CN113704407B - Complaint volume analysis method, device, equipment and storage medium based on category analysis - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a complaint volume analysis method based on category analysis, which comprises the following steps: calculating complaint amounts in a plurality of preset time intervals from complaint amount data of preset business, respectively calculating correlation coefficients between a plurality of operation indexes and the complaint amounts, collecting the operation indexes with the correlation coefficients larger than a preset threshold as characteristic indexes, generating a text matrix of complaint text when each complaint is generated, determining the complaint category of each complaint according to the text matrix, calculating the duty weight of each complaint category, analyzing to obtain the complaint amount of the future preset time period, and calculating the complaint amounts of different complaint categories in the future preset time period according to the duty weight and the complaint amount of the future preset time period. In addition, the invention also relates to a blockchain technology, and complaint volume data can be stored in nodes of the blockchain. The invention also provides a complaint volume analysis device, equipment and medium based on category analysis. The invention can improve the accuracy of complaint quantity analysis.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a complaint volume analysis method and device based on category analysis, electronic equipment and a computer readable storage medium.
Background
Along with the increasing strong competition of duration, more and more companies and enterprises begin to pay attention to the service level of users, and complaint quantity becomes an important index for measuring the speed of a service, and when the complaint quantity of a certain service is too high, the service has a great number of defects, so that the user experience is poor. Therefore, reasonable analysis and prediction are carried out on the complaint quantity, and the method is beneficial to helping enterprises to realize business improvement.
Most of the existing complaint quantity prediction methods are based on regression analysis of historical complaint quantities, namely, the complaint quantity in a future time period is predicted according to the complaint quantity in a certain time period in the history. However, in the method, other factors related to the service are ignored, and the ratio of various complaints among a large number of user complaints cannot be known, so that the accuracy of the complaint quantity obtained by carrying out regression analysis only by using the historical complaint quantity is lower.
Disclosure of Invention
The invention provides a complaint volume analysis method and device based on category analysis and a computer readable storage medium, and mainly aims to solve the problem of lower accuracy in complaint volume analysis.
In order to achieve the above object, the present invention provides a complaint volume analysis method based on category analysis, comprising:
acquiring complaint volume data of a preset service, and counting the complaint volume of each time interval in a plurality of preset time intervals according to the complaint volume data;
acquiring a plurality of operation indexes of the preset service in each time interval, respectively calculating a correlation coefficient between each operation index and the complaint volume, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
and analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
Optionally, the calculating the complaint amount of each time interval in a plurality of preset time intervals according to the complaint amount data includes:
extracting a time data expression format in the complaint volume data;
compiling preset characters into a rule expression according to the time data expression format;
extracting complaint time when each complaint is taken out from the complaint amount data by using the rule expression;
classifying the complaint quantity data into a plurality of preset time intervals according to the complaint time;
counting the number of complaint volume data in each time interval to obtain the complaint volume of each time interval.
Optionally, the calculating the correlation coefficient between each operation index and the complaint volume includes:
respectively converting the operation indexes of the preset service in each time interval into index vectors;
selecting one of the time intervals one by one from the plurality of preset time intervals, selecting one of the time intervals one by one from index vectors of the selected time interval as a target vector, and calculating the association degree between the target vector and complaint volume of the selected time interval;
And summing the association degree between the target vector and the complaint volume of each time interval to obtain the correlation coefficient between the operation index corresponding to the target vector and the complaint volume.
Optionally, the calculating the association degree between the target vector and the complaint volume of the selected time interval includes:
calculating the association degree between the target vector and the complaint volume of the selected time interval by using the following association degree algorithm:
wherein P is x,y For the degree of association between the target vector and the complaint volume of the selected time interval, x is the modular length of the target vector,for the mean value of the modular length of all index vectors, y is the complaint quantity in the selected time interval, +.>Is the average value of complaint amounts in all time intervals.
Optionally, the performing numerical matrix conversion on the complaint text to obtain a text matrix of each complaint text includes:
selecting one text from the complaint texts one by one as a target text;
word segmentation processing is carried out on the target text to obtain text word segmentation;
counting the position information of each text word in the target text, and carrying out vector coding on the position information to obtain a position vector;
Converting each text word into a word vector, and splicing the position code corresponding to each text word and the word vector of the text word into a code vector;
and splicing the coded vectors of all text segmentation as row vectors to obtain a text matrix of the target text.
Optionally, the splicing the encoding vectors of all text segmentation as row vectors to obtain the text matrix of the target text includes:
counting the vector length of each vector in the coding vectors, and determining the maximum vector length as a target length;
extending the vector length of each vector in the coded vectors to the target length by using preset parameters;
and selecting the prolonged coding vectors one by one as row vectors to splice, so as to obtain the text matrix of the target text.
Optionally, the analyzing the complaint amount according to the complaint amount of each time interval and the characteristic index by using a pre-constructed time sequence prediction model to obtain the complaint amount of the future preset time period includes:
collecting complaint quantity and the characteristic index of each time interval as characteristic data;
mapping the characteristic data to a preset characteristic space by using a pre-constructed time sequence detection model, and counting the space coordinates of each characteristic data in the preset characteristic space;
Performing curve fitting on the space coordinates to obtain a fitting function;
substituting a future preset time period as a parameter into the fitting function to obtain the complaint quantity of the future preset time period.
In order to solve the above problems, the present invention also provides a complaint volume analysis device based on category analysis, the device comprising:
the complaint quantity statistics module is used for acquiring complaint quantity data of a preset service and counting complaint quantity of each time interval in a plurality of preset time intervals according to the complaint quantity data;
the index calculation module is used for acquiring a plurality of operation indexes of the preset service in each time interval, calculating the correlation coefficient between each operation index and the complaint volume respectively, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
the text matrix generation module is used for acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
the weight calculation module is used for calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
The complaint quantity prediction module is used for analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the above-described class analysis-based complaint volume analysis method.
In order to solve the above-mentioned problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the above-mentioned class analysis-based complaint volume analysis method.
The embodiment of the invention can screen the operation index related to the complaint amount in the business, and simultaneously predict the complaint amount in the future preset time period based on the complaint amount in the history preset time period and the screened operation index, thereby considering the numerical value of the complaint amount and the factors of the operation index related to the business; according to the duty ratio of each type of complaint in the history, the numerical value of different types of complaint in a preset time period in the future is predicted, the prediction of different types of complaint is realized, and the accuracy of the prediction of the complaint is improved. Therefore, the complaint volume analysis method, the complaint volume analysis device, the electronic equipment and the computer readable storage medium based on the category analysis can solve the problem of lower accuracy in product recommendation.
Drawings
FIG. 1 is a flow chart of a method for analyzing complaint volume based on category analysis according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for calculating correlation coefficients according to an embodiment of the present invention;
FIG. 3 is a flow chart of generating a text matrix according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a complaint volume analysis device based on category analysis according to an embodiment of the present invention;
Fig. 5 is a schematic structural diagram of an electronic device for implementing the complaint volume analysis method based on category analysis according to an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a complaint volume analysis method based on category analysis. The execution subject of the complaint volume analysis method based on category analysis includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the complaint volume analysis method based on category analysis may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a complaint volume analysis method based on category analysis according to an embodiment of the invention is shown. In this embodiment, the complaint volume analysis method based on category analysis includes:
s1, acquiring complaint quantity data of a preset service, and counting the complaint quantity of each time interval in a plurality of preset time intervals according to the complaint quantity data.
In the embodiment of the present invention, the preset service may be any product, service, benefit, etc., and the complaint volume data includes data such as time, frequency, etc. of complaints performed by at least one user on the preset service.
In detail, the remaining stored complaint volume data may be crawled from a pre-built storage area including, but not limited to, a database, blockchain node, network cache, using computer sentences having data crawling functionality (e.g., java sentences, python sentences, etc.).
In the embodiment of the invention, the complaint volume data can be analyzed to obtain the complaint volume of each time interval in a plurality of preset time intervals.
In detail, a rule expression may be constructed, and the time of each complaint is extracted from the complaint amount data by using the rule expression, so as to determine the complaint amount of each time interval in the plurality of preset time intervals according to the complaint time.
In particular, the rule expression may be used to extract fields in the data that have a particular format.
In one embodiment of the present invention, the calculating the complaint amount of each time interval in a plurality of preset time intervals according to the complaint amount data includes:
extracting a time data expression format in the complaint volume data;
compiling preset characters into a rule expression according to the time data expression format;
extracting complaint time when each complaint is taken out from the complaint amount data by using the rule expression;
classifying the complaint quantity data into a plurality of preset time intervals according to the complaint time;
counting the number of complaint volume data in each time interval to obtain the complaint volume of each time interval.
In detail, the time data expression format refers to a data format of data for expressing time among the complaint volume data, for example, time expressed in the form of "xx/xx/xx", or time expressed in the form of "xx year xx month xx day", or the like.
Specifically, a preset compiler may be utilized to compile preset characters into a rule expression according to the actual data expression format, and then a complaint time when each complaint is extracted from the data volume data by utilizing the rule expression obtained by compiling, where the compiler includes but is not limited to: visual Studio compiler, devc++ compiler.
In the embodiment of the invention, after the complaint time of each complaint is extracted, the complaint amount data can be classified into a plurality of preset time intervals according to the complaint time, and the number of the complaint amount data in each time interval is counted to obtain the complaint amount of each time interval.
S2, acquiring a plurality of operation indexes of the preset service in each time interval, respectively calculating the correlation coefficient between each operation index and the complaint volume, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes.
In the embodiment of the invention, the operation index refers to the indexes such as the sum of money, the number of users and the like in the operation process of the preset business.
In detail, the step of obtaining the multiple operation indexes of the preset service in each time interval is consistent with the step of obtaining complaint volume data of the preset service in S1, and will not be described herein.
In the embodiment of the invention, because the number of the operation indexes may be very large, but not every operation index has a relatively obvious relation with the complaint quantity, in order to improve the efficiency and accuracy of the subsequent analysis of the complaint quantity, the correlation coefficient between every operation index and the complaint quantity can be calculated respectively, so that the operation indexes which are relatively obviously related with the complaint quantity can be screened out according to the correlation coefficient.
In the embodiment of the present invention, referring to fig. 2, the calculating the correlation coefficient between each operation index and the complaint volume includes:
s21, respectively converting the operation indexes of the preset service in each time interval into index vectors;
s22, selecting one of the time intervals one by one from the plurality of preset time intervals, selecting one of the time intervals one by one from index vectors of the selected time interval as a target vector, and calculating the association degree between the target vector and complaint volume of the selected time interval;
s23, summing the association degree between the target vector and the complaint volume of each time interval to obtain the correlation coefficient between the operation index corresponding to the target vector and the complaint volume.
In detail, a plurality of business indexes of the preset business in each time interval can be respectively converted into index vectors by using a preset artificial intelligence model with a vector conversion function, wherein the artificial intelligence model comprises, but is not limited to, an NLP (Natural Language Processing ) model, an HMM (Hidden Markov Model, hidden Markov model).
Specifically, the calculating the association degree between the target vector and the complaint volume of the selected time interval includes:
Calculating the association degree between the target vector and the complaint volume of the selected time interval by using the following association degree algorithm:
wherein P is x,y For the degree of association between the target vector and the complaint volume of the selected time interval, x is the modular length of the target vector,for the mean value of the modular length of all index vectors, y is the complaint quantity in the selected time interval, +.>Is the average value of complaint amounts in all time intervals.
In the embodiment of the invention, because a plurality of preset time intervals exist, but the correlation between the operation index corresponding to the target vector and the complaint volume in the time interval where the target vector is located is only calculated, the correlation between the operation index corresponding to the target vector and the complaint volume in each time interval can be summed to obtain the correlation coefficient between the operation index corresponding to the target vector and the overall complaint volume, and then the operation index with the correlation coefficient larger than a preset threshold value is selected as the characteristic index.
S3, acquiring complaint texts of the preset business when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text.
In the embodiment of the invention, the complaint text is a text of complaint opinion generated by the user aiming at the preset service when the preset service is complained by the user.
In detail, the step of obtaining the complaint text of the preset service when each complaint is identical to the step of obtaining the complaint amount data of the preset service in S1, and will not be described in detail herein.
In the embodiment of the invention, the complaint text is in a text form, so that in order to reduce the occupation of calculation resources in analysis of the complaint text, the numerical matrix conversion can be performed on the complaint text to obtain a text matrix corresponding to the complaint text.
In the embodiment of the present invention, referring to fig. 3, the performing numerical matrix conversion on the complaint text to obtain a text matrix of each complaint text includes:
s31, selecting one text from the complaint texts one by one as a target text;
s32, word segmentation processing is carried out on the target text, and text word segmentation is obtained;
s33, counting the position information of each text word in the target text, and carrying out vector coding on the position information to obtain a position vector;
s34, converting each text word into a word vector, and splicing the position code corresponding to each text word and the word vector of the text word into a code vector;
And S35, splicing the coded vectors of all text segmentation as row vectors to obtain a text matrix of the target text.
In detail, the target text can be subjected to word segmentation processing by using an artificial intelligent model with word segmentation processing functions such as a preset word2vec model and a preset bert model, so that text word segmentation is obtained.
In the embodiment of the present invention, the steps of vector encoding the position information and converting each text word into a word vector are identical to the steps of converting the plurality of operation indexes of the preset service in each time interval into the index vectors in S2, and are not described herein.
In the embodiment of the invention, the position information of the text word segmentation is encoded, and the position vector obtained by encoding the position information is added into the word vector of the text word segmentation, so that the position information of the word can be embedded into the word vector, and the accuracy of the finally generated text matrix is improved.
In the embodiment of the present invention, the splicing the encoding vectors of all text segmentation as row vectors to obtain the text matrix of the target text includes:
counting the vector length of each vector in the coding vectors, and determining the maximum vector length as a target length;
Extending the vector length of each vector in the coded vectors to the target length by using preset parameters;
and selecting the prolonged coding vectors one by one as row vectors to splice, so as to obtain the text matrix of the target text.
In detail, since the encoding vectors are obtained by splicing word vectors of different text words and position line vectors of each text word, the vector lengths of different encoding vectors may be different, which is unfavorable for subsequent splicing into a text matrix, and further, the vector length of each vector in the encoding vectors can be prolonged to a uniform length by using a preset parameter.
For example, there is a coding vector a: (10, 33), encoding vector B: (22,35,55) and encoding vector C: (1,42,5,76) the statistics shows that the vector length of the encoded vector C is the largest (4), and when the preset parameter is 0, the vector lengths of the encoded vector a and the encoded vector B can be extended to 4 by using the preset parameter, so as to obtain the extended encoded vector a: (10,33,0,0), and an extended encoding vector B: (22,35,55,0) selecting the prolonged coding vectors one by one as row vectors to splice to obtain the following text matrix:
s4, respectively calculating the relative probability between each text matrix and a preset complaint category, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category
In the embodiment of the present invention, the relative probability refers to a probability value of which type of complaint category the complaint text corresponding to each text matrix belongs to, for example, when the relative probability value between the text matrix and the preset complaint category a is 80, it is indicated that the probability value of the complaint text corresponding to the text matrix belonging to the preset complaint category a is eighty percent.
In the embodiment of the present invention, the calculating the relative probability between each text matrix and the preset complaint category includes:
calculating the relative probability between each text matrix and the preset complaint category by using the following relative probability algorithm:
wherein F is the relative probability, a is the text matrix, b k And presetting a complaint category for the kth.
In detail, the complaint text corresponding to each text matrix can be classified into a preset complaint category with the maximum relative probability with the text matrix according to the relative probability.
For example, if there are a text matrix a and a text matrix B, the preset complaint category includes a category x and a category y, where the relative probability between the text matrix a and the category x is 80, the relative probability between the text matrix a and the category y is 30, the relative probability between the text matrix B and the category x is 50, and the relative probability between the text matrix B and the category y is 90, it may be determined that the complaint text corresponding to the text matrix a belongs to the category x, and the complaint text corresponding to the text matrix B belongs to the text matrix y.
In the embodiment of the invention, after the complaint category of the complaint text corresponding to each text matrix is determined, the number of the complaint text in each preset complaint category can be counted, and the duty ratio weight of each complaint category in the complaint is calculated.
In detail, the calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category includes:
calculating the duty ratio weight of each complaint category in the complaint text by using the following weight algorithm:
wherein W is j Duty weight for j-th complaint category, M j The number of complaint texts in the j-th complaint category is given, and N is the total number of all complaint texts.
S5, analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
In the embodiment of the invention, the time sequence prediction model is an artificial intelligent model with numerical regression analysis, and the time sequence prediction model comprises, but is not limited to, a LightGBM model and an LSTM (Long short-term memory) model.
In the embodiment of the present invention, the analyzing the complaint amount according to the complaint amount of each time interval and the characteristic index by using a pre-constructed time sequence prediction model to obtain the complaint amount of the future preset time period includes:
collecting complaint quantity and the characteristic index of each time interval as characteristic data;
mapping the characteristic data to a preset characteristic space by using a pre-constructed time sequence detection model, and counting the space coordinates of each characteristic data in the preset characteristic space;
performing curve fitting on the space coordinates to obtain a fitting function;
substituting a future preset time period as a parameter into the fitting function to obtain the complaint quantity of the future preset time period.
In detail, the time sequence detection model may map the feature data to a preset feature space by using a preset mapping function, so as to determine the space coordinates of each feature data in the preset feature space, wherein the mapping function includes, but is not limited to, a gaussian function and a map function.
Specifically, the smooth curve may be used to connect the spatial coordinates of each feature data in the preset feature space, so as to obtain a fitting function of the spatial coordinates.
In the embodiment of the invention, after the fitting function is obtained, the future preset time period can be substituted into the fitting function as a parameter to be solved, so as to obtain the complaint quantity of the future preset time period.
Further, a corresponding complaint amount for each of the preset complaint types may be calculated according to the duty weight.
For example, the duty cycle weights include: the duty ratio weight of the preset complaint type A is 0.4, the duty ratio weight of the preset complaint type B is 0.6, and when the complaint amount in the future preset time period is calculated to be 100, 40 complaint amounts of the preset complaint type A and 60 complaint amounts of the preset complaint type B can be obtained according to the duty ratio weight.
The embodiment of the invention can screen the operation index related to the complaint amount in the business, and simultaneously predict the complaint amount in the future preset time period based on the complaint amount in the history preset time period and the screened operation index, thereby considering the numerical value of the complaint amount and the factors of the operation index related to the business; according to the duty ratio of each type of complaint in the history, the numerical value of different types of complaint in a preset time period in the future is predicted, the prediction of different types of complaint is realized, and the accuracy of the prediction of the complaint is improved. Therefore, the complaint volume analysis method based on category analysis can solve the problem of lower accuracy in product recommendation.
Fig. 4 is a functional block diagram of a complaint volume analysis device based on category analysis according to an embodiment of the present invention.
The complaint volume analysis device 100 based on category analysis according to the present invention may be mounted in an electronic apparatus. Depending on the functions implemented, the complaint volume analysis device 100 based on category analysis may include a complaint volume statistics module 101, an index calculation module 102, a text matrix generation module 103, a weight calculation module 104, and a complaint volume prediction module 105. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the complaint volume statistics module 101 is configured to obtain complaint volume data of a preset service, and count complaint volumes of each time interval in a plurality of preset time intervals according to the complaint volume data;
the index calculation module 102 is configured to obtain a plurality of operation indexes of the preset service in each time interval, calculate correlation coefficients between each operation index and the complaint volume, and aggregate operation indexes with the correlation coefficients greater than a preset threshold as feature indexes;
The text matrix generation module 103 is configured to obtain complaint texts of the preset service when each time is complained, and perform numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
the weight calculation module 104 is configured to calculate a relative probability between each text matrix and a preset complaint category, determine a complaint category of a complaint text corresponding to each text matrix according to the relative probability, and calculate a duty weight of each complaint category in the complaint text according to the complaint category;
the complaint amount prediction module 105 is configured to analyze the complaint amount according to the complaint amount of each time interval and the characteristic index by using a pre-constructed time sequence prediction model, obtain a complaint amount of a future preset time period, and calculate a complaint amount of each complaint category in the future preset time period according to the duty weight and the complaint amount of the future preset time period.
In detail, each module in the complaint volume analysis device 100 based on category analysis in the embodiment of the present invention adopts the same technical means as the complaint volume analysis method based on category analysis described in fig. 1 to 3, and can generate the same technical effects, which is not described here again.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a complaint volume analysis method based on category analysis according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a complaint volume analysis program based on category analysis.
The processor 10 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 11 (for example, executes a complaint volume analysis program based on category analysis, etc.), and invokes data stored in the memory 11 to perform various functions of the electronic device and process data.
The memory 11 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of a complaint volume analysis program based on category analysis, but also for temporarily storing data that has been output or is to be output.
The communication bus 12 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
The communication interface 13 is used for communication between the electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The complaint volume analysis program based on category analysis stored in the memory 11 in the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can realize:
acquiring complaint volume data of a preset service, and counting the complaint volume of each time interval in a plurality of preset time intervals according to the complaint volume data;
acquiring a plurality of operation indexes of the preset service in each time interval, respectively calculating a correlation coefficient between each operation index and the complaint volume, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
Acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
and analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
In particular, the specific implementation method of the above instructions by the processor 10 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring complaint volume data of a preset service, and counting the complaint volume of each time interval in a plurality of preset time intervals according to the complaint volume data;
acquiring a plurality of operation indexes of the preset service in each time interval, respectively calculating a correlation coefficient between each operation index and the complaint volume, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
and analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A method of analyzing a complaint volume based on category analysis, the method comprising:
acquiring complaint volume data of a preset service, and counting the complaint volume of each time interval in a plurality of preset time intervals according to the complaint volume data;
acquiring a plurality of operation indexes of the preset service in each time interval, respectively calculating a correlation coefficient between each operation index and the complaint volume, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
Acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future;
wherein the calculating the correlation coefficient between each operation index and the complaint volume includes: respectively converting the operation indexes of the preset service in each time interval into index vectors; selecting one of the time intervals one by one from the plurality of preset time intervals, selecting one of the time intervals one by one from index vectors of the selected time interval as a target vector, and calculating the association degree between the target vector and complaint volume of the selected time interval; summing the association degree between the target vector and the complaint volume of each time interval to obtain a correlation coefficient between the operation index corresponding to the target vector and the complaint volume;
The calculating the association degree between the target vector and the complaint volume of the selected time interval comprises the following steps: calculating the association degree between the target vector and the complaint volume of the selected time interval by using the following association degree algorithm:
wherein P is x,y For the degree of association between the target vector and the complaint volume of the selected time interval, x is the modular length of the target vector,for the mean value of the modular length of all index vectors, y is the complaint quantity in the selected time interval, +.>The average value of complaint amounts in all time intervals is obtained;
the analyzing of the complaint amount is performed according to the complaint amount of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint amount of a future preset time period, and the method comprises the following steps: collecting complaint quantity and the characteristic index of each time interval as characteristic data; mapping the characteristic data to a preset characteristic space by using a pre-constructed time sequence detection model, and counting the space coordinates of each characteristic data in the preset characteristic space; performing curve fitting on the space coordinates to obtain a fitting function; substituting a future preset time period as a parameter into the fitting function to obtain the complaint quantity of the future preset time period.
2. The method for analyzing complaint volume based on category analysis according to claim 1, wherein the counting complaint volume for each of a plurality of preset time intervals based on the complaint volume data includes:
extracting a time data expression format in the complaint volume data;
compiling preset characters into a rule expression according to the time data expression format;
extracting complaint time when each complaint is taken out from the complaint amount data by using the rule expression;
classifying the complaint quantity data into a plurality of preset time intervals according to the complaint time;
counting the number of complaint volume data in each time interval to obtain the complaint volume of each time interval.
3. The method for analyzing the complaint volume based on the category analysis according to claim 1, wherein the step of performing the numerical matrix transformation on the complaint texts to obtain a text matrix of each complaint text comprises the steps of:
selecting one text from the complaint texts one by one as a target text;
word segmentation processing is carried out on the target text to obtain text word segmentation;
counting the position information of each text word in the target text, and carrying out vector coding on the position information to obtain a position vector;
Converting each text word into a word vector, and splicing the position code corresponding to each text word and the word vector of the text word into a code vector;
and splicing the coded vectors of all text segmentation as row vectors to obtain a text matrix of the target text.
4. The method for analyzing complaint volume based on category analysis according to claim 3, wherein the step of concatenating the encoded vectors of all text segmentation words as row vectors to obtain the text matrix of the target text includes:
counting the vector length of each vector in the coding vectors, and determining the maximum vector length as a target length;
extending the vector length of each vector in the coded vectors to the target length by using preset parameters;
and selecting the prolonged coding vectors one by one as row vectors to splice, so as to obtain the text matrix of the target text.
5. A class analysis-based complaint volume analysis apparatus for implementing the class analysis-based complaint volume analysis method as claimed in any one of claims 1 to 4, characterized in that the apparatus includes:
the complaint quantity statistics module is used for acquiring complaint quantity data of a preset service and counting complaint quantity of each time interval in a plurality of preset time intervals according to the complaint quantity data;
The index calculation module is used for acquiring a plurality of operation indexes of the preset service in each time interval, calculating the correlation coefficient between each operation index and the complaint volume respectively, and collecting the operation indexes with the correlation coefficient larger than a preset threshold as characteristic indexes;
the text matrix generation module is used for acquiring complaint texts of the preset service when each time is complaint, and performing numerical matrix conversion on the complaint texts to obtain a text matrix of each complaint text;
the weight calculation module is used for calculating the relative probability between each text matrix and a preset complaint category respectively, determining the complaint category of the complaint text corresponding to each text matrix according to the relative probability, and calculating the duty ratio weight of each complaint category in the complaint text according to the complaint category;
the complaint quantity prediction module is used for analyzing the complaint quantity according to the complaint quantity of each time interval and the characteristic index by utilizing a pre-constructed time sequence prediction model to obtain the complaint quantity of a preset time period in the future, and calculating the complaint quantity of each complaint category in the preset time period in the future according to the duty ratio weight and the complaint quantity of the preset time period in the future.
6. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the class analysis-based complaint volume analysis method as claimed in any one of claims 1 to 4.
7. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the class analysis-based complaint volume analysis method as claimed in any one of claims 1 to 4.
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