithms (such as random forests, support vector machines, etc.) or deep learning models (such as neural networks) can be used for training, so that the model can predict the probability of customer complaints based on the customer's emotional state and business situation. For example, if a customer shows moderate dissatisfaction in a recent transaction and there are similar complaints in the historical record, the model will predict that the customer has a high probability of complaining.

[0058] When the predicted customer complaint probability exceeds the corresponding probability threshold, a trigger is sent to the responsible department corresponding to the target business. The specific steps are as follows.

[0059] In some embodiments of the present application, when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business includes the following steps: obtaining complaint record data of the target business within a preset historical time period, wherein the complaint record data includes at least one of the following: the number of complaints, the type of complaint, and the result of complaint handling; based on the complaint record data, determining the complaint trend curve and complaint handling satisfaction corresponding to the target business, wherein the complaint trend curve is at least used to characterize the trend of the number of complaints received by the target business over time; based on the complaint trend curve and the complaint handling satisfaction, updating the probability threshold corresponding to the target business, and judging whether the target business has been received within the target time. Whether there is a policy update related to the target business within the target time period, where the target time period is the time period from the first moment to the second moment, the second moment is the moment when the probability threshold corresponding to the target business is most recently updated, and the first moment is the moment when the probability threshold corresponding to the target business is last updated; in the case that there is a policy update related to the target business within the target time period, the policy impact coefficient corresponding to the target business is determined based on the latest updated policy, and the probability threshold corresponding to the target business is adjusted based on the policy impact coefficient; in the case that the complaint probability exceeds the adjusted probability threshold corresponding to the target business, a complaint warning information is generated, and the complaint warning information is sent to the responsible department corresponding to the target business.

[0060] Specifically, in this embodiment, the corresponding complaint probability threshold can be set according to the characteristics of different business types of the target business and the historical complaint data. For example, for high-risk businesses (such as loan approval, large-amount transfer, etc.), a lower complaint probability threshold can be set to provide timely warning. At the same time, based on historical data, the complaint trend is analyzed to provide data support for complaint warning. Specifically, the complaint record data of each target business in a historical period can be collected, including information such as the number of complaints, complaint types, and complaint handling results. The data is statistically analyzed to calculate indicators such as the complaint frequency and complaint handling satisfaction of each business, and the time series characteristics of the complaint data are analyzed by establishing a complaint trend monitoring model to identify the rising or falling trend of the number of complaints. For example, the time series analysis method can be used to model and predict the number of complaints, and the complaint probability threshold of each target business can be dynamically adjusted according to the complaint trend monitoring results and complaint handling satisfaction. When the number of complaints of a certain business is on the rise or the complaint handling satisfaction is low, the complaint probability threshold of the business can be increased to more strictly monitor the complaint risk.

[0061] In addition, if there is a policy update within a specific time period, the system will consider the policy impact coefficient, adjust the probability threshold, and adapt to changes in the business environment to more accurately assess the complaint risk. Specifically, the latest financial policy release information can be tracked and collected in real time. Use natural language processing technology to parse the policy text and extract information such as keywords and policy change points related to banking business. Analyze the impact of financial policies on different banking businesses and build a business impact assessment model. The model can assess the impact of policies on the potential risks and complaint probability of each target business based on factors such as policy change points, business types, and historical business data. According to the business impact assessment results, dynamically adjust the complaint probability threshold corresponding to each target business. For example, when a certain financial policy increases the regulatory requirements for loan business, the complaint probability threshold of the loan business can be appropriately lowered to warn of potential complaint risks earlier.

[0062] For example, Figure 3 As shown, by crawling the content of relevant regulatory websites for analysis and real-time calculation, big data analysis can be used to predict potential complaint hotspots and trends, and to discover emergencies that may lead to intensive complaints (such as policy adjustments), so that relevant staff can make relevant work preparations for resolving the incidents. For example, relevant mortgage policy adjustments may lead to a larger complaint outbreak period among existing mortgage users.

[0063] On the other hand, for complaints that have already occurred, the system will automatically identify the content of the complaint, determine the relevant business of the complaint, trace back historical business processing information, and conduct attribution analysis. The specific steps are as follows.

[0064] In some embodiments of the present application, the method also includes the following steps: upon receiving customer complaint information, determining the target business corresponding to the customer complaint information, and backtracking the target business being complained about to obtain historical business handling information corresponding to the target business being complained about, wherein the customer complaint information includes at least one of the following: complaint content, complaint event, and complaint object, and the historical business handling information includes: time, location, handler, business process, and customer information of the business handling; determining the complaint text features corresponding to the customer complaint information, and the business handling features corresponding to the historical business handling information, wherein the complaint text features are at least used to characterize the customer's complaint keywords and emotional tendencies, and the business handling features are at least used to characterize the processing time and results of each link in the business process, as well as the service attitude of the handler; performing attribution analysis based on the complaint text features, the business handling features, and the customer's personal user portrait to obtain the analysis results corresponding to the customer complaint information, wherein the analysis results are used to characterize the cause of the complaint corresponding to the customer complaint information; determining the complaint handling process based on the analysis results, and generating a complaint handling work order corresponding to the customer complaint information, and sending the complaint handling work order to the person in charge corresponding to the initial node in the complaint handling process.

[0065] For example, if a customer complains about a credit card bill, the system will automatically retrieve all business process records related to the credit card and analyze the root cause of the problem, such as errors in the business process or the attitude of the service staff. Based on the analysis results, the system will automatically start the complaint handling process, generate a work order, assign it to the corresponding handler, and track the handling progress.

[0066] In addition, by counting the causes of different types of complaints, visual charts such as pie charts, bar charts and heat maps can be generated so that management can intuitively understand the distribution of complaints, as shown below.

[0067] In some embodiments of the present application, the method also includes: based on the analysis results, statistics are collected on the causes of various types of complaints for various types of target businesses, and based on the statistical results, a visualization chart is generated for display, wherein the causes of complaints include at least one of the following: product problems, service attitude problems, and business process problems, and the visualization chart includes at least one of the following: a pie chart used to characterize the proportion of various causes of complaints in the total number of complaints, a bar chart used to characterize the number of different types of causes of complaints, and a heat map used to characterize the distribution of different causes of complaints in different time periods or different regions; and / or, determining the timestamps corresponding to each process node in the complaint handling process, wherein the timestamp is used to characterize the moment of executing the process node and the processing time of the customer complaint information at the process node in the complaint handling process; based on the timestamp, determining the customer complaint information processed by each process node in the selected time period, and obtaining a process heat map corresponding to the selected time period, wherein the position of each process node in the process heat map is characterized by different thermal values ​​to characterize the number of customer complaint information processed by the process node and / or the processing time of the customer complaint information at the process node.

[0068] For example, a pie chart can show the proportion of service attitude problems, business process problems, and product problems in the total number of complaints; a bar chart can show the number of different types of complaints; and a heat map can reveal the distribution of complaints at different times and regions. In addition, the system will also record and analyze the timestamps in the complaint handling process and generate a process heat map to evaluate the processing efficiency and complaint volume of each node, helping to optimize the process and improve processing speed and customer satisfaction.

[0069] Through comprehensive analysis of multimodal data, this application solution can more accurately understand the emotional state and complaint tendency of customers, thereby early warning of potential complaint risks and effectively improving customer satisfaction and corporate service quality. It can timely adjust business processes and policies to prevent complaints from occurring. At the same time, after a complaint occurs, it can quickly locate the problem, improve the efficiency of complaint handling, and continuously improve the prevention, interception and handling mechanisms of customer complaints, from pre-warning, in-process response to post-event improvement, to ensure accurate control of the entire process of complaint incidents, and improve the professionalism and efficiency of consumer protection work. At the same time, big data analysis technology is used to deeply mine and analyze complaint data to identify potential problems and trends. Through a data-driven approach, it helps companies build a more efficient and humane complaint management system, reduce operating costs, and enhance customer trust and loyalty.

[0070] According to an embodiment of the present application, an embodiment of a consumer protection device based on artificial intelligence is also provided. Figure 4 is a schematic diagram of the structure of a consumer protection device based on artificial intelligence provided according to an embodiment of the present application. Figure 4 As shown, the device comprises:

[0071] The data acquisition module 40 is used to acquire multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data collected from customers during the business handling process of various service channels;

[0072] The feature extraction module 42 is used to extract features from the customer communication corpus data to obtain target features, and determine the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features;

[0073] The probability prediction module 44 is used to determine the complaint probability corresponding to the customer based on the customer's personal user profile and the emotion type and emotion intensity, wherein the personal user profile is at least used to characterize the customer's historical complaint preference;

[0074] The complaint warning module 46 is used to send complaint warning information to the responsible department corresponding to the target business when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer.

[0075] Optionally, feature extraction of customer communication corpus data includes: performing word segmentation on text data in the customer communication corpus data, and determining the part of speech corresponding to each word obtained after word segmentation; performing syntactic analysis on the text data based on the part of speech corresponding to each word, and obtaining semantic structure information corresponding to the text data, wherein the semantic structure information is used to characterize the contextual association relationship between each word in the text data; determining keywords in the text data based on the semantic structure information, and mapping the keywords to a preset vector space to obtain a first feature corresponding to the text data, wherein the keywords are words in the text data used to characterize customer emotions.

[0076] Optionally, feature extraction is performed on the customer communication corpus data to obtain the target feature, which also includes: determining the key frames of the video data, and determining the face area and the limb area in the key frames; determining the facial key points in the face area, and determining the limb key points in the limb area; determining the customer's facial expression features based on the position change information of the facial key points in adjacent key frames, and determining the customer's body language features based on the position change information of the limb key points in adjacent key frames; determining the second feature corresponding to the video data based on the facial expression features and the body language features; and / or extracting a third feature for characterizing the customer's emotion from the audio data of the customer communication corpus data, wherein the third feature includes: acoustic features and rhythmic features, the acoustic features include at least one of the following: fundamental frequency features, energy features, and Mel-frequency cepstral coefficients, and the rhythmic features include at least one of the following: speaking rate features and pause features; and determining the target feature based on the first feature, the second feature, and the third feature.

[0077] Optionally, determining the emotion type and emotion intensity corresponding to the customer based on the target characteristics includes: using an emotion classification model to determine the emotion type and emotion intensity based on the target characteristics, wherein the emotion classification model is trained based on a training data set, the training data set includes multiple training samples, each training sample includes: historical customer communication corpus data, and emotion labels corresponding to the historical customer communication corpus data for characterizing the emotion type and emotion intensity, the emotion type includes at least one of the following: anger, sadness, joy, neutrality, and the emotion intensity includes: mild, moderate, and strong; determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity includes: obtaining relevant information of the customer, wherein the relevant information includes: personal basic information, historical business handling records, and historical complaint records; determining the customer's corresponding personal user portrait based on the relevant information; using a complaint probability prediction model to determine the complaint probability based on the personal user portrait and the emotion type and emotion intensity, wherein the complaint probability is used to characterize the probability of the customer initiating a complaint under the emotion type and emotion intensity.

[0078] Optionally, when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business includes: obtaining complaint record data of the target business within a preset historical time period, wherein the complaint record data includes at least one of the following: the number of complaints, the type of complaint, and the result of complaint handling; determining the complaint trend curve and complaint handling satisfaction corresponding to the target business based on the complaint record data, wherein the complaint trend curve is at least used to characterize the trend of the number of complaints received by the target business over time; updating the probability threshold corresponding to the target business based on the complaint trend curve and the complaint handling satisfaction, and judging whether there is a problem within the target time period. In the policy update related to the target business, wherein the target time period is the time period from the first moment to the second moment, the second moment is the moment when the probability threshold corresponding to the target business is most recently updated, and the first moment is the moment when the probability threshold corresponding to the target business is last updated; in the case that there is a policy update related to the target business within the target time period, the policy impact coefficient corresponding to the target business is determined based on the most recently updated policy, and the probability threshold corresponding to the target business is adjusted based on the policy impact coefficient; in the case that the complaint probability exceeds the adjusted probability threshold corresponding to the target business, a complaint warning information is generated, and the complaint warning information is sent to the responsible department corresponding to the target business.

[0079] Optionally, the artificial intelligence-based consumer protection device is also used to: upon receiving customer complaint information, determine the target business corresponding to the customer complaint information, and trace back the target business complained about to obtain historical business handling information corresponding to the target business complained about, wherein the customer complaint information includes at least one of the following: complaint content, complaint event, and complaint object, and the historical business handling information includes: business handling time, location, handler, business process, and customer information; determine the complaint text features corresponding to the customer complaint information, and the business handling features corresponding to the historical business handling information, wherein the complaint text features are at least used to characterize the customer's complaint keywords and emotional tendencies, and the business handling features are at least used to characterize the processing time and results of each link in the business process, as well as the service attitude of the handler; perform attribution analysis based on the complaint text features, business handling features, and the customer's personal user portrait to obtain the analysis results corresponding to the customer complaint information, wherein the analysis results are used to characterize the cause of the complaint corresponding to the customer complaint information; based on the analysis results, determine the complaint handling process, and generate a complaint handling work order corresponding to the customer complaint information, and send the complaint handling work order to the person in charge corresponding to the initial node in the complaint handling process.

[0080] Optionally, the artificial intelligence-based consumer protection device is also used to: based on the analysis results, count the causes of various types of complaints for various types of target businesses, and based on the statistical results, generate a visual chart for display, wherein the causes of complaints include at least one of the following: product problems, service attitude problems, business process problems, and the visual chart includes at least one of the following: a pie chart for representing the proportion of various types of complaint causes in the total number of complaints, a bar chart for representing the number of different types of complaint causes, and a heat map for representing the distribution of different complaint causes in different time periods or different regions; and / or, determine the timestamp corresponding to each process node in the complaint handling process, wherein the timestamp is used to represent the moment of executing the process node and the processing time of the customer complaint information at the process node in the complaint handling process; based on the timestamp, determine the customer complaint information processed by each process node in the selected time period, and obtain the process heat map corresponding to the selected time period, wherein the position of each process node in the process heat map is represented by different thermal values ​​to represent the number of customer complaint information processed by the process node and / or the processing time of the customer complaint information at the process node.

[0081] It should be noted that the various modules in the above-mentioned artificial intelligence-based consumer protection device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0082] It should be noted that the artificial intelligence-based consumer protection device provided in this embodiment can be used to execute Figure 2 The artificial intelligence-based consumer protection method shown, therefore, the relevant explanations and descriptions of the above-mentioned artificial intelligence-based consumer protection method are also applicable to the embodiments of the present application and will not be repeated here.

[0083] An embodiment of the present application also provides a non-volatile storage medium, the non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following artificial intelligence-based consumer protection method by running the computer program: obtaining multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data of customers collected in the process of handling business in various service channels; performing feature extraction on the customer communication corpus data to obtain target features, and determining the corresponding emotion type of the customer and the emotion intensity corresponding to the emotion type based on the target features; determining the corresponding complaint probability of the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business.

[0084] The embodiments of the present application also provide a computer program product, including a computer program, which, when executed by a processor, implements the steps of the artificial intelligence-based consumer protection method described in each embodiment of the present application: obtaining multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data of customers collected during the business processing of each service channel; performing feature extraction on the customer communication corpus data to obtain target features, and determining the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features; determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business.

[0085] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0086] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0088] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0089] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0090] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc., which can store program code.

[0091] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A consumer protection method based on artificial intelligence, characterized in that: include: Acquire multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data collected from customers during business handling in various service channels; Extracting features from the customer communication corpus data to obtain target features, and determining the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features; Determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; When the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, a complaint warning message is sent to the responsible department corresponding to the target business.

2. The artificial intelligence-based consumer protection method according to claim 1, characterized in that: Extracting features from the customer communication corpus data includes: Performing word segmentation on the text data in the customer communication corpus data, and determining the part of speech corresponding to each word obtained after the word segmentation; According to the part of speech corresponding to each word, the text data is subjected to syntactic analysis to obtain semantic structure information corresponding to the text data, wherein the semantic structure information is used to characterize the contextual association relationship between the words in the text data; Based on the semantic structure information, keywords in the text data are determined, and the keywords are mapped to a preset vector space to obtain a first feature corresponding to the text data, wherein the keywords are words in the text data used to characterize customer emotions.

3. The artificial intelligence-based consumer protection method according to claim 2, characterized in that: Extracting features from the customer communication corpus data to obtain target features also includes: Determine a key frame of the video data, and determine a face area and a body area in the key frame; Determine facial key points in the face region, and determine body key points in the body region; Determine the facial expression features of the customer based on the position change information of the facial key points in the adjacent key frames, and determine the body language features of the customer based on the position change information of the body key points in the adjacent key frames; Determining a second feature corresponding to the video data based on the facial expression feature and the body language feature; And / or, extracting a third feature for characterizing customer emotions from the audio data of the customer communication corpus data, wherein the third feature includes: an acoustic feature and a prosodic feature, the acoustic feature includes at least one of the following: a fundamental frequency feature, an energy feature, and a Mel frequency cepstrum coefficient, and the prosodic feature includes at least one of the following: a speech rate feature and a pause feature; The target feature is determined based on the first feature, the second feature and the third feature.

4. The artificial intelligence-based consumer protection method according to claim 1, characterized in that: Determining the emotion type and emotion intensity corresponding to the customer according to the target feature includes: using an emotion classification model to determine the emotion type and emotion intensity according to the target feature, wherein the emotion classification model is obtained by training based on a training data set, the training data set includes a plurality of training samples, each of the training samples includes: historical customer communication corpus data, and an emotion label corresponding to the historical customer communication corpus data for characterizing the emotion type and emotion intensity, the emotion type includes at least one of the following: anger, sadness, joy, neutrality, and the emotion intensity includes: mild, moderate, and strong; Determining the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity includes: obtaining relevant information of the customer, wherein the relevant information includes: personal basic information, historical business handling records, and historical complaint records; determining the personal user portrait corresponding to the customer based on the relevant information; and using a complaint probability prediction model to determine the complaint probability based on the personal user portrait and the emotion type and emotion intensity, wherein the complaint probability is used to characterize the probability of the customer initiating a complaint under the emotion type and emotion intensity.

5. The artificial intelligence-based consumer protection method according to claim 1, characterized in that: When the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer, sending complaint warning information to the responsible department corresponding to the target business includes: Obtain complaint record data of the target business within a preset historical time period, wherein the complaint record data includes at least one of the following: number of complaints, type of complaint, and complaint handling result; Determine, based on the complaint record data, a complaint trend curve and complaint handling satisfaction corresponding to the target business, wherein the complaint trend curve is at least used to characterize the trend of the number of complaints received by the target business over time; According to the complaint trend curve and the complaint handling satisfaction, the probability threshold corresponding to the target business is updated, and it is determined whether there is a policy update related to the target business within a target time period, wherein the target time period is a time period from a first moment to a second moment, the second moment is the moment when the probability threshold corresponding to the target business is most recently updated, and the first moment is the moment when the probability threshold corresponding to the target business is last updated; In the case where there is a policy update related to the target business within the target time period, determining a policy impact coefficient corresponding to the target business according to the latest updated policy, and adjusting the probability threshold corresponding to the target business according to the policy impact coefficient; In the case where the complaint probability exceeds the adjusted probability threshold corresponding to the target business, the complaint warning information is generated and sent to the responsible department corresponding to the target business.

6. The artificial intelligence-based consumer protection method according to claim 1, characterized in that: The method further comprises: Upon receiving customer complaint information, determine the target business corresponding to the customer complaint information, and trace back the target business being complained about to obtain historical business handling information corresponding to the target business being complained about, wherein the customer complaint information includes at least one of the following: complaint content, complaint event, and complaint object, and the historical business handling information includes: business handling time, location, person handling the business, business process, and customer information; Determine the complaint text features corresponding to the customer complaint information and the business handling features corresponding to the historical business handling information, wherein the complaint text features are at least used to characterize the complaint keywords and sentiment tendencies of the customer, and the business handling features are at least used to characterize the processing time and results of each link in the business process, as well as the service attitude of the handling personnel; Performing attribution analysis based on the complaint text features, business handling features, and the personal user portrait of the customer to obtain analysis results corresponding to the customer complaint information, wherein the analysis results are used to characterize the cause of the complaint corresponding to the customer complaint information; Based on the analysis results, a complaint handling process is determined, and a complaint handling work order corresponding to the customer complaint information is generated, and the complaint handling work order is sent to the person in charge corresponding to the initial node in the complaint handling process.

7. The artificial intelligence-based consumer protection method according to claim 6, characterized in that: The method further comprises: According to the analysis results, statistics are collected on the causes of various types of complaints for various target businesses, and based on the statistical results, visualization charts are generated for display, wherein the causes of complaints include at least one of the following: product problems, service attitude problems, and business process problems, and the visualization charts include at least one of the following: a pie chart representing the proportion of various causes of complaints in the total number of complaints, a bar chart representing the number of different types of causes of complaints, and a heat map representing the distribution of different causes of complaints in different time periods or different regions; And / or, determine the timestamp corresponding to each process node in the complaint handling process, wherein the timestamp is used to characterize the moment when the process node is executed and the processing time of the customer complaint information at the process node in the complaint handling process; based on the timestamp, determine the customer complaint information processed by each process node in the selected time period, and obtain the process heat map corresponding to the selected time period, wherein the position of each process node in the process heat map is characterized by different thermal values ​​to characterize the amount of customer complaint information processed by the process node and / or the processing time of the customer complaint information at the process node.

8. A consumer protection device based on artificial intelligence, characterized in that: include: A data acquisition module, used to acquire multimodal customer communication corpus data, wherein the customer communication corpus data includes at least one of the following: text data, video data, and audio data collected from customers during the business handling process of various service channels; A feature extraction module is used to extract features from the customer communication corpus data to obtain target features, and determine the emotion type corresponding to the customer and the emotion intensity corresponding to the emotion type based on the target features; A probability prediction module, used to determine the complaint probability corresponding to the customer based on the customer's personal user portrait and the emotion type and emotion intensity, wherein the personal user portrait is at least used to characterize the customer's historical complaint preference; The complaint warning module is used to send complaint warning information to the responsible department corresponding to the target business when the complaint probability exceeds the probability threshold corresponding to the target business handled by the customer.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the artificial intelligence-based consumer protection method described in any one of claims 1 to 7 by running the computer program.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the artificial intelligence-based consumer protection method described in any one of claims 1 to 7 are implemented.

Images