Processing method and device for user feedback information in power supply service, equipment, storage medium and program product

Abstract

The invention relates to a processing method and device for user feedback information in power supply business, equipment, a storage medium and a program product, and relates to the technical field of computers. According to the method, the recognition and extraction accuracy of the feedback keywords in the user feedback information and the effect of analyzing and processing the user feedback information can be improved. The method comprises the steps of obtaining user feedback information of multiple preset channels, and judging whether the user feedback information is user feedback information related to a current transformer area or not; judging whether the home address for generating the user feedback information belongs to the area to which the current transformer area belongs or not; identifying feedback keywords in the user feedback information, and judging whether the feedback keywords belong to existing keywords in a user feedback information base or not; and when the feedback keywords do not belong to the existing keywords in the user feedback information base, performing sentiment classification on the feedback keywords to obtain target feedback keywords of which the sentiment types are target sentiment types, and obtaining corresponding user feedback processing indication information according to the target feedback keywords.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing user feedback information in a power supply service. Background Art

[0002] With the development of computer technology, the data processing capabilities of computers have been improved. Subsequently, the technology of using computers to process user feedback information has emerged in the power supply business. This technology is widely used in power grids and other power-related enterprises.

[0003] Customer service work order information may come in various formats, power outage information comes from complex sources, and user feedback information in the power supply business may come from various social platforms. It includes multiple keywords such as topics, objects, and emotions. However, traditional Chinese word segmentation methods may not be able to accurately identify and extract them, resulting in poor analysis and processing of user feedback information. Summary of the Invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for processing user feedback information in power supply services in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for processing user feedback information in a power supply service, comprising:

[0006] Obtain user feedback information from multiple preset channels, and determine whether the user feedback information is user feedback information related to the current station area;

[0007] When the user feedback information is user feedback information of the current station area, determining whether the home address generating the user feedback information belongs to the area to which the current station area belongs;

[0008] When the attribution address of the user feedback information belongs to the belonging area, identifying the feedback keyword in the user feedback information, and determining whether the feedback keyword belongs to an existing keyword in the user feedback information database;

[0009] When the feedback keyword does not belong to the existing keywords in the user feedback information library, emotion classification is performed on the feedback keyword to obtain a target feedback keyword whose emotion type is the target emotion type, and corresponding user feedback processing instruction information is obtained according to the target feedback keyword.

[0010] In one embodiment, determining whether the user feedback information is user feedback information related to the current station area includes:

[0011] Obtain a preset text data corpus for the current station area; perform vector space division on the text data in the text data corpus according to the target topic to obtain a division result, and assign corresponding classification labels to the division result; based on the division result and the classification label, identify user feedback information related to the current station area in the user feedback information.

[0012] In one embodiment, identifying feedback keywords in the user feedback information includes:

[0013] The user feedback information is segmented using natural language processing technology, and the parts of speech of words in the user feedback information after the word segmentation are marked; based on the parts of speech of the words, the user feedback information after the word segmentation is represented as a word frequency vector, and the words whose word frequency vectors exceed a frequency threshold are identified as the feedback keywords.

[0014] In one embodiment, the sentiment classification of the feedback keywords includes:

[0015] The feedback keywords are input into a pre-built naive Bayes classifier to obtain the sentiment score corresponding to each feedback keyword; the feedback keywords whose sentiment score is greater than the sentiment classification threshold are determined as feedback keywords of type one emotion; the feedback keywords whose sentiment score is less than the sentiment classification threshold are determined as feedback keywords of type two emotion; the type one emotion and the type two emotion are two opposite emotion types.

[0016] In one embodiment, the method further comprises:

[0017] When the home address does not belong to the belonging area, identify the external target feedback keyword whose emotion type in the user feedback information is the target emotion type; determine whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information; when the user feedback information corresponding to the external target feedback keyword has not generated abnormal feedback information, use a support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate the abnormal feedback information.

[0018] In one embodiment, the using of a support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate the abnormal feedback information includes:

[0019] A binary classification model is constructed using the support vector machine algorithm; external target feedback keywords and sample user feedback information in a preset format are input into the binary classification model for an inner product operation to obtain a similarity value between the external target feedback keywords and the sample user feedback information; the similarity value is input into the support vector machine algorithm to obtain a prediction result.

[0020] In a second aspect, the present application further provides a device for processing user feedback information in a power supply service, comprising:

[0021] An information acquisition module is used to obtain user feedback information from multiple preset channels and determine whether the user feedback information is user feedback information related to the current station area;

[0022] An information judgment module is used to judge whether the home address generating the user feedback information belongs to the area to which the current station belongs when the user feedback information is the user feedback information of the current station area;

[0023] an attribution determination module, configured to, when the attribution address generating the user feedback information belongs to the attribution area, identify feedback keywords in the user feedback information and determine whether the feedback keywords belong to existing keywords in a user feedback information database;

[0024] The classification processing module is used to perform sentiment classification on the feedback keyword when the feedback keyword does not belong to the existing keywords in the user feedback information library, obtain a target feedback keyword with a target sentiment type, and obtain corresponding user feedback processing instruction information based on the target feedback keyword.

[0025] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0026] Obtain user feedback information from multiple preset channels, and determine whether the user feedback information is user feedback information related to the current station area; when the user feedback information is user feedback information of the current station area, determine whether the attribution address generating the user feedback information belongs to the area to which the current station area belongs; when the attribution address generating the user feedback information belongs to the area to which it belongs, identify feedback keywords in the user feedback information, and determine whether the feedback keywords belong to existing keywords in a user feedback information library; when the feedback keywords do not belong to existing keywords in the user feedback information library, perform sentiment classification on the feedback keywords to obtain target feedback keywords with a target sentiment type, and obtain corresponding user feedback processing instruction information based on the target feedback keywords.

[0027] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the following steps are implemented:

[0028] Obtain user feedback information from multiple preset channels, and determine whether the user feedback information is user feedback information related to the current station area; when the user feedback information is user feedback information of the current station area, determine whether the attribution address generating the user feedback information belongs to the area to which the current station area belongs; when the attribution address generating the user feedback information belongs to the area to which it belongs, identify feedback keywords in the user feedback information, and determine whether the feedback keywords belong to existing keywords in a user feedback information library; when the feedback keywords do not belong to existing keywords in the user feedback information library, perform sentiment classification on the feedback keywords to obtain target feedback keywords with a target sentiment type, and obtain corresponding user feedback processing instruction information based on the target feedback keywords.

[0029] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the following steps:

[0030] Obtain user feedback information from multiple preset channels, and determine whether the user feedback information is user feedback information related to the current station area; when the user feedback information is user feedback information of the current station area, determine whether the attribution address generating the user feedback information belongs to the area to which the current station area belongs; when the attribution address generating the user feedback information belongs to the area to which it belongs, identify feedback keywords in the user feedback information, and determine whether the feedback keywords belong to existing keywords in a user feedback information library; when the feedback keywords do not belong to existing keywords in the user feedback information library, perform sentiment classification on the feedback keywords to obtain target feedback keywords with a target sentiment type, and obtain corresponding user feedback processing instruction information based on the target feedback keywords.

[0031] The above-mentioned method, apparatus, computer device, computer-readable storage medium, and computer program product for processing user feedback information in power supply services collect user feedback information from multiple preset channels obtained by a server, and distinguish the target objects of the user feedback information into the current substation and the non-current substation. When the user feedback information is user feedback information of the current substation, the attribution address of the generated user feedback information is further classified according to whether it belongs to the area of ​​the current substation. Then, natural language processing technology is used to visualize, standardize, and digitize the user feedback information to accurately identify the feedback keywords therein. Secondly, sentiment analysis technology is used to perform sentiment classification on the feedback keywords to identify the target feedback keywords. Finally, a rigorous algorithm is used to generate corresponding user feedback processing instruction information based on the target feedback keywords. Compared with traditional Chinese word segmentation methods, this solution performs word segmentation on user feedback information based on a basic dictionary, then determines feedback keywords based on a preset indicator of relevance to the text topic, and analyzes the user feedback information based on the feedback keywords, so that the user feedback information is visualized in the form of events, thereby improving the accuracy of identifying and extracting feedback keywords in user feedback information and the effect of analyzing and processing user feedback information. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 This is a diagram of an application environment of a method for processing user feedback information in a power supply service in one embodiment;

[0034] Figure 2 1 is a flow chart of a method for processing user feedback information in a power supply service in one embodiment;

[0035] Figure 3 1 is a flow chart of a home address determination step in one embodiment;

[0036] Figure 4 1 is a flow chart of a method for processing user feedback information in a power supply service in a specific embodiment;

[0037] Figure 5 It is a structural block diagram of a device for processing user feedback information in a power supply service in one embodiment;

[0038] Figure 6FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0040] The method for processing user feedback information in the power supply service provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown in FIG. , the terminal communicates with the server through the network. The data storage system can store the data that the server needs to process. The data storage system can be integrated on the server or placed on the cloud or other network servers. Figure 1 In the application environment shown, the terminal can be, but is not limited to, various personal computers, laptops, smart phones, and tablet computers. The server can be implemented as an independent server or a server cluster consisting of multiple servers.

[0041] In one embodiment, Figure 2 As shown, a method for processing user feedback information in power supply business is provided, and the method is applied to Figure 1 The following steps are used as an example to illustrate the terminal in the figure:

[0042] Step S201: obtaining user feedback information from multiple preset channels, and determining whether the user feedback information is user feedback information related to the current station area.

[0043] Specifically, the terminal responds to a request for processing user feedback information in the power supply service, obtains user feedback information from multiple preset channels, and determines whether the user feedback information is user feedback information related to the current substation.

[0044] Step S202: When the user feedback information is user feedback information of the current station area, it is determined whether the home address generating the user feedback information belongs to the area to which the current station area belongs.

[0045] Specifically, when the user feedback information is user feedback information of the current station area, the terminal determines whether the home address generating the user feedback information belongs to the area to which the current station area belongs.

[0046] For example, the terminal can determine whether the home address generating the user feedback information belongs to the area of ​​the current station through the data source identifier, and identify the home address of the area of ​​the current station as address 1, and identify the home address of the area not belonging to the current station as address 0.

[0047] Step S203 : when the home address of the generated user feedback information belongs to the belonging area, identifying the feedback keyword in the user feedback information, and determining whether the feedback keyword belongs to an existing keyword in the user feedback information database.

[0048] Specifically, when the home address of the generated user feedback information belongs to the belonging area, the terminal identifies the feedback keyword in the user feedback information, and determines whether the feedback keyword belongs to an existing keyword in the user feedback information database.

[0049] For example, the terminal identifies feedback keywords in the user feedback information. These keywords may include words related to power supply services, such as the name of the power company.

[0050] Step S204: When the feedback keyword does not belong to the existing keywords in the user feedback information library, emotion classification is performed on the feedback keyword to obtain a target feedback keyword whose emotion type is the target emotion type, and corresponding user feedback processing instruction information is obtained according to the target feedback keyword.

[0051] Among them, sentiment analysis technology is a technology that automatically identifies and analyzes the emotional tendencies expressed in text, speech or images through computers.

[0052] Specifically, when the feedback keyword does not belong to the existing keywords in the user feedback information library, the terminal uses sentiment analysis technology to perform sentiment classification on the feedback keyword, obtains a target feedback keyword with the target sentiment type, and generates corresponding user feedback processing instruction information based on the target feedback keyword.

[0053] In the above-mentioned method for processing user feedback information in the power supply business, user feedback information from multiple preset channels is collected from the server, and the target objects of the user feedback information are divided into the current substation and the non-current substation. When the user feedback information is user feedback information from the current substation, the attribution address of the generated user feedback information is further classified according to whether it belongs to the area to which the current substation belongs. Then, natural language processing technology is used to visualize, standardize and digitize the user feedback information to accurately identify the feedback keywords therein. Secondly, sentiment analysis technology is used to perform sentiment classification on the feedback keywords to identify the target feedback keywords. Finally, a rigorous algorithm is used to generate corresponding user feedback processing instruction information based on the target feedback keywords. Compared with the traditional Chinese word segmentation method, this solution performs word segmentation on the user feedback information based on a basic dictionary, and then determines the feedback keywords based on the preset indicators of relevance to the text topic. The user feedback information is analyzed based on the feedback keywords, so that the user feedback information is visualized in the form of events, thereby improving the accuracy of identifying and extracting feedback keywords in the user feedback information and the effect of analyzing and processing the user feedback information.

[0054] In one embodiment, Figure 3 As shown, in the above step S201, determining whether the user feedback information is user feedback information related to the current station area specifically includes the following steps:

[0055] Step S301: Obtain a preset text data corpus for the current station area.

[0056] Step S302 : performing vector space division on the text data in the text data corpus according to the target topic to obtain a division result, and assigning a corresponding classification label to the division result.

[0057] Step S303: Based on the division result and the classification label, identify the user feedback information related to the current station area in the user feedback information.

[0058] Among them, a text data corpus refers to a collection of a large number of text documents, which can be used for tasks such as text analysis, natural language processing, and machine learning.

[0059] Specifically, the terminal obtains a preset text data corpus of the current station area; divides the text data in the text data corpus into vector space according to the target topic, and assigns corresponding classification labels to the obtained division results; then, based on the division results and classification labels, the terminal identifies user feedback information related to the current station area in the user feedback information.

[0060] In this embodiment, rigorous algorithms are used to visualize, standardize, and digitize user feedback information, thereby improving the scientificity and accuracy of classification and judgment.

[0061] In one embodiment, in the above step S203, identifying feedback keywords in the user feedback information specifically includes the following steps:

[0062] Natural language processing technology is used to segment user feedback information, and the parts of speech of words in the user feedback information after word segmentation are marked; according to the parts of speech of the words, the user feedback information after word segmentation is represented as a word frequency vector, and the words whose word frequency vector exceeds the frequency threshold are identified as feedback keywords.

[0063] Among them, natural language processing is an artificial intelligence technology that aims to realize the interaction between computers and natural language. It involves various technologies and methods, including text processing, speech recognition, sentiment analysis, information extraction and question-answering systems.

[0064] Specifically, the terminal cleans the user feedback information and performs word segmentation on the cleaned user feedback information using natural language processing technology; the parts of speech of the words in the user feedback information after word segmentation are marked; the terminal then represents the user feedback information after word segmentation as a word frequency vector based on the part of speech of the words, and uses the words whose word frequency vectors exceed the frequency threshold as feedback keywords.

[0065] In this embodiment, by utilizing natural language processing technology, the computer can quickly and accurately understand user feedback information, thereby more efficiently processing text data and improving the accuracy and efficiency of feedback keyword recognition.

[0066] In one embodiment, in step S204, sentiment classification of the feedback keywords is performed, specifically including the following steps:

[0067] Feedback keywords are input into a pre-built naive Bayes classifier to obtain the sentiment score corresponding to each feedback keyword; feedback keywords with sentiment scores greater than the sentiment classification threshold are determined as feedback keywords of Class I sentiment; feedback keywords with sentiment scores less than the sentiment classification threshold are determined as feedback keywords of Class II sentiment; Class I sentiment and Class II sentiment are two opposite types of sentiment.

[0068] Among them, the naive Bayes classifier is a probabilistic statistical classification algorithm based on Bayes' theorem and the conditional independence assumption of features. It is a simple but effective classification method and is widely used in natural language processing tasks such as text classification and sentiment analysis.

[0069] Specifically, the terminal inputs the feedback keywords into the trained naive Bayes classifier to obtain the sentiment score corresponding to each feedback keyword; the feedback keywords with sentiment scores greater than the sentiment classification threshold are determined as feedback keywords of the first category of emotion; the feedback keywords with sentiment scores less than the sentiment classification threshold are determined as feedback keywords of the second category of emotion.

[0070] In this embodiment, sentiment analysis technology is used to perform sentiment classification on feedback keywords, thereby improving the accuracy and reliability of sentiment classification, and greatly improving processing efficiency, saving time and labor costs.

[0071] In one embodiment, the method of the present application further includes the following steps:

[0072] When the home address does not belong to the belonging area, identify the external target feedback keyword whose emotion type in the user feedback information is the target emotion type; judge whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information; when the user feedback information corresponding to the external target feedback keyword has not generated abnormal feedback information, use the support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate abnormal feedback information.

[0073] Among them, support vector machine is a very commonly used machine learning algorithm, mainly used for classification and regression analysis. The core idea of ​​the algorithm is to find the maximum margin hyperplane to divide the training data set into different categories.

[0074] Specifically, when the preset channel corresponding to the user feedback information is not the internal system of the current financial institution, the terminal identifies the external target feedback keyword whose emotion type in the user feedback information is the target emotion type; determines whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information; when the user feedback information corresponding to the external target feedback keyword has not generated user abnormal feedback information, uses the support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate user abnormal feedback information.

[0075] In this embodiment, the support vector machine algorithm is used to predict whether user feedback information corresponding to external target feedback keywords will generate abnormal user feedback information, thereby improving the accuracy of the prediction result and increasing the practicality and universality of the solution.

[0076] In one embodiment, in the above embodiment, using a support vector machine algorithm to predict whether user feedback information corresponding to an external target feedback keyword will generate abnormal user feedback information specifically includes the following steps:

[0077] A binary classification model is constructed using the support vector machine algorithm; external target feedback keywords and sample user feedback information in a preset format are input into the binary classification model for an inner product operation to obtain a similarity value between the external target feedback keywords and the sample user feedback information; the similarity value is input into the support vector machine algorithm to obtain a prediction result.

[0078] Specifically, the terminal uses the support vector machine algorithm to build a binary classification model; inputs the external target feedback keywords and sample user feedback information in a preset format into the binary classification model for an inner product operation to obtain the similarity value between the external target feedback keywords and the sample user feedback information; and inputs the similarity value into the support vector machine algorithm to obtain the prediction result.

[0079] Among them, the prediction result can represent the probability value of the user feedback information corresponding to the external target feedback keyword will generate abnormal user feedback information. If the probability value exceeds the preset threshold, it is considered that the user feedback information corresponding to the external target feedback keyword will generate abnormal user feedback information; if the probability value does not exceed the preset threshold, it is considered that the user feedback information corresponding to the external target feedback keyword will not generate abnormal user feedback information.

[0080] In this embodiment, the support vector machine algorithm is used to predict whether user feedback information corresponding to external target feedback keywords will generate abnormal user feedback information, thereby improving the accuracy of the prediction result and increasing the scientific nature of the solution.

[0081] In one embodiment, Figure 4 As shown, a method for processing user feedback information in a power supply service in a specific embodiment is provided, which specifically includes the following steps:

[0082] Step S401, obtain user feedback information from multiple preset channels and a preset text data corpus of the current station; perform vector space division on the text data in the text data corpus according to the target topic to obtain the division result, and assign corresponding classification labels to the division result; based on the division result and the classification label, identify user feedback information related to the current station in the user feedback information.

[0083] Step S402: When the user feedback information is user feedback information of the current station area, it is determined whether the home address generating the user feedback information belongs to the area to which the current station area belongs.

[0084] Step S403: When the address at which the user feedback information is generated belongs to the region, the user feedback information is segmented using natural language processing technology, and the parts of speech of the words in the user feedback information after the segmentation are marked; based on the parts of speech of the words, the user feedback information after the segmentation is represented as a word frequency vector, and the words whose word frequency vectors exceed the frequency threshold are identified as feedback keywords.

[0085] Step S404 : determining whether the feedback keyword belongs to an existing keyword in the user feedback information database, inputting the feedback keyword into a pre-built naive Bayes classifier, and obtaining a sentiment score corresponding to each feedback keyword.

[0086] Step S405: Determine the feedback keywords with a sentiment score greater than the sentiment classification threshold as feedback keywords of the first category of sentiment; determine the feedback keywords with a sentiment score less than the sentiment classification threshold as feedback keywords of the second category of sentiment, and obtain the target feedback keywords whose sentiment type is the target sentiment type.

[0087] Step S406: Obtain corresponding user feedback processing instruction information according to the target feedback keyword.

[0088] Step S407: When the home address does not belong to the belonging area, identify the external target feedback keyword whose emotion type in the user feedback information is the target emotion type; and determine whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information.

[0089] Step S408: When the user feedback information corresponding to the external target feedback keyword does not generate abnormal user feedback information, a binary classification model is constructed using the support vector machine algorithm; the external target feedback keyword and the sample user feedback information in a preset format are input into the binary classification model for an inner product operation to obtain a similarity value between the external target feedback keyword and the sample user feedback information; the similarity value is input into the support vector machine algorithm to obtain a prediction result.

[0090] The beneficial effects brought about by the above embodiment are as follows:

[0091] This application collects and analyzes power outage information and user feedback based on customer service ticket information. It utilizes big data mining and analysis technology to assess the risk level and impact of power outage events. Based on different risk levels, the system can automatically generate targeted response strategies and issue timely risk warnings to ensure that appropriate actions can be taken quickly and accurately when a crisis occurs. By segmenting user feedback information based on a basic dictionary, feedback keywords are determined based on preset indicators of relevance to the text topic, and user feedback information is analyzed based on the feedback keywords, allowing user feedback information to be visualized in the form of events. This improves the accuracy of identifying and extracting feedback keywords from user feedback information, as well as the efficiency of analyzing and processing user feedback information.

[0092] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0093] Based on the same inventive concept, an embodiment of the present application further provides a device for processing user feedback information in a power supply service, which is used to implement the aforementioned method for processing user feedback information in a power supply service. The implementation solution provided by the device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for processing user feedback information in a power supply service provided below can be found in the aforementioned method for processing user feedback information in a power supply service, and will not be repeated here.

[0094] In an exemplary embodiment, Figure 5 As shown, a device for processing user feedback information in a power supply service is provided, comprising:

[0095] The information acquisition module 501 is used to obtain user feedback information from multiple preset channels and determine whether the user feedback information is user feedback information related to the current station area;

[0096] The information determination module 502 is used to determine whether the home address generating the user feedback information belongs to the area to which the current station belongs when the user feedback information is user feedback information of the current station area;

[0097] The attribution determination module 503 is configured to identify feedback keywords in the user feedback information when the attribution address of the generated user feedback information belongs to the attribution area, and determine whether the feedback keywords belong to existing keywords in the user feedback information database;

[0098] The classification processing module 504 is used to perform sentiment classification on the feedback keyword when the feedback keyword does not belong to the existing keywords in the user feedback information library, obtain the target feedback keyword with the target sentiment type, and obtain the corresponding user feedback processing instruction information based on the target feedback keyword.

[0099] In one embodiment, the information acquisition module 501 is also used to obtain a preset text data corpus for the current station; perform vector space division on the text data in the text data corpus according to the target topic to obtain the division result, and assign corresponding classification labels to the division result; based on the division result and the classification label, identify user feedback information related to the current station in the user feedback information.

[0100] In one embodiment, the attribution judgment module 503 is further used to perform word segmentation processing on the user feedback information using natural language processing technology, and mark the parts of speech of the words in the user feedback information after word segmentation processing; based on the parts of speech of the words, the user feedback information after word segmentation processing is represented as a word frequency vector, and the words whose word frequency vector exceeds the frequency threshold are identified as feedback keywords.

[0101] In one embodiment, the classification processing module 504 is also used to input the feedback keywords into a pre-built naive Bayes classifier to obtain the sentiment score corresponding to each feedback keyword; feedback keywords with a sentiment score greater than the sentiment classification threshold are determined as feedback keywords of type one emotion; feedback keywords with a sentiment score less than the sentiment classification threshold are determined as feedback keywords of type two emotion; type one emotion and type two emotion are two opposite emotion types.

[0102] In one embodiment, the device for processing user feedback information in the power supply business also includes an information prediction module, which is used to identify the external target feedback keyword whose emotion type in the user feedback information is the target emotion type when the home address does not belong to the belonging area; determine whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information; when the user feedback information corresponding to the external target feedback keyword has not generated abnormal feedback information, use a support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate abnormal feedback information.

[0103] In one embodiment, the information prediction module is further used to construct a binary classification model using a support vector machine algorithm; input the external target feedback keywords and sample user feedback information in a preset format into the binary classification model for an inner product operation to obtain a similarity value between the external target feedback keywords and the sample user feedback information; and input the similarity value into the support vector machine algorithm to obtain a prediction result.

[0104] Each module in the aforementioned device for processing user feedback information in the power supply service may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0105] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 6As shown. The computer device includes a processor, memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals via wired or wireless means, and the wireless means can be implemented via Wi-Fi, mobile cellular networks, near-field communication (NFC), or other technologies. When executed by the processor, the computer program implements a method for processing user feedback information in a power supply service. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0106] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0107] In one embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0108] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0109] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0110] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0111] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0112] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0113] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for processing user feedback information in a power supply service, characterized in that: The method comprises: Obtain user feedback information from multiple preset channels, and determine whether the user feedback information is user feedback information related to the current station area; When the user feedback information is user feedback information of the current station area, determining whether the home address generating the user feedback information belongs to the area to which the current station area belongs; When the attribution address of the user feedback information belongs to the belonging area, identifying the feedback keyword in the user feedback information, and determining whether the feedback keyword belongs to an existing keyword in the user feedback information database; When the feedback keyword does not belong to the existing keywords in the user feedback information library, emotion classification is performed on the feedback keyword to obtain a target feedback keyword whose emotion type is the target emotion type, and corresponding user feedback processing instruction information is obtained according to the target feedback keyword.

2. The method according to claim 1, characterized in that The determining whether the user feedback information is user feedback information related to the current station area includes: Acquiring a preset text data corpus of the current station area; Performing vector space division on the text data in the text data corpus according to the target topic to obtain a division result, and assigning corresponding classification labels to the division result; Based on the division result and the classification label, the user feedback information related to the current station area is identified in the user feedback information.

3. The method according to claim 2, characterized in that The identifying feedback keywords in the user feedback information includes: Using natural language processing technology to perform word segmentation processing on the user feedback information, and marking the part of speech of the words in the user feedback information after word segmentation processing; The user feedback information after word segmentation is represented as a word frequency vector according to the part of speech of the word, and the words whose word frequency vector exceeds a frequency threshold are identified as the feedback keywords.

4. The method according to claim 1, wherein The sentiment classification of the feedback keywords includes: Input the feedback keywords into a pre-built Naive Bayes classifier to obtain the sentiment score corresponding to each feedback keyword; Determine the feedback keywords whose sentiment scores are greater than the sentiment classification threshold as feedback keywords of a class of sentiment; The feedback keywords whose emotion scores are less than the emotion classification threshold are determined as feedback keywords of the second category of emotion; the first category of emotion and the second category of emotion are two opposite emotion types.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: When the home address does not belong to the belonging area, identifying that the emotion type present in the user feedback information is an external target feedback keyword of the target emotion type; Determining whether the user feedback information corresponding to the external target feedback keyword has generated user abnormal feedback information; When the user feedback information corresponding to the external target feedback keyword does not generate abnormal feedback information, a support vector machine algorithm is used to predict whether the user feedback information corresponding to the external target feedback keyword will generate the abnormal feedback information.

6. The method according to claim 5, characterized in that The using of a support vector machine algorithm to predict whether the user feedback information corresponding to the external target feedback keyword will generate the abnormal feedback information includes: Using the support vector machine algorithm, a binary classification model is constructed; Inputting the external target feedback keywords and sample user feedback information in a preset format into the binary classification model and performing an inner product operation to obtain a similarity value between the external target feedback keywords and the sample user feedback information; The similarity value is input into the support vector machine algorithm to obtain a prediction result.

7. A device for processing user feedback information in a power supply service, characterized in that: The device comprises: An information acquisition module is used to obtain user feedback information from multiple preset channels and determine whether the user feedback information is user feedback information related to the current station area; An information judgment module is used to judge whether the home address generating the user feedback information belongs to the area to which the current station belongs when the user feedback information is the user feedback information of the current station area; an attribution determination module, configured to, when the attribution address generating the user feedback information belongs to the attribution area, identify feedback keywords in the user feedback information and determine whether the feedback keywords belong to existing keywords in a user feedback information database; The classification processing module is used to perform sentiment classification on the feedback keyword when the feedback keyword does not belong to the existing keywords in the user feedback information library, obtain a target feedback keyword with a target sentiment type, and obtain corresponding user feedback processing instruction information based on the target feedback keyword.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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 method according to any one of claims 1 to 6 are implemented.

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